Pub Date : 2026-02-05DOI: 10.1038/s41524-026-01989-z
Nico Unglert, Michael Ketter, Georg K. H. Madsen
Accurate prediction of materials phase diagrams from first principles remains a central challenge in computational materials science. Machine-learning interatomic potentials can provide near-DFT accuracy at a fraction of the cost, but their reliability crucially depends on the availability of representative training data that span all relevant regions of the potential-energy surface. Here, we present a fully automated active-learning (AL) strategy based on replica-exchange nested sampling (RENS) for the generation of training data and the computation of complete pressure-temperature phase diagrams. In our framework, RENS acts as both the exploration engine and the acquisition mechanism: its intrinsic diversity and likelihood-constrained sampling ensure that the configurations selected for DFT labeling are both informative and thermodynamically representative. We apply the approach to silicon, germanium, and titanium using potentials trained at the r2SCAN level of theory. For all systems, the AL process converges within ~ 10–15 iterations, yielding transferable potentials that reproduce known phase transitions and thermodynamic trends. These results demonstrate that RENS-based AL provides a general and autonomous route to constructing machine-learning interatomic potentials and predicting first-principles phase diagrams across broad thermodynamic conditions.
{"title":"Active learning potentials for first-principles phase diagrams using replica-exchange nested sampling","authors":"Nico Unglert, Michael Ketter, Georg K. H. Madsen","doi":"10.1038/s41524-026-01989-z","DOIUrl":"https://doi.org/10.1038/s41524-026-01989-z","url":null,"abstract":"Accurate prediction of materials phase diagrams from first principles remains a central challenge in computational materials science. Machine-learning interatomic potentials can provide near-DFT accuracy at a fraction of the cost, but their reliability crucially depends on the availability of representative training data that span all relevant regions of the potential-energy surface. Here, we present a fully automated active-learning (AL) strategy based on replica-exchange nested sampling (RENS) for the generation of training data and the computation of complete pressure-temperature phase diagrams. In our framework, RENS acts as both the exploration engine and the acquisition mechanism: its intrinsic diversity and likelihood-constrained sampling ensure that the configurations selected for DFT labeling are both informative and thermodynamically representative. We apply the approach to silicon, germanium, and titanium using potentials trained at the r2SCAN level of theory. For all systems, the AL process converges within ~ 10–15 iterations, yielding transferable potentials that reproduce known phase transitions and thermodynamic trends. These results demonstrate that RENS-based AL provides a general and autonomous route to constructing machine-learning interatomic potentials and predicting first-principles phase diagrams across broad thermodynamic conditions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135594","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 : 2026-02-04DOI: 10.1038/s41524-025-01943-5
Simone Di Cataldo, William Cursio, Lilia Boeri
We critically reexamine the superconducting properties of rock-salt transition-metal carbides (TMCs), often regarded as textbook conventional superconductors, combining first-principles electron-phonon calculations with variable-composition evolutionary structure prediction. Studying superconducting trends across the entire transition-metal series, we find that, when the rock-salt stoichiometric phase is dynamically or thermodynamically unstable, carbon-vacant structures identified through unbiased structure prediction permit to reconcile theoretical calculations with experimental trends. Our integrated use of structure prediction and electron-phonon calculations defines a general framework for realistic modeling of superconductors shaped by non-equilibrium synthesis routes and defect tolerance.
