Pub Date : 2026-02-06DOI: 10.1038/s41524-026-01982-6
Fraser Birks, Matthew Nutter, Thomas D. Swinburne, James R. Kermode
Machine-learned interatomic potentials can offer near first-principles accuracy but are computationally expensive, limiting their application to large-scale molecular dynamics simulations. Inspired by quantum mechanics/molecular mechanics methods, we present ML-MIX, a CPU- and GPU-compatible package to accelerate simulations by spatially mixing interatomic potentials of different complexities, allowing deployment of modern MLIPs even under restricted computational budgets. We demonstrate our method for ACE, UF3, SNAP and MACE potential architectures and demonstrate how linear ‘cheap’ potentials can be distilled from a given ‘expensive’ potential, allowing close matching in relevant regions of configuration space. The functionality of ML-MIX is demonstrated through tests on point defects in Si, Fe and W-He, in which speedups of up to 11× over ~8000 atoms are demonstrated, without sacrificing accuracy. The scientific potential of ML-MIX is demonstrated via two case studies in W, measuring the mobility of $$b=frac{1}{2}langle 111rangle$$b=12〈111〉 screw dislocations with ACE/ACE mixing and the implantation of He with MACE/SNAP mixing. The latter returns He reflection coefficients which (for the first time) match experimental observations up to an He incident energy of 80 eV—demonstrating the benefits of deploying state-of-the-art models on large, realistic systems.
{"title":"Efficient and accurate spatial mixing of machine learned interatomic potentials for materials science","authors":"Fraser Birks, Matthew Nutter, Thomas D. Swinburne, James R. Kermode","doi":"10.1038/s41524-026-01982-6","DOIUrl":"https://doi.org/10.1038/s41524-026-01982-6","url":null,"abstract":"Machine-learned interatomic potentials can offer near first-principles accuracy but are computationally expensive, limiting their application to large-scale molecular dynamics simulations. Inspired by quantum mechanics/molecular mechanics methods, we present ML-MIX, a CPU- and GPU-compatible package to accelerate simulations by spatially mixing interatomic potentials of different complexities, allowing deployment of modern MLIPs even under restricted computational budgets. We demonstrate our method for ACE, UF3, SNAP and MACE potential architectures and demonstrate how linear ‘cheap’ potentials can be distilled from a given ‘expensive’ potential, allowing close matching in relevant regions of configuration space. The functionality of ML-MIX is demonstrated through tests on point defects in Si, Fe and W-He, in which speedups of up to 11× over ~8000 atoms are demonstrated, without sacrificing accuracy. The scientific potential of ML-MIX is demonstrated via two case studies in W, measuring the mobility of <jats:inline-formula> <jats:alternatives> <jats:tex-math>$$b=frac{1}{2}langle 111rangle$$</jats:tex-math> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>b</mml:mi> <mml:mo>=</mml:mo> <mml:mfrac> <mml:mrow> <mml:mn>1</mml:mn> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:mfrac> <mml:mo>〈</mml:mo> <mml:mn>111</mml:mn> <mml:mo>〉</mml:mo> </mml:mrow> </mml:math> </jats:alternatives> </jats:inline-formula> screw dislocations with ACE/ACE mixing and the implantation of He with MACE/SNAP mixing. The latter returns He reflection coefficients which (for the first time) match experimental observations up to an He incident energy of 80 eV—demonstrating the benefits of deploying state-of-the-art models on large, realistic systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"59 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135591","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 : 2026-02-06DOI: 10.1038/s41524-026-01985-3
Qichen Xu, Anna Delin
Understanding how complex systems transition between states requires mapping the energy landscape that governs these changes. Local transition-state networks reveal the barrier architecture that explains observed behaviour and enables mechanism-based prediction across computational chemistry, biology, and physics, yet in many practical settings current approaches either require pre-specified endpoints or rely on single-ended searches that provide only a limited sample of nearby saddles. We present a general optimization framework that systematically expands local coverage by coupling a multi-objective explorer with a bilayer minimum-mode kernel. The inner layer uses Hessian-vector products to recover the lowest-curvature subspace, the outer layer optimizes on a reflected force to reach index-1 saddles, then a two-sided descent certifies connectivity. The GPU-based pipeline is portable across autodiff backends and eigensolvers and, on large atomistic-spin tests, matches explicit-Hessian accuracy while cutting peak memory and wall time by orders of magnitude. Applied to a DFT-parameterized Néel-type skyrmionic model, it recovers known routes and reveals previously unreported mechanisms, including meron-antimeron-mediated Néel-type skyrmionic duplication, annihilation, and chiral-droplet formation, enabling up to 32 pathways between biskyrmion ( Q = 2) and biantiskyrmion ( Q = −2). The same core transfers to Cartesian atoms, automatically mapping canonical rearrangements of a Ni(111) heptamer, underscoring the framework’s generality.
