Reference : Non-covalent interactions across organic and biological subsets of chemical space: Ph...
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Physics
Physics and Materials Science; Computational Sciences
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
Bereau, Tristan []
Distasio Jr., Robert A. []
Tkatchenko, Alexandre mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Physics and Materials Science Research Unit >]
von Lilienfeld, Anatole []
Journal of Chemical Physics
American Institute of Physics
Yes (verified by ORBilu)
New York
[en] Classical intermolecular potentials typically require an extensive parametrization procedure for any
new compound considered. To do away with prior parametrization, we propose a combination of
physics-based potentials with machine learning (ML), coined IPML, which is transferable across
small neutral organic and biologically relevant molecules. ML models provide on-the-fly predictions
for environment-dependent local atomic properties: electrostatic multipole coefficients (significant
error reduction compared to previously reported), the population and decay rate of valence atomic
densities, and polarizabilities across conformations and chemical compositions of H, C, N, and O
atoms. These parameters enable accurate calculations of intermolecular contributions—electrostatics,
charge penetration, repulsion, induction/polarization, and many-body dispersion. Unlike other potentials,
this model is transferable in its ability to handle new molecules and conformations without
explicit prior parametrization: All local atomic properties are predicted from ML, leaving only eight
global parameters—optimized once and for all across compounds.We validate IPML on various gasphase
dimers at and away from equilibrium separation, where we obtain mean absolute errors between
0.4 and 0.7 kcal/mol for several chemically and conformationally diverse datasets representative of
non-covalent interactions in biologically relevant molecules. We further focus on hydrogen-bonded
complexes—essential but challenging due to their directional nature—where datasets of DNA base
pairs and amino acids yield an extremely encouraging 1.4 kcal/mol error. Finally, and as a first look,
we consider IPML for denser systems: water clusters, supramolecular host-guest complexes, and the
benzene crystal.

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