diff --git a/examples/pcsaft/PhaseDiagramBinary.ipynb b/examples/pcsaft/PhaseDiagramBinary.ipynb
index e490edae9..73d17edab 100644
--- a/examples/pcsaft/PhaseDiagramBinary.ipynb
+++ b/examples/pcsaft/PhaseDiagramBinary.ipynb
@@ -247,7 +247,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1440x360 with 3 Axes>"
       ]
@@ -262,8 +262,8 @@
     "params = PcSaftParameters.from_multiple_json([(['acetone'], '../../parameters/pcsaft/gross2006.json'), (['water'], '../../parameters/pcsaft/gross2002.json')])\n",
     "\n",
     "saft = EquationOfState.pcsaft(params)\n",
-    "dia_p = PhaseDiagram.binary_vlle(saft, 450*KELVIN, (0.005, 0.9), 50*BAR, 101)\n",
-    "dia_t = PhaseDiagram.binary_vlle(saft, BAR, (0.001, 0.99), 273.15*KELVIN, 101)\n",
+    "dia_p = PhaseDiagram.binary_vlle(saft, 450*KELVIN, (0.005, 0.9), 50*BAR, 25*BAR, 101)\n",
+    "dia_t = PhaseDiagram.binary_vlle(saft, BAR, (0.001, 0.99), 0*CELSIUS, 50*CELSIUS, 101)\n",
     "\n",
     "f, ax = plt.subplots(1,3,figsize=(20,5))\n",
     "ax[0].plot(dia_p.vle.liquid.molefracs[:,0], dia_p.vle.liquid.pressure/BAR, '-k')\n",
@@ -305,7 +305,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1440x360 with 3 Axes>"
       ]
@@ -321,8 +321,8 @@
     "\n",
     "\n",
     "saft = EquationOfState.pcsaft(params)\n",
-    "dia_p = PhaseDiagram.binary_vlle(saft, 350*KELVIN, (0.55, 0.98), BAR, 101)\n",
-    "dia_t = PhaseDiagram.binary_vlle(saft, BAR, (0.5,0.995), 343.15*KELVIN, 101)\n",
+    "dia_p = PhaseDiagram.binary_vlle(saft, 350*KELVIN, (0.55, 0.98), BAR, 0.5*BAR, 101)\n",
+    "dia_t = PhaseDiagram.binary_vlle(saft, BAR, (0.5,0.995), 70*CELSIUS, 80*CELSIUS, 101)\n",
     "\n",
     "f, ax = plt.subplots(1,3,figsize=(20,5))\n",
     "ax[0].plot(dia_p.vle.liquid.molefracs[:,0], dia_p.vle.liquid.pressure/BAR, '-k')\n",
diff --git a/feos-core/CHANGELOG.md b/feos-core/CHANGELOG.md
index f84d3dab4..4bcf2ec2a 100644
--- a/feos-core/CHANGELOG.md
+++ b/feos-core/CHANGELOG.md
@@ -5,9 +5,17 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 ## Unreleased
+### Added
+- Added `State::spinodal` that calculates both spinodal points for a given temperature and composition using the same objective function that is also used in the critical point calculation. [#23](https://github.com/feos-org/feos/pull/23)
+- Added `PhaseDiagram::bubble_point_line`, `PhaseDiagram::dew_point_line`, and `PhaseDiagram::spinodal` to calculate phase envelopes for mixtures with fixed compositions. [#23](https://github.com/feos-org/feos/pull/23)
+
 ### Changed
 - Made binary parameters in `from_records` Python routine an `Option`. [#35](https://github.com/feos-org/feos/pull/35)
 - Added panic with message when parsing missing Identifiers variants. [#35](https://github.com/feos-org/feos/pull/35)
+- Generalized the initialization routines for pure component VLEs at given temperature to multicomponent systems. [#23](https://github.com/feos-org/feos/pull/23)
+
+### Removed
+- Removed the (internal) `SpinodalPoint` struct that was used within density iterations in favor of a simpler interface. [#23](https://github.com/feos-org/feos/pull/23)
 
 ### Fixed
 - Avoid panicking when calculating `ResidualNpt` properties of states with negative pressures. [#42](https://github.com/feos-org/feos/pull/42)
diff --git a/feos-core/src/density_iteration.rs b/feos-core/src/density_iteration.rs
index b62cc89b6..67c8cd4b7 100644
--- a/feos-core/src/density_iteration.rs
+++ b/feos-core/src/density_iteration.rs
@@ -5,12 +5,6 @@ use crate::EosUnit;
 use quantity::{QuantityArray1, QuantityScalar};
 use std::rc::Rc;
 
-pub struct SpinodalPoint<U: EosUnit> {
-    pub p: QuantityScalar<U>,
-    pub dp_drho: QuantityScalar<U>,
-    pub rho: QuantityScalar<U>,
-}
-
 pub fn density_iteration<U: EosUnit, E: EquationOfState>(
     eos: &Rc<E>,
     temperature: QuantityScalar<U>,
@@ -64,9 +58,9 @@ pub fn density_iteration<U: EosUnit, E: EquationOfState>(
                 .2;
 
             if rho > 0.85 * maxdensity {
-                let sp = pressure_spinodal(eos, temperature, initial_density, moles)?;
-                rho = sp.rho;
-                error = sp.p - pressure;
+                let [sp_p, sp_rho] = pressure_spinodal(eos, temperature, initial_density, moles)?;
+                rho = sp_rho;
+                error = sp_p - pressure;
                 if rho > 0.85 * maxdensity {
                     if error.is_sign_negative() {
                         return Err(EosError::IterationFailed(String::from("density_iteration")));
@@ -79,29 +73,28 @@ pub fn density_iteration<U: EosUnit, E: EquationOfState>(
                     rho = (rho * 1.1).min(maxdensity)?
                 }
             } else if error.is_sign_positive() && d2pdrho2.is_sign_positive() {
-                let sp = pressure_spinodal(eos, temperature, initial_density, moles)?;
-                rho = sp.rho;
-                error = sp.p - pressure;
+                let [sp_p, sp_rho] = pressure_spinodal(eos, temperature, initial_density, moles)?;
+                rho = sp_rho;
+                error = sp_p - pressure;
                 if error.is_sign_positive() {
                     rho = 0.001 * maxdensity
                 } else {
                     rho = (rho * 1.1).min(maxdensity)?
                 }
             } else if error.is_sign_negative() && d2pdrho2.is_sign_negative() {
-                let sp = pressure_spinodal(eos, temperature, initial_density, moles)?;
-                rho = sp.rho;
-                error = sp.p - pressure;
+                let [sp_p, sp_rho] = pressure_spinodal(eos, temperature, initial_density, moles)?;
+                rho = sp_rho;
+                error = sp_p - pressure;
                 if error.is_sign_negative() {
                     rho = 0.8 * maxdensity
                 } else {
                     rho = rho * 0.8
                 }
             } else if error.is_sign_negative() && d2pdrho2.is_sign_positive() {
-                let sp_l = pressure_spinodal(eos, temperature, 0.8 * maxdensity, moles)?;
-                let rho_l = sp_l.rho;
-                let sp_v = pressure_spinodal(eos, temperature, 0.001 * maxdensity, moles)?;
-                let rho_v = sp_v.rho;
-                error = sp_v.p - pressure;
+                let [_, rho_l] = pressure_spinodal(eos, temperature, 0.8 * maxdensity, moles)?;
+                let [sp_v_p, rho_v] =
+                    pressure_spinodal(eos, temperature, 0.001 * maxdensity, moles)?;
+                error = sp_v_p - pressure;
                 if error.is_sign_positive()
                     && (initial_density - rho_v).abs() < (initial_density - rho_l).abs()
                 {
@@ -110,11 +103,10 @@ pub fn density_iteration<U: EosUnit, E: EquationOfState>(
                     rho = (rho_l * 1.1).min(maxdensity)?
                 }
             } else if error.is_sign_positive() && d2pdrho2.is_sign_negative() {
-                let sp_l = pressure_spinodal(eos, temperature, 0.8 * maxdensity, moles)?;
-                let rho_l = sp_l.rho;
-                let sp_v = pressure_spinodal(eos, temperature, 0.001 * maxdensity, moles)?;
-                let rho_v = sp_v.rho;
-                error = sp_v.p - pressure;
+                let [_, rho_l] = pressure_spinodal(eos, temperature, 0.8 * maxdensity, moles)?;
+                let [sp_v_p, rho_v] =
+                    pressure_spinodal(eos, temperature, 0.001 * maxdensity, moles)?;
+                error = sp_v_p - pressure;
                 if error.is_sign_negative()
                     && (initial_density - rho_v).abs() > (initial_density - rho_l).abs()
                 {
@@ -149,12 +141,12 @@ pub fn density_iteration<U: EosUnit, E: EquationOfState>(
     }
 }
 
