Condensed-phase molecular representation to link structure and thermodynamics in molecular dynamics
arXiv:2302.09909 · doi:10.1021/acs.jctc.3c00201
Abstract
Molecular design requires systematic and broadly applicable methods to extract structure-property relationships. The focus of this study is on learning thermodynamic properties from molecular-liquid simulations. The methodology relies on an atomic representation originally developed for electronic properties: the Spectrum of London and Axilrod-Teller-Muto representation (SLATM). SLATM's expansion in one-, two-, and three-body interactions makes it amenable to probing structural ordering in molecular liquids. We show that such representation encodes enough critical information to permit the learning of thermodynamic properties via linear methods. We demonstrate our approach on the preferential insertion of small solute molecules toward cardiolipin membranes and monitor selectivity against a similar lipid. Our analysis reveals simple, interpretable relationships between two- and three-body interactions and selectivity, identifies key interactions to build optimal prototypical solutes, and charts a two-dimensional projection that displays clearly separated basins. The methodology is generally applicable to a variety of thermodynamic properties.
References in corpus (4)
- Identifying structural flow defects in disordered solids using machine learning methods
- Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems
- Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation
- Hydration free energies from kernel-based machine learning: Compound-database bias