Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials

The Journal of Chemical Physics, Год журнала: 2024, Номер 160(17)

Опубликована: Май 1, 2024

As the most important solvent, water has been at center of interest since advent computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use simple model potentials describe atomic interactions, accurate ab initio relying on first-principles calculation energies forces have opened way predictive aqueous systems. Still, these are very demanding, which prevents study complex systems their properties. Modern machine learning (MLPs) now reached a mature state, allowing us overcome limitations by combining high accuracy electronic structure calculations with efficiency empirical force fields. In this Perspective, we give concise overview about progress made in simulation employing MLPs, starting from work free molecules clusters via bulk liquid electrolyte solutions solid–liquid interfaces.

Язык: Английский

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Random Sampling Versus Active Learning Algorithms for Machine Learning Potentials of Quantum Liquid Water

Journal of Chemical Theory and Computation, Год журнала: 2025, Номер unknown

Опубликована: Янв. 14, 2025

Training accurate machine learning potentials requires electronic structure data comprehensively covering the configurational space of system interest. As construction this is computationally demanding, many schemes for identifying most important structures have been proposed. Here, we compare performance high-dimensional neural network (HDNNPs) quantum liquid water at ambient conditions trained to sets constructed using random sampling as well various flavors active based on query by committee. Contrary common understanding learning, find that a given set size, leads smaller test errors not included in training process. In our analysis, show can be related small energy offsets caused bias added which overcome instead correlations an error measure invariant such shifts. Still, all HDNNPs yield very similar and structural properties water, demonstrates robustness procedure with respect algorithm even when few 200 structures. However, preliminary potentials, reasonable initial avoid unnecessary extension covered configuration less relevant regions.

Язык: Английский

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Machine Learning Potentials for Heterogeneous Catalysis

ACS Catalysis, Год журнала: 2025, Номер 15(3), С. 1616 - 1634

Опубликована: Янв. 15, 2025

The production of many bulk chemicals relies on heterogeneous catalysis. rational design or improvement the required catalysts critically depends insights into underlying mechanisms atomic scale. In recent years, substantial progress has been made in applying advanced experimental techniques to complex catalytic reactions operando, but order achieve a comprehensive understanding, additional information from computer simulations is indispensable cases. particular, ab initio molecular dynamics (AIMD) become an important tool explicitly address atomistic level structure, dynamics, and reactivity interfacial systems, high computational costs limit applications systems consisting at most few hundred atoms for simulation times up tens picoseconds. Rapid advances development modern machine learning potentials (MLP) now offer promising approach bridge this gap, enabling with accuracy small fraction costs. Perspective, we provide overview current state art MLPs relevant catalysis along discussion prospects use science years come.

Язык: Английский

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The Potential of Neural Network Potentials

ACS Physical Chemistry Au, Год журнала: 2024, Номер 4(3), С. 232 - 241

Опубликована: Март 21, 2024

In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by combination of recent advances in quantum and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are breakthrough new tool that already enabling us to simulate systems at molecular scale with unprecedented accuracy speed, relying on nothing but fundamental laws. The continued development this approach realize Paul Dirac's 80-year-old vision using mechanics unify physics providing invaluable tools for understanding materials science, biology, earth sciences, beyond. era highly accurate efficient first-principles simulations provide wealth training data can be used build automated computational methodologies, such as diffusion models, design optimization scale. Large language models (LLMs) also evolve into increasingly indispensable literature review, coding, idea generation, scientific writing.

Язык: Английский

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Machine learning-aided first-principles calculations of redox potentials

npj Computational Materials, Год журнала: 2024, Номер 10(1)

Опубликована: Май 20, 2024

Abstract We present a method combining first-principles calculations and machine learning to predict the redox potentials of half-cell reactions on absolute scale. By applying force fields for thermodynamic integration from oxidized reduced state, we achieve efficient statistical sampling over broad phase space. Furthermore, through semi-local functionals, functionals hybrid using Δ-machine learning, refine free energy with high precision step-by-step. Utilizing functional that includes 25% exact exchange (PBE0), this predicts three couples, Fe 3+ /Fe 2+ , Cu /Cu + Ag /Ag be 0.92, 0.26, 1.99 V, respectively. These predictions are in good agreement best experimental estimates (0.77, 0.15, 1.98 V). This work demonstrates machine-learned surrogate models provide flexible framework refining accuracy coarse approximation methods precise electronic structure calculations, while also facilitating sufficient sampling.

Язык: Английский

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Density isobar of water and melting temperature of ice: Assessing common density functionals

The Journal of Chemical Physics, Год журнала: 2024, Номер 161(13)

Опубликована: Окт. 3, 2024

We investigate the density isobar of water and melting temperature ice using six different functionals. Machine-learning potentials are employed to ensure computational affordability. Our findings reveal significant discrepancies between various base Notably, even choice damping can result in substantial differences. Overall, outcomes obtained through functional theory not entirely satisfactory across most utilized All functionals exhibit deviations either or equilibrium volume, with them predicting an incorrect volume difference water. heuristic analysis indicates that a hybrid 25% exact exchange van der Waals averaged zero Becke–Johnson dampings yields closest agreement experimental data. This study underscores necessity for further enhancements treatment interactions and, more broadly, enable accurate quantitative predictions molecular liquids.

Язык: Английский

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Unsupervised identification of crystal defects from atomistic potential descriptors

npj Computational Materials, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 27, 2025

Язык: Английский

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Applications of machine learning in surfaces and interfaces

Chemical Physics Reviews, Год журнала: 2025, Номер 6(1)

Опубликована: Март 1, 2025

Surfaces and interfaces play key roles in chemical material science. Understanding physical processes at complex surfaces is a challenging task. Machine learning provides powerful tool to help analyze accelerate simulations. This comprehensive review affords an overview of the applications machine study systems materials. We categorize into following broad categories: solid–solid interface, solid–liquid liquid–liquid surface solid, liquid, three-phase interfaces. High-throughput screening, combined first-principles calculations, force field accelerated molecular dynamics simulations are used rational design such as all-solid-state batteries, solar cells, heterogeneous catalysis. detailed information on for

Язык: Английский

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Transferability of the chemical-bond-based machine learning model for dipole moment: The GHz to THz dielectric properties of liquid propylene glycol and polypropylene glycol

Physical review. B./Physical review. B, Год журнала: 2025, Номер 111(16)

Опубликована: Апрель 24, 2025

Язык: Английский

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Derivative learning of tensorial quantities—Predicting finite temperature infrared spectra from first principles

The Journal of Chemical Physics, Год журнала: 2024, Номер 161(8)

Опубликована: Авг. 22, 2024

We develop a strategy that integrates machine learning and first-principles calculations to achieve technically accurate predictions of infrared spectra. In particular, the methodology allows one predict spectra for complex systems at finite temperatures. The method’s effectiveness is demonstrated in challenging scenarios, such as analysis water organic–inorganic halide perovskite MAPbI3, where our results consistently align with experimental data. A distinctive feature incorporation derivative learning, which proves indispensable obtaining polarization data bulk materials facilitates training surrogate model adapted rotational translational symmetries. prediction accuracies about 1% dimer by only on predicted Born effective charges.

Язык: Английский

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