-pub fn pressure_spinodal<U: EosUnit, E: EquationOfState>(
+fn pressure_spinodal<U: EosUnit, E: EquationOfState>(
     eos: &Rc<E>,
     temperature: QuantityScalar<U>,
     rho_init: QuantityScalar<U>,
     moles: &QuantityArray1<U>,
-) -> EosResult<SpinodalPoint<U>> {
+) -> EosResult<[QuantityScalar<U>; 2]> {
     let maxiter = 30;
     let abstol = 1e-8;
 
@@ -186,11 +178,7 @@ pub fn pressure_spinodal<U: EosUnit, E: EquationOfState>(
             .abs()
             < abstol
         {
-            return Ok(SpinodalPoint {
-                p,
-                dp_drho: dpdrho,
-                rho,
-            });
+            return Ok([p, rho]);
         }
     }
     Err(EosError::NotConverged("pressure_spinodal".to_owned()))
diff --git a/feos-core/src/phase_equilibria/bubble_dew.rs b/feos-core/src/phase_equilibria/bubble_dew.rs
index d699d51df..d61cb0810 100644
--- a/feos-core/src/phase_equilibria/bubble_dew.rs
+++ b/feos-core/src/phase_equilibria/bubble_dew.rs
@@ -19,19 +19,9 @@ const TOL_OUTER: f64 = 1e-10;
 
 const MAX_TSTEP: f64 = 20.0;
 const MAX_LNPSTEP: f64 = 0.1;
-const PROMISING_F: f64 = 1.0;
-const P_START: f64 = 1.0 / 138.0649; // equivalent to 1 bar in SI units
-const T_START: f64 = 400.0;
 const NEWTON_TOL: f64 = 1e-3;
 
 impl<U: EosUnit> TPSpec<U> {
-    fn starting_value(&self) -> QuantityScalar<U> {
-        match self {
-            Self::Temperature(_) => P_START * U::reference_pressure(),
-            Self::Pressure(_) => T_START * U::reference_temperature(),
-        }
-    }
-
     pub(super) fn temperature_pressure(
         &self,
         tp_init: QuantityScalar<U>,
@@ -81,7 +71,7 @@ impl<U: EosUnit, E: EquationOfState> PhaseEquilibrium<U, E, 2> {
     where
         QuantityScalar<U>: std::fmt::Display,
     {
-        Self::bubble_dew_point_with_options(
+        Self::bubble_dew_point(
             eos,
             TPSpec::try_from(temperature_or_pressure)?,
             tp_init,
@@ -105,7 +95,7 @@ impl<U: EosUnit, E: EquationOfState> PhaseEquilibrium<U, E, 2> {
     where
         QuantityScalar<U>: std::fmt::Display,
     {
-        Self::bubble_dew_point_with_options(
+        Self::bubble_dew_point(
             eos,
             TPSpec::try_from(temperature_or_pressure)?,
             tp_init,
@@ -116,7 +106,7 @@ impl<U: EosUnit, E: EquationOfState> PhaseEquilibrium<U, E, 2> {
         )
     }
 
