Pub Date : 2026-02-02DOI: 10.1038/s41524-026-01972-8
Reshma Devi, Keith T. Butler, Gopalakrishnan Sai Gautam
Rate performance of several applications, such as batteries, fuel cells, and electrochemical sensors, is exponentially dependent on the ionic migration barrier (Em) within solids, a difficult-to-estimate quantity. Previous approaches to identify materials with low Em have often relied on imprecise descriptors or rules-of-thumb. Here, we present a graph-neural-network-based architecture that leverages principles of transfer learning to efficiently and accurately predict Em across a variety of materials. We use a model (labeled MPT) that has been simultaneously pre-trained on seven bulk properties, introduce architectural modifications to build inductive bias on different migration pathways in a structure, and subsequently fine-tune (FT) on a manually-curated, literature-derived, first-principles computational dataset of 619 Em values. Importantly, our best-performing FT model (labeled MODEL-3, based on test set scores) demonstrates substantially better accuracy compared to classical machine learning methods, graph models trained from scratch, and a universal machine learned interatomic potential, with a R2 score and a mean absolute error of 0.703 ± 0.109 and 0.261 ± 0.034 eV, respectively, on the test set and is able to classify ‘good’ ionic conductors with an 80% accuracy. Thus, our work demonstrates the effective use of FT strategies and MPT architectural modifications to predict Em, and can be extended to make predictions on other data-scarce material properties.
{"title":"Leveraging transfer learning for accurate estimation of ionic migration barriers in solids","authors":"Reshma Devi, Keith T. Butler, Gopalakrishnan Sai Gautam","doi":"10.1038/s41524-026-01972-8","DOIUrl":"https://doi.org/10.1038/s41524-026-01972-8","url":null,"abstract":"Rate performance of several applications, such as batteries, fuel cells, and electrochemical sensors, is exponentially dependent on the ionic migration barrier (Em) within solids, a difficult-to-estimate quantity. Previous approaches to identify materials with low Em have often relied on imprecise descriptors or rules-of-thumb. Here, we present a graph-neural-network-based architecture that leverages principles of transfer learning to efficiently and accurately predict Em across a variety of materials. We use a model (labeled MPT) that has been simultaneously pre-trained on seven bulk properties, introduce architectural modifications to build inductive bias on different migration pathways in a structure, and subsequently fine-tune (FT) on a manually-curated, literature-derived, first-principles computational dataset of 619 Em values. Importantly, our best-performing FT model (labeled MODEL-3, based on test set scores) demonstrates substantially better accuracy compared to classical machine learning methods, graph models trained from scratch, and a universal machine learned interatomic potential, with a R2 score and a mean absolute error of 0.703 ± 0.109 and 0.261 ± 0.034 eV, respectively, on the test set and is able to classify ‘good’ ionic conductors with an 80% accuracy. Thus, our work demonstrates the effective use of FT strategies and MPT architectural modifications to predict Em, and can be extended to make predictions on other data-scarce material properties.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"39 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1038/s41524-026-01974-6
Qingkun Tian, Longgang Hou, Junmei Wang, Flemming J. H. Ehlers, Hui Su, Yawen Wang, Yuhong Zhao, Linzhong Zhuang
Age-hardenable Al–Li alloys are critical lightweight structural materials, offering high specific strength. However, the early-stage decomposition of supersaturated solid solution, specifically formation of Guinier-Preston (GPAl-Li) zones during aging, remains a key gap in understanding precipitation sequence. Using density functional theory and cluster expansion method, we determined effective cluster interactions for Al–Li alloys in an fcc lattice and computed Gibbs free energy via meta-dynamics Monte Carlo simulations. A metastable phase diagram encompassing ({{rm{alpha }}}_{{rm{Al}}}), GPAl-Li, and ({{rm{delta }}}^{{prime} }) phases was constructed across relevant temperatures. GPAl–Li zones was revealed to possess a well-ordered structure, further supported by electronic structure analysis. Kinetic phase-field simulations of early-stage decomposition revealed that within appropriate Li concentration ranges, GPAl-Li zones form rapidly and extensively below 483 K, later transforming into ({{rm{delta }}}^{{prime} }) precipitates. These GPAl–Li zones should be directly discernable in cryogenic treated Al–Li alloys, owing to their deeper free energy well and sufficiently slow transformation. We propose that even outside this composition range, GPAl–Li zones may form transiently on the path towards ({{rm{delta }}}^{{prime} }), justifying their inclusion in precipitation sequence. Factors promoting T1 phase nucleation via GPAl–Li zones in Al–Li–Cu alloys were also explored, providing theoretical insights for advanced alloy design.
