General
*******


Introduction
============

The ADF COSMO-RS (COnductor like Screening MOdel for Realistic
Solvents) program is a program that can be used for calculating
thermodynamic properties of (mixed) fluids. The COSMO-RS method was
developed by Klamt and coworkers [1] [2] [3]. On the basis of the
framework of COSMO-RS, Lin and Sandler [6] suggested a variation, the
COSMO-SAC (where SAC denotes segment activity coefficient) model.
There are different implementations of COSMO-RS and COSMO-SAC or
derivatives, and different parametrizations. The implementation of
COSMO-RS in ADF is described in Ref. [4], which is based on the COSMO-
RS method developed by Klamt et al. [2]. The implementation of COSMO-
SAC 2013-ADF in ADF is based on the COSMO-SAC model developed by Xiong
et al. [7]. The implementation of COSMO-SAC 2016-ADF in ADF is based
on the COSMO-SAC model developed by Hsieh et al. [5], but the
parameters in COSMO-SAC 2016-ADF were optimized by Chen et al., like
in [8], for use with ADF. The implementation of COSMO-SAC DHB-ADF in
ADF is based on the COSMO-SAC-DHB model developed by Chen et al. [9],
but the parameters were reoptimized by Chen et al. for use with ADF.
The implementation of COSMO-SAC DHB-ADF MESP is based on the model
developed by Chang et al. [11].

An alternative to COSMO-SAC or COSMO-RS based methods is the UNIFAC
(UNIQUAC Functional-group Activity Coefficients) method, which was
developed by Fredenslund, Jones, and Prausnitz, see [10]. The
empirical UNIFAC method is a group contribution based method to
predict activity coefficients and other thermodynamic properties, in
which the group specific parameters and are parametrized against a
large data base. The implementation in ADF uses to so called original
UNIFAC parameters.

Our COSMO-RS capabilities are summarized on the product page.

With COSMO-RS it is possible to use a thermodynamically consistent
combinatorial contribution to the chemical potential as is used in
Ref. [3], and a temperature dependent hydrogen bond interaction, also
described in Ref. [3]. The parameters in the paper [2] were
reparametrized for ADF, see Ref. [4] for details.

The parameters in COSMO-SAC 2013-ADF, COSMO-SAC 2016-ADF, COSMO-SAC
DHB-ADF and COSMO-SAC DHB-ADF MESP were optimized for use with ADF
COSMO result files. Other COSMO-SAC parameter sets exists that were
optimized for different QM packages.

The ADF COSMO-RS (and COSMO-SAC) command line program is called *crs*.
The main authors of this program are Cory Pye (Saint Mary's
University, Halifax NS Canada) and Jaap Louwen (Albemarle
Corporation). COSMO-SAC 2013-ADF was implemented in collaboration with
R. Xiong and R.I. Burnett (Sandler group, University of Delaware,
Newark, USA). Previous COSMO-SAC methods were implemented by Erin
McGarrity (TU Delft, the Netherlands). The COSMO-RS GUI *AMScrs*
contains an input builder for COSMO-RS and can visualize results, see
the  COSMO-RS GUI tutorials and the COSMO-RS GUI reference manual.

COSMO-RS (and COSMO-SAC)  use the intermediate results from quantum
mechanical (QM) calculations on individual molecules to predict
thermodynamic properties of mixtures of these molecules, for example,
solubility. There are a fair number of reports of accurate prediction
by COSMO-RS of thermodynamic properties in general in the literature.
Many of these have been written by Klamt and co-workers, see Ref. [3]
and references therein. Instead of a relatively expensive QM
calculation one can use a fast Quantitative Structure-Property
Relationship (QSPR) method to estimate the so called COSMO sigma-
profile of a molecule that is needed in COSMO-RS (and COSMO-SAC)
calculations.

There are also empirical methods like UNIFAC that can predict
thermodynamic properties (including the activity coefficients). These
methods contain group specific parameters and are parametrized against
a large data base. They will often do better than COSMO-RS or COSMO-
SAC methods (especially, of course, if the system of interest was part
of the data base used for parameter estimation). An advantage of these
methods is that they require no QM calculations to be done in order to
provide an estimate of thermodynamics properties. However, these
methods cannot handle every type of molecule. In particular when
unusual combinations of functional groups occur (such as in drug
molecules), no parametrization is available. COSMO-RS and COSMO-SAC
methods, on the other hand, only feature general parameters not
specific to chemical groups or functionalities. All that is required
is that a quantum mechanical calculation can be done on the molecule.
Therefore, COSMO-RS or COSMO-SAC can be a valuable tool for the
prediction of chemical engineering thermodynamical properties, like,
for example, partial vapor pressures, solubilities, and partition
coefficients. An additional advantage of COSMO-RS and COSMO-SAC over
empirical methods is that the molecules dissolved may in fact be
transition states of a chemical reaction. This follows from the fact
that all that is required is that one can do a QM calculation on the
solute and QM on a transition state has become standard in the last
two decades. This affords a unique opportunity to predict the
thermodynamics of a reaction including, for instance, the balance
between kinetically and thermodynamically favored reaction pathways as
a function of the solvent used.