-    pub(super) fn bubble_dew_point_with_options(
+    pub(super) fn bubble_dew_point(
         eos: &Rc<E>,
         tp_spec: TPSpec<U>,
         tp_init: Option<QuantityScalar<U>>,
@@ -128,25 +118,184 @@ impl<U: EosUnit, E: EquationOfState> PhaseEquilibrium<U, E, 2> {
     where
         QuantityScalar<U>: std::fmt::Display,
     {
-        let tp_init = tp_init.unwrap_or_else(|| tp_spec.starting_value());
+        match tp_spec {
+            TPSpec::Temperature(t) => {
+                // First use given initial pressure if applicable
+                let mut vle = tp_init
+                    .map(|p| {
+                        Self::iterate_bubble_dew(
+                            eos,
+                            tp_spec,
+                            p,
+                            molefracs_spec,
+                            molefracs_init,
+                            bubble,
+                            options,
+                        )
+                    })
+                    .and_then(Result::ok);
+
+                // Next try to initialize with an ideal gas assumption
+                vle = vle.or_else(|| {
+                    let (p, x) =
+                        Self::starting_pressure_ideal_gas(eos, t, molefracs_spec, bubble).ok()?;
+                    Self::iterate_bubble_dew(
+                        eos,
+                        tp_spec,
+                        p,
+                        molefracs_spec,
+                        Some(&x),
+                        bubble,
+                        options,
+                    )
+                    .ok()
+                });
+
+                // Finally use the spinodal to initialize the calculation
+                vle.map_or_else(
+                    || {
+                        Self::iterate_bubble_dew(
+                            eos,
+                            tp_spec,
+                            Self::starting_pressure_spinodal(eos, t, molefracs_spec)?,
+                            molefracs_spec,
+                            molefracs_init,
+                            bubble,
+                            options,
+                        )
+                    },
+                    Ok,
+                )
+            }
+            TPSpec::Pressure(_) => {
+                let temperature = tp_init.expect("An initial temperature is required for the calculation of bubble/dew points at given pressure!");
+                Self::iterate_bubble_dew(
+                    eos,
+                    tp_spec,
+                    temperature,
+                    molefracs_spec,
+                    molefracs_init,
+                    bubble,
+                    options,
+                )
+            }
+        }
+    }
+
+    fn iterate_bubble_dew(
+        eos: &Rc<E>,
+        tp_spec: TPSpec<U>,
+        tp_init: QuantityScalar<U>,
+        molefracs_spec: &Array1<f64>,
+        molefracs_init: Option<&Array1<f64>>,
+        bubble: bool,
+        options: (SolverOptions, SolverOptions),
+    ) -> EosResult<Self>
+    where
+        QuantityScalar<U>: std::fmt::Display,
+    {
         let (var, t, p) = tp_spec.temperature_pressure(tp_init);
-        let (state1, state2) = if bubble {
+        let [state1, state2] = if bubble {
             starting_x2_bubble(eos, t, p, molefracs_spec, molefracs_init)
         } else {
             starting_x2_dew(eos, t, p, molefracs_spec, molefracs_init)
         }?;
         bubble_dew(tp_spec, var, state1, state2, options)
     }
+
+    fn starting_pressure_ideal_gas(
+        eos: &Rc<E>,
+        temperature: QuantityScalar<U>,
+        molefracs_spec: &Array1<f64>,
+        bubble: bool,
+    ) -> EosResult<(QuantityScalar<U>, Array1<f64>)>
+    where
+        QuantityScalar<U>: std::fmt::Display,
+    {
+        if bubble {
+            Self::starting_pressure_ideal_gas_bubble(eos, temperature, molefracs_spec)
+        } else {
+            Self::starting_pressure_ideal_gas_dew(eos, temperature, molefracs_spec)
+        }
+    }
+
+    pub(super) fn starting_pressure_ideal_gas_bubble(
+        eos: &Rc<E>,
+        temperature: QuantityScalar<U>,
+        liquid_molefracs: &Array1<f64>,
+    ) -> EosResult<(QuantityScalar<U>, Array1<f64>)> {
+        let m = liquid_molefracs * U::reference_moles();
+        let density = 0.75 * eos.max_density(Some(&m))?;
+        let liquid = State::new_nvt(eos, temperature, m.sum() / density, &m)?;
+        let v_l = liquid.molar_volume(Contributions::Total);
+        let p_l = liquid.pressure(Contributions::Total);
+        let mu_l = liquid.chemical_potential(Contributions::ResidualNvt);
+        let p_i = (temperature * density * U::gas_constant() * liquid_molefracs)
+            * (mu_l - p_l * v_l)
+                .to_reduced(U::gas_constant() * temperature)?
+                .mapv(f64::exp);
+        let y = p_i.to_reduced(p_i.sum())?;
+        Ok((p_i.sum(), y))
+    }
+
+    fn starting_pressure_ideal_gas_dew(
+        eos: &Rc<E>,
+        temperature: QuantityScalar<U>,
+        vapor_molefracs: &Array1<f64>,
+    ) -> EosResult<(QuantityScalar<U>, Array1<f64>)>
+    where
+        QuantityScalar<U>: std::fmt::Display,
+    {
+        let mut p: Option<QuantityScalar<U>> = None;
+
+        let mut x = vapor_molefracs.clone();
+        for _ in 0..5 {
+            let m = x * U::reference_moles();
+            let density = 0.75 * eos.max_density(Some(&m))?;
+            let liquid = State::new_nvt(eos, temperature, m.sum() / density, &m)?;
+            let v_l = liquid.molar_volume(Contributions::Total);
+            let p_l = liquid.pressure(Contributions::Total);
+            let mu_l = liquid.chemical_potential(Contributions::ResidualNvt);
+            let k = vapor_molefracs
+                / (mu_l - p_l * v_l)
+                    .to_reduced(U::gas_constant() * temperature)?
+                    .mapv(f64::exp);
+            let p_new = U::gas_constant() * temperature * density / k.sum();
+            x = &k / k.sum();
+            if let Some(p_old) = p {
+                if (p_new - p_old).to_reduced(p_old)?.abs() < 1e-5 {
+                    p = Some(p_new);
+                    break;
+                }
+            }
+            p = Some(p_new);
+        }
+        Ok((p.unwrap(), x))
+    }
+
+    pub(super) fn starting_pressure_spinodal(
+        eos: &Rc<E>,
+        temperature: QuantityScalar<U>,
+        molefracs: &Array1<f64>,
+    ) -> EosResult<QuantityScalar<U>>
+    where
+        QuantityScalar<U>: std::fmt::Display,
+    {
+        let moles = molefracs * U::reference_moles();
+        let [sp_v, sp_l] = State::spinodal(eos, temperature, Some(&moles), Default::default())?;
+        let pv = sp_v.pressure(Contributions::Total);
+        let pl = sp_l.pressure(Contributions::Total);
+        Ok(0.5 * ((0.0 * U::reference_pressure()).max(pl)? + pv))
+    }
 }
 
-#[allow(clippy::type_complexity)]
 fn starting_x2_bubble<U: EosUnit, E: EquationOfState>(
     eos: &Rc<E>,
     temperature: QuantityScalar<U>,
     pressure: QuantityScalar<U>,
     liquid_molefracs: &Array1<f64>,
     vapor_molefracs: Option<&Array1<f64>>,
-) -> EosResult<(State<U, E>, State<U, E>)> {
+) -> EosResult<[State<U, E>; 2]> {
     let liquid_state = State::new_npt(
         eos,
         temperature,
@@ -165,17 +314,16 @@ fn starting_x2_bubble<U: EosUnit, E: EquationOfState>(
         &(xv * U::reference_moles()),
         Vapor,
     )?;
-    Ok((liquid_state, vapor_state))
+    Ok([liquid_state, vapor_state])
 }
 
-#[allow(clippy::type_complexity)]
 fn starting_x2_dew<U: EosUnit, E: EquationOfState>(
     eos: &Rc<E>,
     temperature: QuantityScalar<U>,
     pressure: QuantityScalar<U>,
     vapor_molefracs: &Array1<f64>,
     liquid_molefracs: Option<&Array1<f64>>,
-) -> EosResult<(State<U, E>, State<U, E>)> {
+) -> EosResult<[State<U, E>; 2]> {
     let vapor_state = State::new_npt(
         eos,
         temperature,
@@ -204,7 +352,7 @@ fn starting_x2_dew<U: EosUnit, E: EquationOfState>(
         &(xl * U::reference_moles()),
         Liquid,
     )?;
-    Ok((vapor_state, liquid_state))
+    Ok([vapor_state, liquid_state])
 }
 
 fn bubble_dew<U: EosUnit, E: EquationOfState>(
@@ -223,10 +371,9 @@ where
     let mut err_out = 1.0;
     let mut k_out = 0;
 
-    // If the starting values are insufficient find better ones
-    if !promising_values(&state1, &state2) {
+    if PhaseEquilibrium::is_trivial_solution(&state1, &state2) {
         log_iter!(options_outer.verbosity, "Trivial solution encountered!");
-        // find_starting_values(&mut var_tp, &mut state1, bubble)?;
+        return Err(EosError::TrivialSolution);
     }
 
     log_iter!(
@@ -235,6 +382,14 @@ where
         var_tp.identifier()
     );
     log_iter!(options_outer.verbosity, "{:-<85}", "");
+    log_iter!(
+        options_outer.verbosity,
+        "{:14} | {:14} | {:12.8} | {:.8}",
+        "",
+        "",
+        var_tp,
+        state2.molefracs
+    );
 
     // Outer loop for finding x2
     for ko in 0..options_outer.max_iter.unwrap_or(MAX_ITER_OUTER) {
@@ -264,10 +419,9 @@ where
             )
         }?;
 
-        // if a trivial solution is encountered, reinitialize T or p
         if PhaseEquilibrium::is_trivial_solution(&state1, &state2) {
             log_iter!(options_outer.verbosity, "Trivial solution encountered!");
-            // find_starting_values(iterate_t, bubble, &mut itervars)?;
+            return Err(EosError::TrivialSolution);
         }
 
