Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials DOI
Zachary A. H. Goodwin, Malia B. Wenny, Julia H. Yang

et al.

The Journal of Physical Chemistry Letters, Journal Year: 2024, Volume and Issue: 15(30), P. 7539 - 7547

Published: July 18, 2024

Ionic liquids (ILs) are an exciting class of electrolytes finding applications in many areas from energy storage to solvents, where they have been touted as "designer solvents" can be mixed precisely tailor the physiochemical properties. As using machine learning interatomic potentials (MLIPs) simulate ILs is still relatively unexplored, several questions need answered see if MLIPs transformative for ILs. Since often not pure, but either together or contain additives, we first demonstrate that a MLIP trained compositionally transferable; i.e., applied mixtures ions directly on, while only being on few same ions. We also investigated accuracy novel IL, which experimentally synthesize and characterize. Our ∼200 DFT frames reasonable agreement with our experiments DFT.

Language: Английский

Recent advances and outstanding challenges for machine learning interatomic potentials DOI
Tsz Wai Ko, Shyue Ping Ong

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

Published: Dec. 13, 2023

Language: Английский

Citations

65
Recent advances in artificial intelligence boosting materials design for electrochemical energy storage DOI Creative Commons
X.-B. Liu, Kexin Fan, Xinmeng Huang

et al.

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

Citations

34
Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations DOI Creative Commons
Guanjie Wang, Changrui Wang,

Xuanguang Zhang

et al.

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

Citations

30
Robust training of machine learning interatomic potentials with dimensionality reduction and stratified sampling DOI Creative Commons
Ji Qi, Tsz Wai Ko, Brandon C. Wood

et al.

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

Citations

28
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials DOI Creative Commons
Bohayra Mortazavi

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 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.

Language: Английский

Citations

26
Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation DOI Creative Commons
Rui Ding, Junhong Chen, Yuxin Chen

et al.

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

Citations

24
Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation DOI
Chi Chen, Dan Thien Nguyen, Shannon Lee

et al.

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

Citations

23
Molecular engineering of renewable cellulose biopolymers for solid-state battery electrolytes DOI
Jinyang Li, Ziyang Hu,

Sidong Zhang

et al.

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

Published: Sept. 3, 2024

Language: Английский

Citations

23
A reactive neural network framework for water-loaded acidic zeolites DOI Creative Commons
Andreas Erlebach, Martin Šípka, Indranil Saha

et al.

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

Published: May 17, 2024

Language: Английский

Citations

20
Decoding Electrochemical Processes of Lithium‐Ion Batteries by Classical Molecular Dynamics Simulations DOI
Xi Tan, Ming Chen, Jinkai Zhang

et al.

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

Citations

19