Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

Advanced Energy Materials, Год журнала: 2024, Номер unknown

Опубликована: Дек. 10, 2024

Abstract This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification candidates with desirable properties. Recently, development highly accurate interatomic potentials generative models has not only improved robust prediction physical but also significantly accelerated discovery In past couple years, methods have enabled high‐precision first‐principles predictions electronic optical properties for large systems, providing unprecedented opportunities science. Furthermore, ML‐assisted microstructure reconstruction physics‐informed solutions partial differential equations facilitated understanding microstructure–property relationships. Most recently, seamless integration various platforms led emergence autonomous laboratories that combine quantum mechanical calculations, language models, experimental validations, fundamentally transforming traditional approach novel synthesis. While highlighting aforementioned advances, existing challenges are discussed. Ultimately, is expected fully integrate atomic‐scale simulations, reverse engineering, process optimization, device fabrication, empowering system design. will drive transformative innovations conversion, storage, harvesting technologies.

Язык: Английский

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Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023

Chemical Science, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

We present a comprehensive analysis of the capabilities modern machine learning force fields to simulate long-term molecular dynamics at near-ambient conditions for molecules, molecule-surface interfaces, and materials within TEA Challenge 2023.

Язык: Английский

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ANI-1ccx-gelu Universal Interatomic Potential and Its Fine-Tuning: Toward Accurate and Efficient Anharmonic Vibrational Frequencies

The Journal of Physical Chemistry Letters, Год журнала: 2025, Номер unknown, С. 483 - 493

Опубликована: Янв. 2, 2025

Calculating anharmonic vibrational modes of molecules for interpreting experimental spectra is one the most interesting challenges contemporary computational chemistry. However, traditional QM methods are costly this application. Machine learning techniques have emerged as a powerful tool substituting methods. Universal interatomic potentials (UIPs) hold particular promise to deliver accurate results at fraction cost methods, but performance UIPs calculating frequencies remains hitherto unknown. Here we show that despite known excellent representative UIP ANI-1ccx thermochemical properties, it fails due original unfortunate choice activation function. Hence, recommend evaluating new on an additional important quality test. To remedy shortcomings ANI-1ccx, introduce its reformulation ANI-1ccx-gelu with GELU function, which capable IR reasonable accuracy (close B3LYP/6-31G*). We also our can be fine-tuned obtain very some specific more effort needed improve overall and capability fine-tuning. The will included part universal updatable AI-enhanced (UAIQM) platform available together usage fine-tuning tutorials in open-source MLatom https://github.com/dralgroup/mlatom. calculations performed via web browser https://XACScloud.com.

Язык: Английский

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A practical guide to machine learning interatomic potentials – Status and future

Current Opinion in Solid State and Materials Science, Год журнала: 2025, Номер 35, С. 101214 - 101214

Опубликована: Фев. 26, 2025

Язык: Английский

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The evolution of machine learning potentials for molecules, reactions and materials

Chemical Society Reviews, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

This review offers a comprehensive overview of the development machine learning potentials for molecules, reactions, and materials over past two decades, evolving from traditional models to state-of-the-art.

Язык: Английский

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AI-driven accelerated discovery of intercalation-type cathode materials for magnesium batteries

Journal of Energy Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Язык: Английский

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Efficient equivariant model for machine learning interatomic potentials

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

Опубликована: Фев. 26, 2025

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Accelerating high-throughput phonon calculations via machine learning universal potentials

Materials Today Physics, Год журнала: 2025, Номер unknown, С. 101688 - 101688

Опубликована: Март 1, 2025

Язык: Английский

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CrySPAI: A New Crystal Structure Prediction Software Based on Artificial Intelligence

Inventions, Год журнала: 2025, Номер 10(2), С. 26 - 26

Опубликована: Март 6, 2025

Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most these approaches are limited to predicting specific systems, which hinders their application unknown or unexplored domains. In this paper, we present a crystal prediction software artificial intelligence, named as CrySPAI, predict energetically stable structures inorganic given chemical compositions. The consists three key modules, an evolutionary optimization algorithm (EOA) that searches for all possible configurations, density functional theory (DFT) provides accurate energy values structures, deep neural network (DNN) learns relationship between corresponding energies. To optimize process across distributed framework is implemented parallelize tasks, automated workflow has been integrated into CrySPAI seamless execution. This paper reports development implementation AI-based Prediction Software tool its unique features.

Язык: Английский

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The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design

Wiley Interdisciplinary Reviews Computational Molecular Science, Год журнала: 2025, Номер 15(2)

Опубликована: Март 1, 2025

ABSTRACT With the increasing global demand for energy transition and environmental sustainability, catalysts play a vital role in mitigating climate change, as they facilitate over 90% of chemical material conversions. It is important to investigate complex structures properties enhanced performance, which artificial intelligence (AI) methods, especially graph neural networks (GNNs) could be useful. In this article, we explore cutting‐edge applications future potential GNNs intelligent catalyst design. The fundamental theories their practical catalytic simulation inverse design are first reviewed. We analyze critical roles accelerating screening, performance prediction, reaction pathway analysis, mechanism modeling. By leveraging convolution techniques accurately represent molecular structures, integrating symmetry constraints ensure physical consistency, applying generative models efficiently space, these approaches work synergistically enhance efficiency accuracy Furthermore, highlight high‐quality databases crucial catalysis research innovative application thermocatalysis, electrocatalysis, photocatalysis, biocatalysis. end, key directions advancing catalysis: dynamic frameworks real‐time conditions, hierarchical linking atomic details features, multi‐task interpretability mechanisms reveal pathways. believe advancements will significantly broaden science, paving way more efficient, accurate, sustainable methodologies.

Язык: Английский

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