         if err_out < options_outer.tol.unwrap_or(TOL_OUTER) {
@@ -604,16 +758,3 @@ where
     );
     Ok(error)
 }
-
-fn promising_values<U: EosUnit, E: EquationOfState>(
-    state1: &State<U, E>,
-    state2: &State<U, E>,
-) -> bool {
-    if PhaseEquilibrium::is_trivial_solution(state1, state2) {
-        return false;
-    }
-
-    let ln_phi_1 = state1.ln_phi();
-    let ln_phi_2 = state2.ln_phi();
-    ((&state1.molefracs * &(ln_phi_1 - ln_phi_2).mapv(f64::exp)).sum() - 1.0).abs() < PROMISING_F
-}
diff --git a/feos-core/src/phase_equilibria/mod.rs b/feos-core/src/phase_equilibria/mod.rs
index 5bc5a5467..b5cf20255 100644
--- a/feos-core/src/phase_equilibria/mod.rs
+++ b/feos-core/src/phase_equilibria/mod.rs
@@ -10,6 +10,7 @@ use std::rc::Rc;
 mod bubble_dew;
 mod phase_diagram_binary;
 mod phase_diagram_pure;
+mod phase_envelope;
 mod stability_analysis;
 mod tp_flash;
 mod vle_pure;
diff --git a/feos-core/src/phase_equilibria/phase_diagram_binary.rs b/feos-core/src/phase_equilibria/phase_diagram_binary.rs
index 41a9d5d34..c158b87e0 100644
--- a/feos-core/src/phase_equilibria/phase_diagram_binary.rs
+++ b/feos-core/src/phase_equilibria/phase_diagram_binary.rs
@@ -209,7 +209,7 @@ where
     let mut y_old = None;
     vle_vec.push(vle_0);
     for xi in x {
-        let vle = PhaseEquilibrium::bubble_dew_point_with_options(
+        let vle = PhaseEquilibrium::bubble_dew_point(
             eos,
             tp,
             tp_old,
@@ -269,6 +269,7 @@ impl<U: EosUnit, E: EquationOfState> PhaseDiagram<U, E> {
         temperature_or_pressure: QuantityScalar<U>,
         x_lle: (f64, f64),
         tp_lim_lle: Option<QuantityScalar<U>>,
+        tp_init_vlle: Option<QuantityScalar<U>>,
         npoints_vle: Option<usize>,
         npoints_lle: Option<usize>,
         bubble_dew_options: (SolverOptions, SolverOptions),
@@ -289,6 +290,7 @@ impl<U: EosUnit, E: EquationOfState> PhaseDiagram<U, E> {
                 eos,
                 t,
                 x_lle,
+                tp_init_vlle,
                 SolverOptions::default(),
                 bubble_dew_options,
             ),
@@ -296,6 +298,7 @@ impl<U: EosUnit, E: EquationOfState> PhaseDiagram<U, E> {
                 eos,
                 p,
                 x_lle,
+                tp_init_vlle,
                 SolverOptions::default(),
                 bubble_dew_options,
             ),
@@ -368,15 +371,16 @@ where
         eos: &Rc<E>,
         temperature_or_pressure: QuantityScalar<U>,
         x_init: (f64, f64),
+        tp_init: Option<QuantityScalar<U>>,
         options: SolverOptions,
         bubble_dew_options: (SolverOptions, SolverOptions),
     ) -> EosResult<Self> {
         match TPSpec::try_from(temperature_or_pressure)? {
             TPSpec::Temperature(t) => {
-                Self::heteroazeotrope_t(eos, t, x_init, options, bubble_dew_options)
+                Self::heteroazeotrope_t(eos, t, x_init, tp_init, options, bubble_dew_options)
             }
             TPSpec::Pressure(p) => {
-                Self::heteroazeotrope_p(eos, p, x_init, options, bubble_dew_options)
+                Self::heteroazeotrope_p(eos, p, x_init, tp_init, options, bubble_dew_options)
             }
         }
     }
@@ -387,16 +391,29 @@ where
         eos: &Rc<E>,
         temperature: QuantityScalar<U>,
         x_init: (f64, f64),
+        p_init: Option<QuantityScalar<U>>,
         options: SolverOptions,
         bubble_dew_options: (SolverOptions, SolverOptions),
     ) -> EosResult<Self> {
         // calculate initial values using bubble point
         let x1 = arr1(&[x_init.0, 1.0 - x_init.0]);
         let x2 = arr1(&[x_init.1, 1.0 - x_init.1]);
-        let vle1 =
-            PhaseEquilibrium::bubble_point(eos, temperature, &x1, None, None, bubble_dew_options)?;
-        let vle2 =
-            PhaseEquilibrium::bubble_point(eos, temperature, &x2, None, None, bubble_dew_options)?;
+        let vle1 = PhaseEquilibrium::bubble_point(
+            eos,
+            temperature,
+            &x1,
+            p_init,
+            None,
+            bubble_dew_options,
+        )?;
+        let vle2 = PhaseEquilibrium::bubble_point(
+            eos,
+            temperature,
+            &x2,
+            p_init,
+            None,
+            bubble_dew_options,
+        )?;
         let mut l1 = vle1.liquid().clone();
         let mut l2 = vle2.liquid().clone();
         let p0 = (vle1.vapor().pressure(Contributions::Total)
@@ -526,6 +543,7 @@ where
         eos: &Rc<E>,
         pressure: QuantityScalar<U>,
         x_init: (f64, f64),
+        t_init: Option<QuantityScalar<U>>,
         options: SolverOptions,
         bubble_dew_options: (SolverOptions, SolverOptions),
     ) -> EosResult<Self> {
@@ -535,9 +553,9 @@ where
         let x1 = arr1(&[x_init.0, 1.0 - x_init.0]);
         let x2 = arr1(&[x_init.1, 1.0 - x_init.1]);
         let vle1 =
-            PhaseEquilibrium::bubble_point(eos, pressure, &x1, None, None, bubble_dew_options)?;
+            PhaseEquilibrium::bubble_point(eos, pressure, &x1, t_init, None, bubble_dew_options)?;
         let vle2 =
-            PhaseEquilibrium::bubble_point(eos, pressure, &x2, None, None, bubble_dew_options)?;
+            PhaseEquilibrium::bubble_point(eos, pressure, &x2, t_init, None, bubble_dew_options)?;
         let mut l1 = vle1.liquid().clone();
         let mut l2 = vle2.liquid().clone();
         let t0 = (vle1.vapor().temperature + vle2.vapor().temperature) * 0.5;
diff --git a/feos-core/src/phase_equilibria/phase_diagram_pure.rs b/feos-core/src/phase_equilibria/phase_diagram_pure.rs
index ca94a3ce3..31ccdfb17 100644
--- a/feos-core/src/phase_equilibria/phase_diagram_pure.rs
+++ b/feos-core/src/phase_equilibria/phase_diagram_pure.rs
@@ -2,7 +2,7 @@ use super::{PhaseEquilibrium, SolverOptions};
 use crate::equation_of_state::EquationOfState;
 use crate::errors::EosResult;
 use crate::state::{State, StateVec};
-use crate::{Contributions, EosUnit};
+use crate::EosUnit;
 use quantity::{QuantityArray1, QuantityScalar};
 use std::rc::Rc;
 
