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

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

doi:10.1021/acs.iecr.2c01163

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

Journal Article · Mon Jun 13 00:00:00 EDT 2022 · Industrial and Engineering Chemistry Research

DOI:https://doi.org/10.1021/acs.iecr.2c01163· OSTI ID:1876166

Li, Jianping ^[1]; ^[2]; ^[3]

Univ. of Wisconsin, Madison, WI (United States); University of Wisconsin-Madison Department of Chemical and Biological Engineering
Princeton Univ., NJ (United States)
Univ. of Wisconsin, Madison, WI (United States)

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. Here, 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.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Wisconsin, Madison, WI (United States). Great Lakes Bioenergy Research Center

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0018409

OSTI ID:: 1876166

Journal Information:: Industrial and Engineering Chemistry Research, Journal Name: Industrial and Engineering Chemistry Research Journal Issue: 25 Vol. 61; ISSN 0888-5885

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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