Adaptive Conformer Sampling for Property Prediction Using the Conductor-like Screening Model for Real Solvents

Jianping Li, Christos T. Maravelias, Reid C. Van Lehn

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The valorization of lignocellulose-derived bioproducts requires effective separation from excessive water. Liquid-liquid extraction is a promising low-energy separation technology, but effective extraction requires solvent selection based on the thermodynamic properties of the bioproduct and solvent components. We propose a computational framework for predicting such properties by developing an adaptive conformer selection approach for use with COSMO-RS (conductor-like screening model for real solvents) calculations. In this framework, molecular dynamics simulations are used to generate many molecular structures (conformers) at representative temperatures in varying solvent environments. Conformers are then clustered based on structural metrics in a low-dimensional space and selected using a mixed-integer quadratic programming problem to iteratively insert a sampled conformer. At each iteration, we determine bioproduct properties using COSMO-RS. We demonstrate the capability of the proposed framework on representative bioproducts to show convergence of the adaptive sampling toward experimentally measured properties with fewer calculations than required by random conformer sampling, enabling the improved screening of solvent systems for liquid-phase separation.

Original languageEnglish (US)
Pages (from-to)9025-9036
Number of pages12
JournalIndustrial and Engineering Chemistry Research
Issue number25
StatePublished - Jun 29 2022

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Chemical Engineering
  • Industrial and Manufacturing Engineering


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