@@ -60,64 +60,4 @@ impl<U: EosUnit, E: EquationOfState> PhaseDiagram<U, E> {
     pub fn liquid(&self) -> StateVec<'_, U, E> {
         self.states.iter().map(|s| s.liquid()).collect()
     }
-
-    pub fn mix(
-        eos: &Rc<E>,
-        moles: &QuantityArray1<U>,
-        min_pressure: QuantityScalar<U>,
-        npoints: usize,
-        critical_temperature: Option<QuantityScalar<U>>,
-        options: (SolverOptions, SolverOptions),
-    ) -> EosResult<Self>
-    where
-        QuantityScalar<U>: std::fmt::Display + std::fmt::LowerExp,
-    {
-        let mut states = Vec::with_capacity(npoints);
-
-        let sc = State::critical_point(
-            eos,
-            Some(moles),
-            critical_temperature,
-            SolverOptions::default(),
-        )?;
-
-        let max_pressure = min_pressure
-            + (sc.pressure(Contributions::Total) - min_pressure)
-                * ((npoints - 2) as f64 / (npoints - 1) as f64);
-        let pressures = QuantityArray1::linspace(min_pressure, max_pressure, npoints - 1)?;
-        let molefracs = moles.to_reduced(moles.sum())?;
-
-        let mut vle_liquid: Option<PhaseEquilibrium<U, E, 2>> = None;
-        let mut vle_vapor: Option<PhaseEquilibrium<U, E, 2>> = None;
-        for ti in &pressures {
-            // calculate new liquid point
-            let t_init = vle_liquid.as_ref().map(|vle| vle.vapor().temperature);
-            let vapor_molefracs = vle_liquid.as_ref().map(|vle| &vle.vapor().molefracs);
-            vle_liquid = PhaseEquilibrium::bubble_point(
-                eos,
-                ti,
-                &molefracs,
-                t_init,
-                vapor_molefracs,
-                options,
-            )
-            .ok();
-
-            // calculate new vapor point
-            let t_init = vle_vapor.as_ref().map(|vle| vle.liquid().temperature);
-            let liquid_molefracs = vle_vapor.as_ref().map(|vle| &vle.liquid().molefracs);
-            vle_vapor =
-                PhaseEquilibrium::dew_point(eos, ti, &molefracs, t_init, liquid_molefracs, options)
-                    .ok();
-            if let (Some(vle_liquid), Some(vle_vapor)) = (vle_liquid.as_ref(), vle_vapor.as_ref()) {
-                states.push(PhaseEquilibrium::from_states(
-                    vle_liquid.liquid().clone(),
-                    vle_vapor.vapor().clone(),
-                ));
-            }
-        }
-        states.push(PhaseEquilibrium::from_states(sc.clone(), sc));
-
-        Ok(PhaseDiagram { states })
-    }
 }
diff --git a/feos-core/src/phase_equilibria/phase_envelope.rs b/feos-core/src/phase_equilibria/phase_envelope.rs
new file mode 100644
index 000000000..161fb09a2
--- /dev/null
+++ b/feos-core/src/phase_equilibria/phase_envelope.rs
@@ -0,0 +1,165 @@
+use super::{PhaseDiagram, PhaseEquilibrium, SolverOptions};
+use crate::equation_of_state::EquationOfState;
+use crate::errors::EosResult;
+use crate::state::State;
+use crate::{Contributions, EosUnit};
+use quantity::{QuantityArray1, QuantityScalar};
+use std::rc::Rc;
+
+impl<U: EosUnit, E: EquationOfState> PhaseDiagram<U, E> {
+    /// Calculate the bubble point line of a mixture with given composition.
+    pub fn bubble_point_line(
+        eos: &Rc<E>,
+        moles: &QuantityArray1<U>,
+        min_temperature: QuantityScalar<U>,
+        npoints: usize,
+        critical_temperature: Option<QuantityScalar<U>>,
+        options: (SolverOptions, SolverOptions),
+    ) -> EosResult<Self>
+    where
+        QuantityScalar<U>: std::fmt::Display + std::fmt::LowerExp,
+    {
+        let mut states = Vec::with_capacity(npoints);
+
+        let sc = State::critical_point(
+            eos,
+            Some(moles),
+            critical_temperature,
+            SolverOptions::default(),
+        )?;
+
+        let max_temperature = min_temperature
+            + (sc.temperature - min_temperature) * ((npoints - 2) as f64 / (npoints - 1) as f64);
+        let temperatures = QuantityArray1::linspace(min_temperature, max_temperature, npoints - 1)?;
+        let molefracs = moles.to_reduced(moles.sum())?;
+
+        let mut vle: Option<PhaseEquilibrium<U, E, 2>> = None;
+        for ti in &temperatures {
+            // calculate new liquid point
+            let p_init = vle
+                .as_ref()
+                .map(|vle| vle.vapor().pressure(Contributions::Total));
+            let vapor_molefracs = vle.as_ref().map(|vle| &vle.vapor().molefracs);
+            vle = PhaseEquilibrium::bubble_point(
+                eos,
+                ti,
+                &molefracs,
+                p_init,
+                vapor_molefracs,
+                options,
+            )
+            .ok();
+
+            if let Some(vle) = vle.as_ref() {
+                states.push(vle.clone());
+            }
+        }
+        states.push(PhaseEquilibrium::from_states(sc.clone(), sc));
+
+        Ok(PhaseDiagram { states })
+    }
+
+    /// Calculate the dew point line of a mixture with given composition.
+    pub fn dew_point_line(
+        eos: &Rc<E>,
+        moles: &QuantityArray1<U>,
+        min_temperature: QuantityScalar<U>,
+        npoints: usize,
+        critical_temperature: Option<QuantityScalar<U>>,
+        options: (SolverOptions, SolverOptions),
+    ) -> EosResult<Self>
+    where
+        QuantityScalar<U>: std::fmt::Display + std::fmt::LowerExp,
+    {
+        let mut states = Vec::with_capacity(npoints);
+
+        let sc = State::critical_point(
+            eos,
+            Some(moles),
+            critical_temperature,
+            SolverOptions::default(),
+        )?;
+
+        let n_t = npoints / 2;
+        let max_temperature = min_temperature
+            + (sc.temperature - min_temperature) * ((n_t - 2) as f64 / (n_t - 1) as f64);
+        let temperatures = QuantityArray1::linspace(min_temperature, max_temperature, n_t - 1)?;
+        let molefracs = moles.to_reduced(moles.sum())?;
+
+        let mut vle: Option<PhaseEquilibrium<U, E, 2>> = None;
+        for ti in &temperatures {
+            let p_init = vle
+                .as_ref()
+                .map(|vle| vle.vapor().pressure(Contributions::Total));
+            let liquid_molefracs = vle.as_ref().map(|vle| &vle.liquid().molefracs);
+            vle =
+                PhaseEquilibrium::dew_point(eos, ti, &molefracs, p_init, liquid_molefracs, options)
+                    .ok();
+            if let Some(vle) = vle.as_ref() {
+                states.push(vle.clone());
+            }
+        }
+
+        let n_p = npoints - n_t;
+        if vle.is_none() {
+            return Ok(PhaseDiagram { states });
+        }
+
+        let min_pressure = vle.as_ref().unwrap().vapor().pressure(Contributions::Total);
+        let p_c = sc.pressure(Contributions::Total);
+        let max_pressure =
+            min_pressure + (p_c - min_pressure) * ((n_p - 2) as f64 / (n_p - 1) as f64);
+        let pressures = QuantityArray1::linspace(min_pressure, max_pressure, n_p)?;
+
+        for pi in &pressures {
+            let t_init = vle.as_ref().map(|vle| vle.vapor().temperature);
+            let liquid_molefracs = vle.as_ref().map(|vle| &vle.liquid().molefracs);
+            vle =
+                PhaseEquilibrium::dew_point(eos, pi, &molefracs, t_init, liquid_molefracs, options)
+                    .ok();
+            if let Some(vle) = vle.as_ref() {
+                states.push(vle.clone());
+            }
+        }
+
+        states.push(PhaseEquilibrium::from_states(sc.clone(), sc));
+
+        Ok(PhaseDiagram { states })
+    }
+
+    /// Calculate the spinodal lines for a mixture with fixed composition.
+    pub fn spinodal(
+        eos: &Rc<E>,
+        moles: &QuantityArray1<U>,
+        min_temperature: QuantityScalar<U>,
+        npoints: usize,
+        critical_temperature: Option<QuantityScalar<U>>,
+        options: SolverOptions,
+    ) -> EosResult<Self>
+    where
+        QuantityScalar<U>: std::fmt::Display + std::fmt::LowerExp,
+    {
+        let mut states = Vec::with_capacity(npoints);
+
+        let sc = State::critical_point(
+            eos,
+            Some(moles),
+            critical_temperature,
+            SolverOptions::default(),
+        )?;
+
+        let max_temperature = min_temperature
+            + (sc.temperature - min_temperature) * ((npoints - 2) as f64 / (npoints - 1) as f64);
+        let temperatures = QuantityArray1::linspace(min_temperature, max_temperature, npoints - 1)?;
+
+        for ti in &temperatures {
+            let spinodal = State::spinodal(eos, ti, Some(moles), options).ok();
+            if let Some(spinodal) = spinodal {
+                states.push(PhaseEquilibrium(spinodal));
+            }
+        }
+        states.push(PhaseEquilibrium::from_states(sc.clone(), sc));
+
+        Ok(PhaseDiagram { states })
+    }
+}
diff --git a/feos-core/src/phase_equilibria/vle_pure.rs b/feos-core/src/phase_equilibria/vle_pure.rs
index db8b0cddd..8b3130d3c 100644
--- a/feos-core/src/phase_equilibria/vle_pure.rs
+++ b/feos-core/src/phase_equilibria/vle_pure.rs
@@ -1,11 +1,10 @@
 use super::{PhaseEquilibrium, SolverOptions, Verbosity};
-use crate::density_iteration::pressure_spinodal;
 use crate::equation_of_state::EquationOfState;
 use crate::errors::{EosError, EosResult};
 use crate::state::{Contributions, DensityInitialization, State, TPSpec};
 use crate::EosUnit;
 use ndarray::{arr1, Array1};
-use quantity::{QuantityArray1, QuantityScalar};
+use quantity::QuantityScalar;
 use std::convert::TryFrom;
 use std::rc::Rc;
 
