Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations
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arXiv
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2025
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| author | Wang, JingChun Upadhyay, Meenu Boittier, Eric D. Chaton, Kham Lek Andreichev, Valerii Devereux, Mike Patel, Shimoni Aydin, Sena Töpfer, Kai Meuwly, Markus |
| author_facet | Wang, JingChun Upadhyay, Meenu Boittier, Eric D. Chaton, Kham Lek Andreichev, Valerii Devereux, Mike Patel, Shimoni Aydin, Sena Töpfer, Kai Meuwly, Markus |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_11639 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations Wang, JingChun Upadhyay, Meenu Boittier, Eric D. Chaton, Kham Lek Andreichev, Valerii Devereux, Mike Patel, Shimoni Aydin, Sena Töpfer, Kai Meuwly, Markus Chemical Physics 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. |
| title | Cluster Models for Next-Generation, Machine-Learning-Based Energy Functions for Molecular Simulations |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2509.11639 |