Universal machine-learning interatomic potentials (uMLIPs) have become powerful tools for accelerating computational materials discovery by replacing expensive first-principles calculations in crystal structure prediction (CSP). However, their effectiveness in identifying new, complex materials remains uncertain. Here, we systematically assess the capability of a uMLIP (i.e.,M3GNet) to accelerate CSP in quaternary oxides. Through extensive exploration of the Sr-Li-Al-O and Ba-Y-Al-O systems, we show that uMLIP can rediscover experimentally known materials absent from its training set and identify seven new thermodynamically and dynamically stable compounds. These include a new polymorph of Sr2LiAlO4 (P3221) and a new disordered phase, Sr2Li4Al2O7 (P1_bar). Furthermore, our results show stability predictions based on the semilocal PBE functional require cross-validation with higher-level methods, such as SCAN and RPA, to ensure reliability. While uMLIPs substantially reduce the computational cost of CSP, the primary bottleneck has shifted to the efficiency of search algorithms in navigating complex structural spaces. This work highlights both the promise and current limitations of uMLIP-driven CSP in the discovery of new materials.
Structures and properties of many inorganic compounds have been collected historically. However, it only covers a very small portion of possible inorganic crystals, which implies the presence of numerous currently unknown compounds. A powerful machine-learning strategy is mandatory to discover new inorganic compounds from all chemical combinations. Herein we propose a descriptor-based recommender-system approach to estimate the relevance of chemical compositions where stable crystals can be formed [i.e., chemically relevant compositions (CRCs)]. As well as data-driven compositional similarity …
Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive density functional theory (DFT) calculations, which limits ML-based exploration to either known crystals or a small number of hypothetical crystals. Here, we demonstrate that the application of Bayesian optimization with symmetry constraints using a graph deep learning energy model can be used to perform "DFT-free" relaxations of crystal structures. Using this a…
Accelerating Combinatorial Electrocatalyst Discovery with Bayesian Optimization: A Case Study in the Quaternary System Ni-Pd-Pt-Ru for the Oxygen Evolution Reaction
Accelerating Discovery of Extreme Lattice Thermal Conductivity by Crystal Attention Graph Neural Network (CATGNN) Using Chemical Bonding Intuitive Descriptors
Role of Large Language Models and Retrieval-Augmented Generation for Accelerating Crystalline Material Discovery: A Systematic Review
Bredesen Center for Interdisciplinary Research, University of Tennessee, Knoxville, USA（跨学科研究中心，田纳西大学，诺克斯维尔，美国）；University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee, Knoxville, USA（田纳西大学橡树岭创新研究所，田纳西大学，诺克斯维尔，美国）
Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation
Accelerating discovery across scientific disciplines through reproducible workflows with AiiDAlab
Centre for Computational Chemistry, School of Chemistry, University of Bristol（布里斯托大学化学学院计算化学中心）；PSI Center for Scientific Computing, Theory and Data, Paul Scherrer Institute（保罗谢尔研究所科学计算、理论与数据中心）；Department of Information Technology and Electrical Engineering, ETH Zurich（苏黎世联邦理工学院信息技术与电气工程系）；PSI Center for Neutron and Muon Sciences, Paul Scherrer Institute（保罗谢尔研究所中子与缪子科学中心）
Two-dimensional materials supported by single atom catalysis (SACs) are foreseen to replace platinum for large-scale industrial scalability of sustainable hydrogen generation. Here, a series of metal (Al, Sc, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn) and non-metal (B, C, N, O, F, Si, P, S, Cl) single atoms embedded on various active sites of g-C$_3$N$_4$ are screened by DFT calculations and six machine learning (ML) algorithms (support vector regression, gradient boosting regression, random forest regression, AdaBoost regression, multilayer perceptron regression, ridge regression). Our results based on f…
Accelerating the discovery of low-energy structure configurations: a computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning
Accelerating discovery of infrared nonlinear optical materials with large shift current via high-throughput screening
Soochow University（苏州大学）；AI for Science Institute（人工智能科学研究院）；Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University（香港理工大学智能穿戴系统研究所）；Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学学院生物医学工程系）