@@ -274,39 +273,19 @@ impl<U: EosUnit, E: EquationOfState> PhaseEquilibrium<U, E, 2> {
 
     fn init_pure_ideal_gas(eos: &Rc<E>, temperature: QuantityScalar<U>) -> EosResult<Self> {
         let m = arr1(&[1.0]) * U::reference_moles();
-        let density = 0.75 * eos.max_density(None)?;
-        let liquid = State::new_nvt(eos, temperature, U::reference_moles() / density, &m)?;
-        let z = liquid.compressibility(Contributions::Total);
-        let mu = liquid.chemical_potential(Contributions::ResidualNvt);
-        let p = temperature
-            * density
-            * U::gas_constant()
-            * (mu.get(0).to_reduced(U::gas_constant() * temperature)? - z).exp();
+        let p = Self::starting_pressure_ideal_gas_bubble(eos, temperature, &arr1(&[1.0]))?.0;
         PhaseEquilibrium::new_npt(eos, temperature, p, &m, &m)?.check_trivial_solution()
     }
 
-    fn init_pure_spinodal(eos: &Rc<E>, temperature: QuantityScalar<U>) -> EosResult<Self> {
+    fn init_pure_spinodal(eos: &Rc<E>, temperature: QuantityScalar<U>) -> EosResult<Self>
+    where
+        QuantityScalar<U>: std::fmt::Display,
+    {
+        let p = Self::starting_pressure_spinodal(eos, temperature, &arr1(&[1.0]))?;
         let m = arr1(&[1.0]) * U::reference_moles();
-        let spinodal = Self::spinodal(eos, temperature, &m)?;
-        let pv = spinodal.vapor().pressure(Contributions::Total);
-        let pl = spinodal.liquid().pressure(Contributions::Total);
-        let p = 0.5 * ((0.0 * U::reference_pressure()).max(pl)? + pv);
         PhaseEquilibrium::new_npt(eos, temperature, p, &m, &m)
     }
 
-    fn spinodal(
-        eos: &Rc<E>,
-        temperature: QuantityScalar<U>,
-        moles: &QuantityArray1<U>,
-    ) -> EosResult<Self> {
-        let max_density = eos.max_density(Some(moles))?;
-        let sp = pressure_spinodal(eos, temperature, max_density * 1e-5, moles)?;
-        let vapor = State::new_nvt(eos, temperature, moles.get(0) / sp.rho, moles)?;
-        let sp = pressure_spinodal(eos, temperature, max_density, moles)?;
-        let liquid = State::new_nvt(eos, temperature, moles.get(0) / sp.rho, moles)?;
-        Ok(PhaseEquilibrium([vapor, liquid]))
-    }
-
     /// Initialize a new VLE for a pure substance for a given pressure.
     fn init_pure_p(eos: &Rc<E>, pressure: QuantityScalar<U>) -> EosResult<Self>
     where
diff --git a/feos-core/src/python/phase_equilibria.rs b/feos-core/src/python/phase_equilibria.rs
index 73aa434f2..d1f8a3293 100644
--- a/feos-core/src/python/phase_equilibria.rs
+++ b/feos-core/src/python/phase_equilibria.rs
@@ -346,6 +346,9 @@ macro_rules! impl_phase_equilibrium {
             /// x_init : list[float]
             ///     Initial guesses for the liquid molefracs of component 1
             ///     at the heteroazeotropic point.
+            /// tp_init : SINumber, optional
+            ///     Initial guess for the temperature/pressure at the
+            ///     heteroszeotropic point.
             /// max_iter : int, optional
             ///     The maximum number of iterations.
             /// tol: float, optional
@@ -363,11 +366,12 @@ macro_rules! impl_phase_equilibrium {
             /// verbosity_bd : Verbosity, optional
             ///     The verbosity of the bubble/dew point iteration.
             #[staticmethod]
-            #[pyo3(text_signature = "(eos, temperature_or_pressure, x_init, max_iter=None, tol=None, verbosity=None, max_iter_bd_inner=None, max_iter_bd_outer=None, tol_bd_inner=None, tol_bd_outer=None, verbosity_bd=None)")]
+            #[pyo3(text_signature = "(eos, temperature_or_pressure, x_init, tp_init=None, max_iter=None, tol=None, verbosity=None, max_iter_bd_inner=None, max_iter_bd_outer=None, tol_bd_inner=None, tol_bd_outer=None, verbosity_bd=None)")]
             fn heteroazeotrope(
                 eos: $py_eos,
                 temperature_or_pressure: PySINumber,
                 x_init: (f64, f64),
+                tp_init: Option<PySINumber>,
                 max_iter: Option<usize>,
                 tol: Option<f64>,
                 verbosity: Option<Verbosity>,
@@ -381,6 +385,7 @@ macro_rules! impl_phase_equilibrium {
                     &eos.0,
                     temperature_or_pressure.into(),
                     x_init,
+                    tp_init.map(|t| t.into()),
                     (max_iter, tol, verbosity).into(),
                     (
                         (max_iter_bd_inner, tol_bd_inner, verbosity_bd).into(),
@@ -508,7 +513,11 @@ macro_rules! impl_phase_equilibrium {
                 Ok(Self(dia))
             }
 
-            /// Calculate a phase diagram for a mixture with fixed composition.
+            /// Calculate the bubble point line of a mixture with given composition.
+            ///
+            /// In the resulting phase diagram, the liquid states correspond to the
+            /// bubble point line while the vapor states contain the corresponding
+            /// equilibrium states at different compositions.
             ///
             /// Parameters
             /// ----------
@@ -540,7 +549,7 @@ macro_rules! impl_phase_equilibrium {
             /// PhaseDiagram
             #[staticmethod]
             #[pyo3(text_signature = "(eos, moles, min_temperature, npoints, critical_temperature=None, max_iter_inner=None, max_iter_outer=None, tol_inner=None, tol_outer=None, verbosity=None)")]
-            pub fn mix(
+            pub fn bubble_point_line(
                 eos: &$py_eos,
                 moles: PySIArray1,
                 min_temperature: PySINumber,
@@ -552,7 +561,7 @@ macro_rules! impl_phase_equilibrium {
                 tol_outer: Option<f64>,
                 verbosity: Option<Verbosity>,
             ) -> PyResult<Self> {
-                let dia = PhaseDiagram::mix(
+                let dia = PhaseDiagram::bubble_point_line(
                     &eos.0,
                     &moles,
                     min_temperature.into(),
@@ -566,6 +575,117 @@ macro_rules! impl_phase_equilibrium {
                 Ok(Self(dia))
             }
 