{"title":"Multi-scale modeling GPAl-Li zones in Al-Li alloys starting from first-principles","authors":"Qingkun Tian, Longgang Hou, Junmei Wang, Flemming J. H. Ehlers, Hui Su, Yawen Wang, Yuhong Zhao, Linzhong Zhuang","doi":"10.1038/s41524-026-01974-6","DOIUrl":"https://doi.org/10.1038/s41524-026-01974-6","url":null,"abstract":"Age-hardenable Al–Li alloys are critical lightweight structural materials, offering high specific strength. However, the early-stage decomposition of supersaturated solid solution, specifically formation of Guinier-Preston (GPAl-Li) zones during aging, remains a key gap in understanding precipitation sequence. Using density functional theory and cluster expansion method, we determined effective cluster interactions for Al–Li alloys in an fcc lattice and computed Gibbs free energy via meta-dynamics Monte Carlo simulations. A metastable phase diagram encompassing ({{rm{alpha }}}_{{rm{Al}}}), GPAl-Li, and ({{rm{delta }}}^{{prime} }) phases was constructed across relevant temperatures. GPAl–Li zones was revealed to possess a well-ordered structure, further supported by electronic structure analysis. Kinetic phase-field simulations of early-stage decomposition revealed that within appropriate Li concentration ranges, GPAl-Li zones form rapidly and extensively below 483 K, later transforming into ({{rm{delta }}}^{{prime} }) precipitates. These GPAl–Li zones should be directly discernable in cryogenic treated Al–Li alloys, owing to their deeper free energy well and sufficiently slow transformation. We propose that even outside this composition range, GPAl–Li zones may form transiently on the path towards ({{rm{delta }}}^{{prime} }), justifying their inclusion in precipitation sequence. Factors promoting T1 phase nucleation via GPAl–Li zones in Al–Li–Cu alloys were also explored, providing theoretical insights for advanced alloy design.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"7 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1038/s41524-026-01975-5
Xinran Zhou, Jaime Marian, Fei Zhou, Vasily V. Bulatov
Refractory complex concentrated alloys (RCCA) offer exceptionally high-temperature strength compared to pure metals and dilute alloys, but predictive theory for RCCA design is lacking. We present large-scale molecular Dynamics (MD) simulations of crystal plasticity to explore alloy compositions for maximum mechanical strength, focusing on Fe-Ta-W and Nb-Ta-Mo-W alloy families modeled with Embedded Atom Model (EAM) and Spectral Neighbor Analysis Potentials (SNAP). To efficiently guide the search for strong alloy compositions, we employ iterative optimization using Gaussian process regression. Many simulated RCCA compositions exhibit pronounced cocktail strengthening, with strengths surpassing their strongest constituent metal, tungsten. Contrary to expectations, the highest strength is found on binary edges of the RCCA composition space. Detailed analyses of atomistic simulations reveal that, similar to pure BCC metals, plastic response in RCCA is primarily governed by screw dislocations. However, at large strains, dislocation multiplication and interactions (Taylor hardening) become the dominant mechanisms contributing to RCCA strength.