Knowledge Lab, University of Chicago（芝加哥大学知识实验室）；Santa Fe Institute（圣塔菲研究所）
Data-driven artificial intelligence models fed with published scientific findings have been used to create powerful prediction engines for scientific and technological advance, such as the discovery of novel materials with desired properties and the targeted invention of new therapies and vaccines. These AI approaches typically ignore the distribution of human prediction engines -- scientists and inventor -- who continuously alter the landscape of discovery and invention. As a result, AI hypotheses are designed to substitute for human experts, failing to complement them for punctuated collecti…
Accelerating Discovery of Vacancy Ordered 18-Valence Electron Half-Heusler Compounds: A Synergistic Approach of Machine Learning and Density Functional Theory
Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery
Guangzhou Municipal Key Laboratory of Materials Informatics, Advanced Materials Thrust, The Hong Kong University of Science and Technology (Guangzhou)（广州材料信息学重点实验室，先进材料推力组，香港科技大学（广州））；Department of Physics, City University of Hong Kong（香港城市大学物理系）；Materials Genome Institute, Shanghai University（上海大学材料基因组研究院）；State Key Laboratory of Materials for Advanced Nuclear Energy, Shanghai University（上海大学先进核能材料国家重点实验室）；School of Materials Science and Engineering, Shanghai Jiao Tong University（上海交通大学材料科学与工程学院）；School of Aerospace, Mechanical and Mechatronic Engineering, School of Computer Science, The University of Sydney（悉尼大学航空航天、机械与机电工程学院，计算机科学学院）；School of Materials Science and Engineering, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）材料科学与工程学院）；Suzhou Laboratory（苏州实验室）；State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University（西安交通大学材料力学行为国家重点实验室）；AiMaterials Research（AiMaterials研究）；University College London（伦敦大学学院）
Accelerating Phonon Thermal Conductivity Prediction by an Order of Magnitude Through Machine Learning-Assisted Extraction of Anharmonic Force Constants
Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery. To overcome this issue, we have developed a universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, wh…
Accelerated Discovery of Crystalline Materials with Record Ultralow Lattice Thermal Conductivity via a Universal Descriptor
Northwestern Polytechnical University（西北工业大学）；CRISMAT, CNRS, Normandie Univ, ENSICAEN, UNICAEN（诺曼底大学，卡昂国立高等工程学院，法国国家科学研究中心材料研究化学实验室）；Thayer School of Engineering, Dartmouth College（达特茅斯学院塔克工程学院）；Institute Laue Langevin（朗之万研究所）；FZU - Institute of Physics of the Czech Academy of Sciences（捷克科学院物理研究所）；ShanghaiTech Laboratory for Topological Physics & School of Physical Science and Technology, ShanghaiTech University（上海科技大学物理科学与技术学院拓扑物理实验室）；Université de Lorraine, CNRS, IJL（洛林大学，法国国家科学研究中心，界面与层析实验室）
Massive Discovery of Low-Dimensional Materials from Universal Computational Strategy
Nanoscience Center, Department of Physics, University of Jyväskylä（于韦斯屈莱大学）；Department of Physics, Chalmers University of Technology（查尔姆斯理工大学）；Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu（奥卢大学）
Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
organization= Department of Materials Science；Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA；organization= J. Mike Walker '66 Department of Mechanical Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA；organization= Wm Michael Barnes '64 Department of Industrial；Systems Engineering, Texas A\&M University , city= College Station , postcode= 77843 , state= TX , country= USA
Assessing the synthesizability of inorganic materials is a grand challenge for accelerating their discovery using computations. Synthesis of a material is a complex process that depends not only on its thermodynamic stability with respect to others, but also on factors from kinetics, to advances in synthesis techniques, to the availability of precursors. This complexity makes the development of a general theory or first-principles approach to synthesizability currently impractical. Here we show how an alternative pathway to predicting synthesizability emerges from the dynamics of the materials…
Generative Chemical Language Models for Energetic Materials Discovery
Theoretical Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室理论部）；Weapon Stockpile Modernization Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室武器库存现代化部）；Computing and Artificial Intelligence Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室计算与人工智能部）