+            /// Calculate the dew point line of a mixture with given composition.
+            ///
+            /// In the resulting phase diagram, the vapor states correspond to the
+            /// dew point line while the liquid states contain the corresponding
+            /// equilibrium states at different compositions.
+            ///
+            /// Parameters
+            /// ----------
+            /// eos: Eos
+            ///     The equation of state.
+            /// moles: SIArray1
+            ///     The moles of the individual components
+            /// min_temperature: SINumber
+            ///     The lower limit for the temperature.
+            /// npoints: int
+            ///     The number of points.
+            /// critical_temperature: SINumber, optional
+            ///     An estimate for the critical temperature to initialize
+            ///     the calculation if necessary. For most components not necessary.
+            ///     Defaults to `None`.
+            /// max_iter_inner : int, optional
+            ///     The maximum number of inner iterations in the bubble/dew point iteration.
+            /// max_iter_outer : int, optional
+            ///     The maximum number of outer iterations in the bubble/dew point iteration.
+            /// tol_inner : float, optional
+            ///     The solution tolerance in the inner loop of the bubble/dew point iteration.
+            /// tol_outer : float, optional
+            ///     The solution tolerance in the outer loop of the bubble/dew point iteration.
+            /// verbosity : Verbosity, optional
+            ///     The verbosity of the bubble/dew point iteration.
+            ///
+            /// Returns
+            /// -------
+            /// PhaseDiagram
+            #[staticmethod]
+            #[pyo3(text_signature = "(eos, moles, min_temperature, npoints, critical_temperature=None, max_iter_inner=None, max_iter_outer=None, tol_inner=None, tol_outer=None, verbosity=None)")]
+            pub fn dew_point_line(
+                eos: &$py_eos,
+                moles: PySIArray1,
+                min_temperature: PySINumber,
+                npoints: usize,
+                critical_temperature: Option<PySINumber>,
+                max_iter_inner: Option<usize>,
+                max_iter_outer: Option<usize>,
+                tol_inner: Option<f64>,
+                tol_outer: Option<f64>,
+                verbosity: Option<Verbosity>,
+            ) -> PyResult<Self> {
+                let dia = PhaseDiagram::dew_point_line(
+                    &eos.0,
+                    &moles,
+                    min_temperature.into(),
+                    npoints,
+                    critical_temperature.map(|t| t.into()),
+                    (
+                        (max_iter_inner, tol_inner, verbosity).into(),
+                        (max_iter_outer, tol_outer, verbosity).into(),
+                    )
+                )?;
+                Ok(Self(dia))
+            }
+
+            /// Calculate the spinodal lines for a mixture with fixed composition.
+            ///
+            /// Parameters
+            /// ----------
+            /// eos: Eos
+            ///     The equation of state.
+            /// moles: SIArray1
+            ///     The moles of the individual components
+            /// min_temperature: SINumber
+            ///     The lower limit for the temperature.
+            /// npoints: int
+            ///     The number of points.
+            /// critical_temperature: SINumber, optional
+            ///     An estimate for the critical temperature to initialize
+            ///     the calculation if necessary. For most components not necessary.
+            ///     Defaults to `None`.
+            /// max_iter : int, optional
+            ///     The maximum number of iterations.
+            /// tol: float, optional
+            ///     The solution tolerance.
+            /// verbosity : Verbosity, optional
+            ///     The verbosity.
+            ///
+            /// Returns
+            /// -------
+            /// PhaseDiagram
+            #[staticmethod]
+            #[pyo3(text_signature = "(eos, moles, min_temperature, npoints, critical_temperature=None, max_iter=None, tol=None, verbosity=None)")]
+            pub fn spinodal(
+                eos: &$py_eos,
+                moles: PySIArray1,
+                min_temperature: PySINumber,
+                npoints: usize,
+                critical_temperature: Option<PySINumber>,
+                max_iter: Option<usize>,
+                tol: Option<f64>,
+                verbosity: Option<Verbosity>,
+            ) -> PyResult<Self> {
+                let dia = PhaseDiagram::spinodal(
+                    &eos.0,
+                    &moles,
+                    min_temperature.into(),
+                    npoints,
+                    critical_temperature.map(|t| t.into()),
+                    (max_iter, tol, verbosity).into(),
+                )?;
+                Ok(Self(dia))
+            }
+
             #[getter]
             pub fn get_states(&self) -> Vec<PyPhaseEquilibrium> {
                 self.0
@@ -739,14 +859,16 @@ macro_rules! impl_phase_equilibrium {
             /// ----------
             /// eos: SaftFunctional
             ///     The SAFT Helmholtz energy functional.
-            /// pressure: SINumber
-            ///     The pressure.
+            /// temperature_or_pressure: SINumber
+            ///     The temperature_or_pressure.
             /// x_lle: SINumber
             ///     Initial values for the molefractions of component 1
             ///     at the heteroazeotrop.
-            /// min_temperature_lle: SINumber, optional
+            /// tp_lim_lle: SINumber, optional
             ///     The minimum temperature up to which the LLE is calculated.
             ///     If it is not provided, no LLE is calcualted.
+            /// tp_init_vlle: SINumber, optional
+            ///     Initial value for the calculation of the VLLE.
             /// npoints_vle: int, optional
             ///     The number of points for the VLE (default 51).
             /// npoints_lle: int, optional
@@ -766,12 +888,13 @@ macro_rules! impl_phase_equilibrium {
             /// -------
             /// PhaseDiagramHetero
             #[staticmethod]
-            #[pyo3(text_signature = "(eos, pressure, x_lle, min_temperature_lle=None, npoints_vle=None, npoints_lle=None, max_iter_bd_inner=None, max_iter_bd_outer=None, tol_bd_inner=None, tol_bd_outer=None, verbosity_bd=None)")]
+            #[pyo3(text_signature = "(eos, temperature_or_pressure, x_lle, tp_lim_lle=None, tp_init_vlle=None, npoints_vle=None, npoints_lle=None, max_iter_bd_inner=None, max_iter_bd_outer=None, tol_bd_inner=None, tol_bd_outer=None, verbosity_bd=None)")]
             pub fn binary_vlle(
                 eos: $py_eos,
-                pressure: PySINumber,
+                temperature_or_pressure: PySINumber,
                 x_lle: (f64, f64),
-                min_temperature_lle: Option<PySINumber>,
+                tp_lim_lle: Option<PySINumber>,
+                tp_init_vlle: Option<PySINumber>,
                 npoints_vle: Option<usize>,
                 npoints_lle: Option<usize>,
                 max_iter_inner: Option<usize>,
@@ -782,9 +905,10 @@ macro_rules! impl_phase_equilibrium {
             ) -> PyResult<PyPhaseDiagramHetero> {
                 let dia = PhaseDiagram::binary_vlle(
                     &eos.0,
-                    pressure.into(),
+                    temperature_or_pressure.into(),
                     x_lle,
-                    min_temperature_lle.map(|t| t.into()),
+                    tp_lim_lle.map(|t| t.into()),
+                    tp_init_vlle.map(|t| t.into()),
                     npoints_vle,
                     npoints_lle,
                     (
diff --git a/feos-core/src/python/state.rs b/feos-core/src/python/state.rs
index f9586cd3b..6d36e512e 100644
--- a/feos-core/src/python/state.rs
+++ b/feos-core/src/python/state.rs
@@ -224,6 +224,46 @@ macro_rules! impl_state {
                 )?))
             }
 