{"title":"Probing multi-dimensional composition spaces in search of strong metallic alloys","authors":"Xinran Zhou, Jaime Marian, Fei Zhou, Vasily V. Bulatov","doi":"10.1038/s41524-026-01975-5","DOIUrl":"https://doi.org/10.1038/s41524-026-01975-5","url":null,"abstract":"Refractory complex concentrated alloys (RCCA) offer exceptionally high-temperature strength compared to pure metals and dilute alloys, but predictive theory for RCCA design is lacking. We present large-scale molecular Dynamics (MD) simulations of crystal plasticity to explore alloy compositions for maximum mechanical strength, focusing on Fe-Ta-W and Nb-Ta-Mo-W alloy families modeled with Embedded Atom Model (EAM) and Spectral Neighbor Analysis Potentials (SNAP). To efficiently guide the search for strong alloy compositions, we employ iterative optimization using Gaussian process regression. Many simulated RCCA compositions exhibit pronounced cocktail strengthening, with strengths surpassing their strongest constituent metal, tungsten. Contrary to expectations, the highest strength is found on binary edges of the RCCA composition space. Detailed analyses of atomistic simulations reveal that, similar to pure BCC metals, plastic response in RCCA is primarily governed by screw dislocations. However, at large strains, dislocation multiplication and interactions (Taylor hardening) become the dominant mechanisms contributing to RCCA strength.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"79 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146090087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ferroelectric perovskites with giant spontaneous polarization have extensive applications in electronic devices, energy conversion, sensor and so on. However, the rapid discovery of new perovskites with giant polarization remains an open challenge especially when thousands of candidates are treated. Here, combining machine learning (ML) and first-principles calculations, we successfully predict 8 perovskites with giant polarization from 2021 different possible compounds, among which seven candidates have never been reported before. These perovskites have large c/a ratio and giant polarization compared to the reported ferroelectric perovskites, and room temperature stability. Among them, the polarization of SnFeO3 with G-AFM magnetic ordering is as high as 138.63 µC/cm2. The non-magnetic SrPbO3 and magnetic EuSnO3 not only exhibit giant polarization, but also possess band gaps close to the ideal value for photovoltaic applications, showing great potential in the field of ferroelectric photovoltaics. Besides, polarity and metallicity coexist in SnFeO3 and CaTaO3, which are suggested to have potential applications in fields such as spintronics and superconductivity. This work thus provides an effective strategy for discovering new functional materials.
{"title":"Accelerated discovery of supertetragonal perovskites with giant polarization via machine learning","authors":"Wenguang Hu, Zebin Wu, Menglu Li, Shan Feng, Hangbo Qi, Xingjian Lu, Xiaotao Zu, Haiyan Xiao, Liang Qiao","doi":"10.1038/s41524-026-01970-w","DOIUrl":"https://doi.org/10.1038/s41524-026-01970-w","url":null,"abstract":"Ferroelectric perovskites with giant spontaneous polarization have extensive applications in electronic devices, energy conversion, sensor and so on. However, the rapid discovery of new perovskites with giant polarization remains an open challenge especially when thousands of candidates are treated. Here, combining machine learning (ML) and first-principles calculations, we successfully predict 8 perovskites with giant polarization from 2021 different possible compounds, among which seven candidates have never been reported before. These perovskites have large c/a ratio and giant polarization compared to the reported ferroelectric perovskites, and room temperature stability. Among them, the polarization of SnFeO3 with G-AFM magnetic ordering is as high as 138.63 µC/cm2. The non-magnetic SrPbO3 and magnetic EuSnO3 not only exhibit giant polarization, but also possess band gaps close to the ideal value for photovoltaic applications, showing great potential in the field of ferroelectric photovoltaics. Besides, polarity and metallicity coexist in SnFeO3 and CaTaO3, which are suggested to have potential applications in fields such as spintronics and superconductivity. This work thus provides an effective strategy for discovering new functional materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"143 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1038/s41524-026-01964-8
Jason B. Gibson, Ajinkya C. Hire, Pawan Prakash, Philip M. Dee, Benjamin Geisler, Jung Soo Kim, Zhongwei Li, James J. Hamlin, Gregory R. Stewart, P. J. Hirschfeld, Richard G. Hennig
The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed Tc > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.