{"title":"Vacancy-controlled superconductivity in rock-salt carbides: towards predictive modelling of real-world superconductors","authors":"Simone Di Cataldo, William Cursio, Lilia Boeri","doi":"10.1038/s41524-025-01943-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01943-5","url":null,"abstract":"We critically reexamine the superconducting properties of rock-salt transition-metal carbides (TMCs), often regarded as textbook conventional superconductors, combining first-principles electron-phonon calculations with variable-composition evolutionary structure prediction. Studying superconducting trends across the entire transition-metal series, we find that, when the rock-salt stoichiometric phase is dynamically or thermodynamically unstable, carbon-vacant structures identified through unbiased structure prediction permit to reconcile theoretical calculations with experimental trends. Our integrated use of structure prediction and electron-phonon calculations defines a general framework for realistic modeling of superconductors shaped by non-equilibrium synthesis routes and defect tolerance.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"398 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146115918","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 : 2026-02-03DOI: 10.1038/s41524-025-01936-4
Xing Wang, Edan Bainglass, Miki Bonacci, Andres Ortega-Guerrero, Lorenzo Bastonero, Marnik Bercx, Pietro Bonfà, Roberto De Renzi, Dou Du, Peter N. O. Gillespie, Michael A. Hernández-Bertrán, Daniel Hollas, Sebastiaan P. Huber, Elisa Molinari, Ifeanyi J. Onuorah, Nataliya Paulish, Deborah Prezzi, Junfeng Qiao, Timo Reents, Christopher J. Sewell, Iurii Timrov, Aliaksandr V. Yakutovich, Jusong Yu, Nicola Marzari, Carlo A. Pignedoli, Giovanni Pizzi
Despite the wide availability of density functional theory (DFT) codes, their adoption by the broader materials science community remains limited due to challenges such as software installation, input preparation, high-performance computing setup, and output analysis. To overcome these barriers, we introduce the Quantum ESPRESSO app, an intuitive, web-based platform built on AiiDAlab that integrates user-friendly graphical interfaces with automated DFT workflows. The app employs a modular Input-Process-Output model and a plugin-based architecture, providing predefined computational protocols, automated error handling, and interactive results visualization. We demonstrate the app’s capabilities through plugins for electronic band structures, projected density of states, phonon, infrared/Raman, X-ray and muon spectroscopies, Hubbard parameters (DFT+U+V), Wannier functions, and post-processing tools. By extending the FAIR principles to simulations, workflows, and analyses, the app enhances the accessibility and reproducibility of advanced DFT calculations and provides a general template to interface with other first-principles calculation codes.
{"title":"Making atomistic materials calculations accessible with the AiiDAlab Quantum ESPRESSO app","authors":"Xing Wang, Edan Bainglass, Miki Bonacci, Andres Ortega-Guerrero, Lorenzo Bastonero, Marnik Bercx, Pietro Bonfà, Roberto De Renzi, Dou Du, Peter N. O. Gillespie, Michael A. Hernández-Bertrán, Daniel Hollas, Sebastiaan P. Huber, Elisa Molinari, Ifeanyi J. Onuorah, Nataliya Paulish, Deborah Prezzi, Junfeng Qiao, Timo Reents, Christopher J. Sewell, Iurii Timrov, Aliaksandr V. Yakutovich, Jusong Yu, Nicola Marzari, Carlo A. Pignedoli, Giovanni Pizzi","doi":"10.1038/s41524-025-01936-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01936-4","url":null,"abstract":"Despite the wide availability of density functional theory (DFT) codes, their adoption by the broader materials science community remains limited due to challenges such as software installation, input preparation, high-performance computing setup, and output analysis. To overcome these barriers, we introduce the Quantum ESPRESSO app, an intuitive, web-based platform built on AiiDAlab that integrates user-friendly graphical interfaces with automated DFT workflows. The app employs a modular Input-Process-Output model and a plugin-based architecture, providing predefined computational protocols, automated error handling, and interactive results visualization. We demonstrate the app’s capabilities through plugins for electronic band structures, projected density of states, phonon, infrared/Raman, X-ray and muon spectroscopies, Hubbard parameters (DFT+U+V), Wannier functions, and post-processing tools. By extending the FAIR principles to simulations, workflows, and analyses, the app enhances the accessibility and reproducibility of advanced DFT calculations and provides a general template to interface with other first-principles calculation codes.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"62 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102147","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 : 2026-02-02DOI: 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}
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