Jul 2026· Journal of Chemical Theory and Computation· Vol 22, pp. 7264-7273· 0 citations· 26 references
Medicine
Abstract
The convergence of molecular dynamics simulations and machine-learned interatomic potentials (MLIPs) promises density functional theory (DFT) level accuracy at near-classical force-field computational costs. However, while the average fidelity to reference energies and forces approaches perfection, several failure modes limit MLIP reliability in production simulations. These include spurious bond formation, inconsistent reproduction of long-range interactions, and inconsistent spin-state references. Here, the origins of these behaviors are studied by benchmarking the UMA, ORB, MACE, and AIMNet2 models against reference DFT bond dissociation curves for an illustrative range of species. These benchmarks reveal that models without explicit atomic charge resolution predict spurious stable bonds between like-charged halide anions, effectively transmuting two Cl- ions into neutral Cl2. Models with atomic partial charge equilibration correctly predict repulsion in these systems. Conversely, several secondary limitations are exposed in these benchmarks, including inconsistent agreement with unrestricted DFT (uDFT) versus restricted DFT (rDFT) energies and inconsistent core-region treatment. This comparative analysis suggests that, while artifacts related to core repulsion and asymptotic electrostatics are readily repairable through improved physical priors and better data curation, the issue of spurious bond formation is intrinsic to the inability of global charge specification to disambiguate similar local geometries at different charge and spin states.
Accurate treatment of long-range interactions in machine learning interatomic potentials (MLIPs) is essential for electrochemical simulations. However, aggregate energy and force errors alone are insufficient to establish an MLIP's physical accuracy since they do not detect qualitative inconsistencies in the model such as the prediction of image-charge attraction, dielectric screening, or charge transfer. We introduce a benchmark suite EPhEct (Electrostatic Phenomena for Electrochemistry) of focused test cases designed to evaluate MLIPs on electrochemically relevant physical phenomena. The tests probe for image-charge attraction at a metal electrode, the splitting between longitudinal and transverse optical phonons as a probe of ionic and electronic screening, the dipole moment of interfacial water, and Fermi-level pinning during ion discharge. These tests establish a qualitative diagnostic routine complementary to aggregate energy-force metrics.
Barbara Sumić, Ria Vasdev, S. Ethirajan et al.· 0 citations
This work employs a dual-coordinate approach where both solutes are explicitly present but do not interact with one another, and utilizes a harmonic ″anchor″ restraint to a central atom on each molecule to enforce spatial overlap without modifying the internal intramolecular dynamics of either solute.
Anna Katharina Picha, S. Boresch· Journal of Chemical Theory a...· 1 citation
This review aims to provide a comprehensive perspective on the ongoing transition from conventional DFT-based simulations toward scalable, statistically rigorous, and predictive atomistic modeling frameworks for HEAs and related compositionally complex materials.
Yuji Ikeda, Xiang Xu, Pranav Kumar et al.· Journal of Materials Science· 0 citations
A semiparametric interatomic potential is introduced based on a generalization of the Abell--Tersoff bond-order potential, incorporating a chemically informed functional form and explicit high-order many-body correlations to address this challenge of reliability for out-of-distribution configurations far beyond the training domain.
Accurate benchmarking of intermolecular interaction energies is central to evaluating quantum chemical methods and guiding the development of reliable machine-learned interatomic potentials (MLIPs). We benchmark five MLIPs (AIMNet2(2023), AIMNet2(2025), MACE-OFF23(M), MACE-OMol, and UMA-S-OMol) across twenty-one datasets spanning hydrogen-bonded, dispersion- and pi-dominated, sigma-hole, ionic and charge transfer, and repulsive nonequilibrium interactions, with reference values at or near CCSD(T)/CBS accuracy. AIMNet2(2025) is a continually pretrained variant of AIMNet2(2023) that retains the original architecture but incorporates 3.8 million additional structures curated to improve noncovalent interactions (NCIs). AIMNet2(2025) improves on its predecessor across nearly all benchmark categories, with the largest gains in the hydrogen-bonded, sigma-hole, and repulsive regimes, while remaining competitive with the much larger MACE-OMol and UMA-S-OMol. The supramolecular S12L and L7 benchmarks show only marginal improvement: every evaluated MLIP exhibits large errors driven by a small number of pathological complexes. Two factors beyond intrinsic model quality significantly influence the reported performance. First, partial overlap between training and benchmark data, quantified here via systematic overlap detection, inflates apparent accuracy for all models, most strongly for those trained on OMol25. Second, differences in the DFT reference level used for MLIP training establish irreducible error floors, so superior benchmark performance may partly reflect closer proximity of the training functional to the CCSD(T)/CBS reference rather than stronger modeling capability. Sigma-hole interactions emerge as the category with the lowest training-benchmark overlap across all models and therefore provide the most discriminating test of true generalization. Meaningful MLIP evaluation must account for data provenance, reference theory consistency, and the distinction between interpolation and out-of-distribution generalization, particularly as standard NCI benchmark sets become absorbed into large-scale training datasets.
Methanol-water mixtures find use in many applications, particularly catalytic energy conversion processes. Their importance has motivated numerous computational studies, most of which employed molecular dynamics based on classical force fields. These enable simulations of large systems on long time scales but do not reliably describe reactive dynamics involving bond breaking and bond formation. In contrast, ab initio molecular dynamics (AIMD) based on density functional theory (DFT) is generally more reliable for such applications but has a high computational cost, which discourages systematic studies of alcohol-water mixtures. To remedy this, we trained a machine learning interatomic potential capable of probing the properties of aqueous methanol mixtures at the DFT level using the SCAN functional. Our results show that SCAN qualitatively reproduces multiple key experimental features arising from the amphiphilic nature of methanol, including density, diffusion coefficients, X-ray structure factors, and Kirkwood-Buff integrals. We also find that structural correlations between water molecules are somewhat overestimated, leading to a stronger preferential association than that predicted by experiments. However, increasing the temperature by 30 K mitigates this effect and also recovers the correct mobilities of both methanol and water. These results indicate that SCAN provides an accurate description of methanol-water mixtures, making it a reliable choice for investigating the reactive dynamics in such systems.
Sanghyun J. Park, A. Selloni· Journal of Physical Chemistr...· 0 citations
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