Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 2, 2024

Abstract Silicon–oxygen compounds are among the most important ones in natural sciences, occurring as building blocks minerals and being used semiconductors catalysis. Beyond well-known silicon dioxide, there phases with different stoichiometric composition nanostructured composites. One of key challenges understanding Si–O system is therefore to accurately account for its nanoscale heterogeneity beyond length scale individual atoms. Here we show that a unified computational description full indeed possible, based on atomistic machine learning coupled an active-learning workflow. We showcase applications very-high-pressure silica, surfaces aerogels, structure amorphous monoxide. In wider context, our work illustrates how structural complexity functional materials atomic few-nanometre scales can be captured active learning.

Language: Английский

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MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows

Journal of Chemical Theory and Computation, Journal Year: 2024, Volume and Issue: 20(3), P. 1193 - 1213

Published: Jan. 25, 2024

Machine learning (ML) is increasingly becoming a common tool in computational chemistry. At the same time, rapid development of ML methods requires flexible software framework for designing custom workflows. MLatom 3 program package designed to leverage power enhance typical chemistry simulations and create complex This open-source provides plenty choice users who can run with command-line options, input files, or scripts using as Python package, both on their computers online XACS cloud computing service at XACScloud.com. Computational chemists calculate energies thermochemical properties, optimize geometries, molecular quantum dynamics, simulate (ro)vibrational, one-photon UV/vis absorption, two-photon absorption spectra ML, mechanical, combined models. The choose from an extensive library containing pretrained models mechanical approximations such AIQM1 approaching coupled-cluster accuracy. developers build own various algorithms. great flexibility largely due use interfaces many state-of-the-art packages libraries.

Language: Английский

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Machine learned force-fields for an Ab-initio quality description of metal-organic frameworks

npj Computational Materials, Journal Year: 2024, Volume and Issue: 10(1)

Published: Jan. 20, 2024

Language: Английский

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Decoding Electrochemical Processes of Lithium‐Ion Batteries by Classical Molecular Dynamics Simulations

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: 14(22)

Published: March 19, 2024

Abstract Lithium‐ion batteries (LIBs) have played an essential role in the energy storage industry and dominated power sources for consumer electronics electric vehicles. Understanding electrochemistry of LIBs at molecular scale is significant improving their performance, stability, lifetime, safety. Classical dynamics (MD) simulations could directly capture atomic motions thus provide dynamic insights into electrochemical processes ion transport during charging discharging that are usually challenging to observe experimentally, which momentous developing with superb performance. This review discusses developments MD approaches using non‐reactive force fields, reactive machine learning potential modeling chemical reactions reactants electrodes, electrolytes, electrode‐electrolyte interfaces. It also comprehensively how interactions, structures, transport, reaction affect electrode capacity, interfacial properties. Finally, remaining challenges envisioned future routes commented on high‐fidelity, effective simulation methods decode invisible interactions LIBs.

Language: Английский

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Data Generation for Machine Learning Interatomic Potentials and Beyond

Chemical Reviews, Journal Year: 2024, Volume and Issue: 124(24), P. 13681 - 13714

Published: Nov. 21, 2024

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides ML-based interatomic potentials have paved the way accurate modeling diverse chemical structural at atomic level. key determinant defining MLIP reliability remains quality training data. A paramount challenge lies constructing sets that capture specific domains vast space. This Review navigates intricate landscape essential components integrity data ensure extensibility transferability resulting models. We delve into details active learning, discussing its various facets implementations. outline different types uncertainty quantification applied to atomistic acquisition correlations between estimated true error. role samplers generating informative structures highlighted. Furthermore, we discuss via modified surrogate potential energy surfaces as innovative approach diversify also provides a list publicly available cover

Language: Английский

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