+            /// Calculate spinodal states for a given temperature and composition.
+            ///
+            /// Parameters
+            /// ----------
+            /// eos: EquationOfState
+            ///     The equation of state to use.
+            /// temperature: SINumber
+            ///     The temperature.
+            /// moles: SIArray1, optional
+            ///     Amount of substance of each component.
+            ///     Only optional for a pure component.
+            /// max_iter : int, optional
+            ///     The maximum number of iterations.
+            /// tol: float, optional
+            ///     The solution tolerance.
+            /// verbosity : Verbosity, optional
+            ///     The verbosity.
+            ///
+            /// Returns
+            /// -------
+            /// State : State at critical conditions.
+            #[staticmethod]
+            #[pyo3(text_signature = "(eos, temperature, moles=None, max_iter=None, tol=None, verbosity=None)")]
+            fn spinodal(
+                eos: $py_eos,
+                temperature: PySINumber,
+                moles: Option<PySIArray1>,
+                max_iter: Option<usize>,
+                tol: Option<f64>,
+                verbosity: Option<Verbosity>,
+            ) -> PyResult<(Self, Self)> {
+                let [state1, state2] = State::spinodal(
+                    &eos.0,
+                    temperature.into(),
+                    moles.as_deref(),
+                    (max_iter, tol, verbosity).into(),
+                )?;
+                Ok((PyState(state1), PyState(state2)))
+            }
+
             /// Performs a stability analysis and returns a list of stable
             /// candidate states.
             ///
diff --git a/feos-core/src/state/critical_point.rs b/feos-core/src/state/critical_point.rs
index 5676171c7..fd875e56d 100644
--- a/feos-core/src/state/critical_point.rs
+++ b/feos-core/src/state/critical_point.rs
@@ -2,7 +2,7 @@ use super::{State, StateHD, TPSpec};
 use crate::equation_of_state::EquationOfState;
 use crate::errors::{EosError, EosResult};
 use crate::phase_equilibria::{SolverOptions, Verbosity};
-use crate::EosUnit;
+use crate::{DensityInitialization, EosUnit};
 use ndarray::{arr1, arr2, Array1, Array2};
 use num_dual::linalg::{norm, smallest_ev, LU};
 use num_dual::{Dual, Dual3, Dual64, DualNum, DualVec64, HyperDual, StaticVec};
@@ -359,9 +359,113 @@ impl<U: EosUnit, E: EquationOfState> State<U, E> {
         }
         Err(EosError::NotConverged(String::from("Critical point")))
     }
+
+    pub fn spinodal(
+        eos: &Rc<E>,
+        temperature: QuantityScalar<U>,
+        moles: Option<&QuantityArray1<U>>,
+        options: SolverOptions,
+    ) -> EosResult<[Self; 2]>
+    where
+        QuantityScalar<U>: std::fmt::Display,
+    {
+        let critical_point = Self::critical_point(eos, moles, None, options)?;
+        let moles = eos.validate_moles(moles)?;
+        let spinodal_vapor = Self::calculate_spinodal(
+            eos,
+            temperature,
+            &moles,
+            DensityInitialization::Vapor,
+            options,
+        )?;
+        let rho = 2.0 * critical_point.density - spinodal_vapor.density;
+        let spinodal_liquid = Self::calculate_spinodal(
+            eos,
+            temperature,
+            &moles,
+            DensityInitialization::InitialDensity(rho),
+            options,
+        )?;
+        Ok([spinodal_vapor, spinodal_liquid])
+    }
+
+    fn calculate_spinodal(
+        eos: &Rc<E>,
+        temperature: QuantityScalar<U>,
+        moles: &QuantityArray1<U>,
+        density_initialization: DensityInitialization<U>,
+        options: SolverOptions,
+    ) -> EosResult<Self>
+    where
+        QuantityScalar<U>: std::fmt::Display,
+    {
+        let (max_iter, tol, verbosity) = options.unwrap_or(MAX_ITER_CRIT_POINT, TOL_CRIT_POINT);
+
+        let max_density = eos
+            .max_density(Some(moles))?
+            .to_reduced(U::reference_density())?;
+        let t = temperature.to_reduced(U::reference_temperature())?;
+        let mut rho = match density_initialization {
+            DensityInitialization::Vapor => 1e-5 * max_density,
+            DensityInitialization::Liquid => max_density,
+            DensityInitialization::InitialDensity(rho) => rho.to_reduced(U::reference_density())?,
+            DensityInitialization::None => unreachable!(),
+        };
+        let n = moles.to_reduced(U::reference_moles())?;
+
+        log_iter!(verbosity, " iter |    residual    |       density        ");
+        log_iter!(verbosity, "{:-<46}", "");
+        log_iter!(
+            verbosity,
+            " {:4} |                | {:12.8}",
+            0,
+            rho * U::reference_density(),
+        );
+
+        for i in 1..=max_iter {
+            // calculate residuals and derivative w.r.t. density
+            let res = spinodal_objective(eos, Dual64::from(t), Dual64::from(rho).derive(), &n)?;
+
+            // calculate Newton step
+            let mut delta = res.re / res.eps[0];
+
+            // reduce step if necessary
+            if delta.abs() > 0.03 * max_density {
+                delta *= 0.03 * max_density / delta.abs()
+            }
+
+            // apply step
+            rho -= delta;
+            rho = f64::max(rho, 1e-4 * max_density);
+
+            log_iter!(
+                verbosity,
+                " {:4} | {:14.8e} | {:12.8}",
+                i,
+                res.re.abs(),
+                rho * U::reference_density(),
+            );
+
+            // check convergence
+            if res.re.abs() < tol {
+                log_result!(
+                    verbosity,
+                    "Spinodal calculation converged in {} step(s)\n",
+                    i
+                );
+                return State::new_nvt(
+                    eos,
+                    temperature,
+                    moles.sum() / (rho * U::reference_density()),
+                    moles,
+                );
+            }
+        }
+        Err(EosError::SuperCritical)
+    }
 }
 
-pub fn critical_point_objective<E: EquationOfState>(
+fn critical_point_objective<E: EquationOfState>(
     eos: &Rc<E>,
     temperature: Dual64,
     density: Dual64,
@@ -482,3 +586,28 @@ fn critical_point_objective_p<E: EquationOfState>(
         p.eps[0] * temperature + pressure,
     ]))
 }
+
+fn spinodal_objective<E: EquationOfState>(
+    eos: &Rc<E>,
+    temperature: Dual64,
+    density: Dual64,
+    moles: &Array1<f64>,
+) -> EosResult<Dual64> {
+    // calculate second partial derivatives w.r.t. moles
+    let t = HyperDual::from_re(temperature);
+    let v = HyperDual::from_re(density.recip() * moles.sum());
+    let qij = Array2::from_shape_fn((eos.components(), eos.components()), |(i, j)| {
+        let mut m = moles.mapv(HyperDual::from);
+        m[i].eps1[0] = Dual64::one();
+        m[j].eps2[0] = Dual64::one();
+        let state = StateHD::new(t, v, m);
+        (eos.evaluate_residual(&state).eps1eps2[(0, 0)]
+            + eos.ideal_gas().evaluate(&state).eps1eps2[(0, 0)])
+            * (moles[i] * moles[j]).sqrt()
+    });
+
+    // calculate smallest eigenvalue of q
+    let (eval, _) = smallest_ev(qij);
+
+    Ok(eval)
+}