What's new in COSMO-RS 2026.1
=============================

* Michelsen Tangent Plane Distance (TPD) stability test is implemented
  for multicomponent systems.

* Liquid-Liquid Equilibrium is implemented for multicomponent systems.

* Reuse Sigma Profile is implemented to reduce computational cost.


What's new in COSMO-RS 2025.1
=============================

* COSMO-SAC DHB MESP is implemented, with the hydrogen bond center
  determined from the local minima of the molecular electrostatic
  potential


What's new in COSMO-RS 2024.1
=============================

* A new Database module in pyCRS designed for the management of coskf
  files

* A new CRSManager module in pyCRS facilitates a streamlined scripting
  process for the creation and execution of CRSJob in conjunction with
  the Database module


What's new in COSMO-RS 2023.1
=============================

* pyCRS is a new python module that provides an interface to the pure
  compound property prediction and sigma profile prediction tools

* SG1: a new sigma profile prediction method that relies on a database
  of pre-computed molecular subgraphs

* A new, temperature-dependent pure compound liquid viscosity model

* A synthetic accessibility estimation method -- see the Property
  prediction documentation for more details.

* Improved convergence criteria that make multispecies calculations
  more stable


What's new in COSMO-RS 2022.1
=============================

* Pitzer-Debye-Hückel long-range electrostatic correction


What's new in COSMO-RS 2021.1
=============================

* Improved handling of compounds with multi-species components


What's new in COSMO-RS 2020.1
=============================

* Support for compounds with multi-species components

  * conformers

  * dimers, trimers, ...

  * dissociation

  * association

[1] A. Klamt,  *Conductor-like Screening Model for Real Solvents: A
    New Approach to the Quantitative Calculation of Solvation
    Phenomena.*  J. Phys. Chem. 99, 2224 (1995)

[2] A. Klamt, V. Jonas, T. Bürger and J.C. Lohrenz,  *Refinement and
    Parametrization of COSMO-RS.*  J. Phys. Chem. A 102, 5074 (1998)

[3] A. Klamt,  *COSMO-RS From Quantum Chemistry to Fluid Phase
    Thermodynamics and Drug Design, Elsevier.* Amsterdam (2005), ISBN
    0-444-51994-7.

[4] C.C. Pye, T. Ziegler, E. van Lenthe, J.N. Louwen,  *An
    implementation of the conductor-like screening model of solvation
    within the Amsterdam density functional package. Part II. COSMO
    for real solvents.*  Can. J. Chem. 87, 790 (2009)

[5] C.M. Hsieh, S.I. Sandler, S.T. Lin, *Improvements of COSMO-SAC for
    vapor-liquid and liquid-liquid equilibrium predictions*,  Fluid
    Phase Equilib. 297, 90 (2010)

[6] S.T. Lin and S.I. Sandler, *A Priori Phase Equilibrium Prediction
    from a Segment Contribution Solvation Model*,  Ind. Eng. Chem.
    Res. 41, 899 (2002)

[7] R. Xiong, S.I. Sandler, R.I. Burnett, *An improvement to COSMO-SAC
    for predicting thermodynamic properties*,  Ind. Eng. Chem. Res.
    53, 8265 (2014)

[8] W.L. Chen, C.M. Hsieh, L. Yang, C.C. Hsu, S.T. Lin, *A Critical
    Evaluation on the Performance of COSMO-SAC Models for Vapor-Liquid
    and Liquid-Liquid Equilibrium Predictions Based on Different
    Quantum Chemical Calculations*,  Ind. Eng. Chem. Res. 55, 9312
    (2016)

[9] W.L. Chen, S.T. Lin, *Explicit consideration of spatial hydrogen
    bonding direction for activity coefficient prediction based on
    implicit solvation calculations*,  Phys.Chem.Chem.Phys. 19, 20367
    (2017)

[10] A. Fredenslund, R.L. Jones, and J.M. Prausnitz, *Group-
     contribution estimation of activity coefficients in nonideal
     liquid mixtures*, AIChE Journal 21, 1086 (1975)

[11] C.C. Chang, W.L. Chen, D.T. Wu, S.T. Lin , *Improved directional
     hydrogen bonding interactions for the prediction of activity
     coefficients with COSMO-SAC*,  Ind. Eng. Chem. Res. 57, 11229
     (2018)