{"title":"Developing a complete AI-accelerated workflow for superconductor discovery","authors":"Jason B. Gibson, Ajinkya C. Hire, Pawan Prakash, Philip M. Dee, Benjamin Geisler, Jung Soo Kim, Zhongwei Li, James J. Hamlin, Gregory R. Stewart, P. J. Hirschfeld, Richard G. Hennig","doi":"10.1038/s41524-026-01964-8","DOIUrl":"https://doi.org/10.1038/s41524-026-01964-8","url":null,"abstract":"The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed Tc > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"296 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The overall development of the machine learning force field (MLFF) has advanced rapidly, with a wide range of models emerging in recent years. However, some fundamental questions remain underexplored, such as why certain systems are intrinsically more difficult to train than others. Understanding this question can help us to propose different models and prepare appropriate datasets for different situations. We constructed Metal-43, a high-quality dataset comprising elemental structures of 43 metallic elements. Through systematic analysis, we reveal regular trends of fitting accuracies of these elemental metals in the periodic table. Unlike previous approaches that generally attribute fitting challenges to a vague notion of a “complex potential energy surface (PES)”, which is almost a synonym of the fitting difficulty, we provide a physical picture which connects the Fermi surface complexity to this complexity of PES. Furthermore, we demonstrate that current MLFF models still face clear limitations in capturing the complex PES even for elemental materials. These findings can provide a theoretical foundation and directional guidance for the development of more general and accurate MLFF models in the future.
{"title":"Origin of the machine learning forces field errors across metal elements","authors":"Xingze Geng, Wentao Zhang, Lin-Wang Wang, Xiangying Meng","doi":"10.1038/s41524-026-01977-3","DOIUrl":"https://doi.org/10.1038/s41524-026-01977-3","url":null,"abstract":"The overall development of the machine learning force field (MLFF) has advanced rapidly, with a wide range of models emerging in recent years. However, some fundamental questions remain underexplored, such as why certain systems are intrinsically more difficult to train than others. Understanding this question can help us to propose different models and prepare appropriate datasets for different situations. We constructed Metal-43, a high-quality dataset comprising elemental structures of 43 metallic elements. Through systematic analysis, we reveal regular trends of fitting accuracies of these elemental metals in the periodic table. Unlike previous approaches that generally attribute fitting challenges to a vague notion of a “complex potential energy surface (PES)”, which is almost a synonym of the fitting difficulty, we provide a physical picture which connects the Fermi surface complexity to this complexity of PES. Furthermore, we demonstrate that current MLFF models still face clear limitations in capturing the complex PES even for elemental materials. These findings can provide a theoretical foundation and directional guidance for the development of more general and accurate MLFF models in the future.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Colossal magnetoresistance (CMR) is typically observed in manganites and magnetic semiconductors, marked by a resistivity peak near the magnetic transition temperature that is significantly suppressed by an applied magnetic field, commonly referred to as peak-type CMR. This type of CMR has attracted extensive research efforts over the past decades. However, in some materials such as Mn3Si2Te6, both peak-type and upturn-type CMR coexist—the latter characterized by a sharp resistivity upturn at low temperatures that is also strongly suppressed by an external field. Research on the coexistence of these two types of CMR remains relatively unexplored. In our work, we propose a theoretical framework to unravel the mechanisms underlying the above mentioned CMR phenomenon in magnetic semiconductors, and apply it to the ferrimagnetic semiconductor Mn3Si2Te6. The experimentally observed ρ(B, T) behaviors are accurately reproduced, including the upturn-type CMR, peak-type CMR, and movement of Tc (or resistivity peak) with fields. Additionally, the suppression of Tc and resistivity with increasing direct currents, possibly associated with current control of the chiral orbital current (COC) state in the previous work, can also be reproduced within our framework by properly accounting for the Joule heating effects. Our work provides a new perspective for quantitatively calculating and analyzing the unusual resistivity responses to temperature, field, and current in magnetic semiconductors.
