arXiv subjects

Kham Lek Chaton

Publications and source records attributed to Kham Lek Chaton.

3 recordsLinked to original sources

arXiv2026

Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations

A new pairwise hybrid machine-learning/molecular mechanics (ML/MM) potential is introduced that is conceived for application to large, heterogeneous condensed-phase systems. The PhysNet ML method describes monomers and short-range dimer interactions, while a classical MM force field describes pairwise interactions beyond a defined switching distance. Models are fitted to MP2 dimer and pairwise cluster energies, and the quality of each model is assessed at different switching distances and using MM approaches with and without detailed distributed charge electrostatics. The applicability of the approach to molecular dynamics simulations is demonstrated for a basic implementation applied to a small model system. Dichloromethane and acetone are used as test systems to demonstrate the accuracy of the approach in describing pairwise reference data, and also to highlight the limitations of the pairwise approach for systems that exhibit significant many-body effects in condensed phase, paving the way for the addition of a general many-body correction in future work.

physics.chem-ph↗

arXiv2025

Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations

Energy functions for pure and heterogenous systems are one of the backbones for molecular simulation of condensed phase systems. With the advent of machine learned potential energy surfaces (ML-PESs) a new era has started. Statistical models allow the representation of reference data from electronic structure calculations for chemical systems of almost arbitrary complexity at unprecedented detail and accuracy. Here, kernel- and neural network-based approaches for intramolecular degrees of freedom are combined with distributed charge models for long range electrostatics to describe the interaction energies of condensed phase systems. The main focus is on illustrative examples ranging from pure liquids (dichloromethane, water) to chemically and structurally heterogeneous systems (eutectic liquids, CO on amorphous solid water), reactions (Menshutkin), and spectroscopy (triatomic probes for protein dynamics). For all examples, small to medium-sized clusters are used to represent and improve the total interaction energy compared with reference quantum chemical calculations. Although remarkable accuracy can be achieved for some systems (chemical accuracy for dichloromethane and water), it is clear that more realistic models are required for van der Waals contributions and improved water models need to be used for more quantitative simulations of heterogeneous chemical and biological systems.

physics.chem-ph↗

arXiv2024

Machine Learning-Based Enhancements of Empirical Energy Functions: Structure, Dynamics and Spectroscopy of Modified Benzenes

The effect of replacing individual contributions to an empirical energy function are assessed for halogenated benzenes (X-Bz, X = H, F, Cl, Br) and chlorinated phenols (Cl-PhOH). Introducing electrostatic models based on distributed charges (MDCM) instead of usual atom-centered point charges yields overestimated hydration free energies unless the van der Waals parameters are reparametrized. Scaling van der Waals ranges by 10 \% to 20 \% for three Cl-PhOH and most X-Bz yield results within experimental error bars, which is encouraging, whereas for benzene (H-Bz) point charge-based models are sufficient. Replacing the bonded terms by a neural network-trained energy function with either fluctuating charges or MDCM electrostatics also yields qualitatively correct hydration free energies which still require adaptation of the van der Waals parameters. The infrared spectroscopy of Cl-PhOH is rather well predicted by all models although the ML-based energy function performs somewhat better in the region of the framework modes. It is concluded that refinements of empirical energy functions for targeted applications is a meaningful way towards more quantitative simulations.

physics.chem-ph↗