Recent advances and outstanding challenges for machine learning interatomic potentials

Nature Computational Science, Journal Year: 2023, Volume and Issue: 3(12), P. 998 - 1000

Published: Dec. 13, 2023

Language: Английский

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Recent advances in artificial intelligence boosting materials design for electrochemical energy storage

Chemical Engineering Journal, Journal Year: 2024, Volume and Issue: 490, P. 151625 - 151625

Published: April 24, 2024

In the rapidly evolving landscape of electrochemical energy storage (EES), advent artificial intelligence (AI) has emerged as a keystone for innovation in material design, propelling forward design and discovery batteries, fuel cells, supercapacitors, many other functional materials. This review paper elucidates burgeoning role AI materials from foundational machine learning (ML) techniques to its current pivotal advancing frontiers science storage, including enhancing performance, durability, safety battery technologies, cell efficiency longevity, fine-tuning supercapacitors achieve superior capabilities. Collectively, we present comprehensive overview recent advancements that have significantly accelerated development next-generation EES, offering insights into future research trajectories potential unlock new horizons science.

Language: Английский

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Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations

iScience, Journal Year: 2024, Volume and Issue: 27(5), P. 109673 - 109673

Published: April 4, 2024

Machine learning interatomic potential (MLIP) overcomes the challenges of high computational costs in density-functional theory and relatively low accuracy classical large-scale molecular dynamics, facilitating more efficient precise simulations materials research design. In this review, current state four essential stages MLIP is discussed, including data generation methods, material structure descriptors, six unique machine algorithms, available software. Furthermore, applications various fields are investigated, notably phase-change memory materials, searching, properties predicting, pre-trained universal models. Eventually, future perspectives, consisting standard datasets, transferability, generalization, trade-off between complexity MLIPs, reported.

Language: Английский

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Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling

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

Published: Feb. 26, 2024

Abstract Machine learning interatomic potentials (MLIPs) enable accurate simulations of materials at scales beyond that accessible by ab initio methods and play an increasingly important role in the study design materials. However, MLIPs are only as robust data on which they trained. Here, we present DImensionality-Reduced Encoded Clusters with sTratified (DIRECT) sampling approach to select a training set structures from large complex configuration space. By applying DIRECT Materials Project relaxation trajectories dataset over one million 89 elements, develop improved 3-body graph network (M3GNet) universal potential extrapolates more reliably unseen structures. We further show molecular dynamics (MD) M3GNet can be used instead expensive MD rapidly create space for target systems. combined this scheme reliable moment tensor titanium hydrides without need iterative augmentation This work paves way high-throughput development across any compositional complexity.

Language: Английский

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Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(29), P. 20009 - 20018

Published: July 9, 2024

High-throughput computational materials discovery has promised significant acceleration of the design and new for many years. Despite a surge in interest activity, constraints imposed by large-scale resources present bottleneck. Furthermore, examples very carried out through experimental validation remain scarce, especially with product applicability. Here, we demonstrate how this vision became reality combining state-of-the-art machine learning (ML) models traditional physics-based on cloud high-performance computing (HPC) to quickly navigate more than 32 million candidates predict around half potentially stable materials. By focusing solid-state electrolytes battery applications, our pipeline further identified 18 promising compositions rediscovered decade's worth collective knowledge field as byproduct. We then synthesized experimentally characterized structures conductivities top candidates, Na

Language: Английский

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Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Chemical Society Reviews, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

This review explores machine learning's impact on designing electrocatalysts for hydrogen energy, detailing how it transcends traditional methods by utilizing experimental and computational data to enhance electrocatalyst efficiency discovery.

Language: Английский

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Application of machine learning in perovskite materials and devices: A review

Journal of Energy Chemistry, Journal Year: 2024, Volume and Issue: 94, P. 254 - 272

Published: March 6, 2024

Language: Английский

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Molecular engineering of renewable cellulose biopolymers for solid-state battery electrolytes

Nature Sustainability, Journal Year: 2024, Volume and Issue: 7(11), P. 1481 - 1491

Published: Sept. 3, 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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A reactive neural network framework for water-loaded acidic zeolites

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

Published: May 17, 2024

Language: Английский

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