{"title":"Colossal magnetoresistance and unusual resistivity behaviors in magnetic semiconductors: Mn3Si2Te6 as a case study","authors":"Zhihao Liu, Zhong Fang, Hongming Weng, Quansheng Wu","doi":"10.1038/s41524-026-01963-9","DOIUrl":"https://doi.org/10.1038/s41524-026-01963-9","url":null,"abstract":"Colossal magnetoresistance (CMR) is typically observed in manganites and magnetic semiconductors, marked by a resistivity peak near the magnetic transition temperature that is significantly suppressed by an applied magnetic field, commonly referred to as peak-type CMR. This type of CMR has attracted extensive research efforts over the past decades. However, in some materials such as Mn3Si2Te6, both peak-type and upturn-type CMR coexist—the latter characterized by a sharp resistivity upturn at low temperatures that is also strongly suppressed by an external field. Research on the coexistence of these two types of CMR remains relatively unexplored. In our work, we propose a theoretical framework to unravel the mechanisms underlying the above mentioned CMR phenomenon in magnetic semiconductors, and apply it to the ferrimagnetic semiconductor Mn3Si2Te6. The experimentally observed ρ(B, T) behaviors are accurately reproduced, including the upturn-type CMR, peak-type CMR, and movement of Tc (or resistivity peak) with fields. Additionally, the suppression of Tc and resistivity with increasing direct currents, possibly associated with current control of the chiral orbital current (COC) state in the previous work, can also be reproduced within our framework by properly accounting for the Joule heating effects. Our work provides a new perspective for quantitatively calculating and analyzing the unusual resistivity responses to temperature, field, and current in magnetic semiconductors.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"86 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1038/s41524-026-01971-9
Jiaxiang Li, Junwei Feng, Jie Luo, Bowen Jiang, Xiangyu Zheng, Qigang Song, Jian Lv, Keith Butler, Hanyu Liu, Congwei Xie, Yu Xie, Yanming Ma
Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges persist in ensuring robust generalization to unknown structures and minimizing the requirement for substantial expert knowledge and time-consuming manual interventions. Here, we propose an automated crystal structure prediction framework built upon the attention-coupled neural network potential to address these limitations. The generalizability of the potential is achieved by sampling regions across the local minima of the potential energy surface, where the self-evolving pipeline autonomously refines the potential iteratively while minimizing human intervention. The workflow is validated on Mg-Ca-H ternary and Be-P-N-O quaternary systems by exploring nearly 10 million configurations, demonstrating substantial speedup compared to first-principles calculations. These results underscore the effectiveness of our approach in accelerating the exploration and discovery of complex multi-component functional materials.
{"title":"Self-optimizing machine learning potential assisted automated workflow for highly efficient complex systems material design","authors":"Jiaxiang Li, Junwei Feng, Jie Luo, Bowen Jiang, Xiangyu Zheng, Qigang Song, Jian Lv, Keith Butler, Hanyu Liu, Congwei Xie, Yu Xie, Yanming Ma","doi":"10.1038/s41524-026-01971-9","DOIUrl":"https://doi.org/10.1038/s41524-026-01971-9","url":null,"abstract":"Machine learning interatomic potentials have revolutionized complex materials design by enabling rapid exploration of material configurational spaces via crystal structure prediction with ab initio accuracy. However, critical challenges persist in ensuring robust generalization to unknown structures and minimizing the requirement for substantial expert knowledge and time-consuming manual interventions. Here, we propose an automated crystal structure prediction framework built upon the attention-coupled neural network potential to address these limitations. The generalizability of the potential is achieved by sampling regions across the local minima of the potential energy surface, where the self-evolving pipeline autonomously refines the potential iteratively while minimizing human intervention. The workflow is validated on Mg-Ca-H ternary and Be-P-N-O quaternary systems by exploring nearly 10 million configurations, demonstrating substantial speedup compared to first-principles calculations. These results underscore the effectiveness of our approach in accelerating the exploration and discovery of complex multi-component functional materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"67 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：