arXiv subjects

Valerii Andreichev

Publications and source records attributed to Valerii Andreichev.

6 recordsLinked to original sources

arXiv2026

Fidelity of Machine Learned Potentials: Quantitative Assessment for Protonated Oxalate

There has been a veritable explosion of methods and software to perform machine-learned regression on datasets of electronic energies and forces to develop high-dimensional machine learned potential energy surfaces (ML-PESs). A major, but not deeply-studied aspect is how well different ML-PESs represent the same dataset on which they are trained, beyond the standard fitting precision metrics. Here, this is examined in detail using several ''stress tests'', for two widely applied machine-learned potential approaches. One is based on permutationally invariant polynomial (PIP) linear least square regression and the other is the message-passing neural network PhysNet approach. These potentials and dipole moment surfaces are used in VSCF/VCI calculations of vibrational energies and wavefunctions. The energies from the two PESs are directly compared as are the IR spectra. In addition, tunneling splittings for the hydrogen transfer between two equivalent structures are reported from using three methods: ring polymer instanton theory, diffusion Monte Carlo simulations, and the $Q_{im}$ path method. These calculations require the evaluation of on the order of one billion energies that are widely dispersed in the 15-dimensional configurational space. The two PESs yield results for these quantities in excellent agreement with each other.

physics.chem-ph↗

arXiv2026

High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics

Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of this workflow without any loss of accuracy. We discuss various methods for constructing potential energy surfaces including transfer learning, which requires a minimal number of expensive training points. In this way, we can study chemical reactions at a high level but a low cost. In particular, as the potentials are smooth and differentiable, they enable the use of more advanced semiclassical approximations to quantum dynamics, such as perturbatively corrected instanton theory, which can capture both tunnelling and anharmonicity.

physics.chem-ph↗

arXiv2025

Design, Assessment, and Application of Machine Learning Potential Energy Surfaces

Potential Energy Surfaces (PESs) are an indispensable tool to investigate, characterise and understand chemical and biological systems in the gas and condensed phases. Advances in Machine Learning (ML) methodologies have led to the development of Machine Learned Potential Energy Surfaces (ML-PES) which are now widely used to simulate such systems. The present work provides an overview of concepts, methodologies and recommendations for constructing and using ML-PESs. The choice of topics is focused on practical and recurrent issues to conceive and use such model. Application of the principles discussed are illustrated through two different systems of biomolecular importance: the non-reactive dynamics of the Alanine-Lysine-Alanine tripeptide in gas and solution phases, and double proton transfer reactions in DNA base pairs.

physics.chem-ph↗

arXiv2025

Tripeptide-Dynamics from Empirical and Machine-Learned Energy Functions

Molecular dynamics simulations for tripeptides in the gas phase and in solution using empirical and machine-learned energy functions are presented. For cationic AAA a machine-learned potential energy surface (ML-PES) trained on MP2 reference data yields quantitative agreement with measured splittings of the amide-I vibrations. Experimental spectroscopy in solution reports a splitting of 25 cm-1 which compares with 20 cm-1 from ML/MM-MD simulations of AAA in explicit solvent. For the AMA tripeptide a ML-PES describing both, the zwitterionic and neutral form is trained and used to map out the accessible conformational space. Due to cyclization and H-bonding between the termini in neutral AMA the NH- and OH-stretch spectra are strongly red-shifted below 3000 cm-1. The present work demonstrates that meaningful MD simulations on the nanosecond time scale are feasible and provides insight into experiments.

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↗

arXiv2025

Dynamics of Protonated Oxalate from Machine-Learned Simulations and Experiment: Infrared Signatures, Proton Transfer Dynamics and Tunneling Splittings

The infrared spectroscopy and proton transfer dynamics together with the associated tunneling splittings for H/D-transfer in oxalate are investigated using a machine learning-based potential energy surface (PES) of CCSD(T) quality, calibrated against the results of new spectroscopic measurements. Second order vibrational perturbation calculations (VPT2) very successfully describe both the framework and H-transfer modes compared with the experiments. In particular, a new low-intensity signature at 1666 cm$^{-1}$ was correctly predicted from the VPT2 calculations. An unstructured band centered at 2940 cm$^{-1}$ superimposed on a broad background extending from 2600 to 3200 cm$^{-1}$ is assigned to the H-transfer motion. The broad background involves a multitude of combination bands but a major role is played by the COH-bend. For the deuterated species, VPT2 and molecular dynamics simulations provide equally convincing assignments, in particular for the framework modes. Finally, based on the new PES the tunneling splitting for H-transfer is predicted as $Δ_{\rm H} = 35.0$ cm$^{-1}$ from ring polymer instanton calculations using higher-order corrections. This provides an experimentally accessible benchmark to validate the computations, in particular the quality of the machine-learned PES.

physics.chem-ph↗