{"title":"A general optimization framework for mapping local transition-state networks","authors":"Qichen Xu, Anna Delin","doi":"10.1038/s41524-026-01985-3","DOIUrl":"https://doi.org/10.1038/s41524-026-01985-3","url":null,"abstract":"Understanding how complex systems transition between states requires mapping the energy landscape that governs these changes. Local transition-state networks reveal the barrier architecture that explains observed behaviour and enables mechanism-based prediction across computational chemistry, biology, and physics, yet in many practical settings current approaches either require pre-specified endpoints or rely on single-ended searches that provide only a limited sample of nearby saddles. We present a general optimization framework that systematically expands local coverage by coupling a multi-objective explorer with a bilayer minimum-mode kernel. The inner layer uses Hessian-vector products to recover the lowest-curvature subspace, the outer layer optimizes on a reflected force to reach index-1 saddles, then a two-sided descent certifies connectivity. The GPU-based pipeline is portable across autodiff backends and eigensolvers and, on large atomistic-spin tests, matches explicit-Hessian accuracy while cutting peak memory and wall time by orders of magnitude. Applied to a DFT-parameterized Néel-type skyrmionic model, it recovers known routes and reveals previously unreported mechanisms, including meron-antimeron-mediated Néel-type skyrmionic duplication, annihilation, and chiral-droplet formation, enabling up to 32 pathways between biskyrmion ( <jats:italic>Q</jats:italic> = 2) and biantiskyrmion ( <jats:italic>Q</jats:italic> = −2). The same core transfers to Cartesian atoms, automatically mapping canonical rearrangements of a Ni(111) heptamer, underscoring the framework’s generality.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"9 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135590","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 : 2026-02-05DOI: 10.1038/s41524-025-01893-y
Lars Moreels, Ian Lateur, Diego De Gusem, Jeroen Mulkers, Jonathan Maes, Milorad V. Milošević, Jonathan Leliaert, Bartel Van Waeyenberge
{"title":"mumax+: extensible GPU-accelerated micromagnetics and beyond","authors":"Lars Moreels, Ian Lateur, Diego De Gusem, Jeroen Mulkers, Jonathan Maes, Milorad V. Milošević, Jonathan Leliaert, Bartel Van Waeyenberge","doi":"10.1038/s41524-025-01893-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01893-y","url":null,"abstract":"","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"9 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135526","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 : 2026-02-05DOI: 10.1038/s41524-026-01980-8
Michael R. Tonks, David A. Andersson, Assel Aitkaliyeva
{"title":"Computational design of materials for nuclear reactors","authors":"Michael R. Tonks, David A. Andersson, Assel Aitkaliyeva","doi":"10.1038/s41524-026-01980-8","DOIUrl":"https://doi.org/10.1038/s41524-026-01980-8","url":null,"abstract":"","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"240 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135592","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 : 2026-02-05DOI: 10.1038/s41524-025-01894-x
Yeyong Yu, Xilei Bian, Jie Xiong, Xing Wu, Quan Qian
{"title":"AIMATDESIGN: knowledge-augmented reinforcement learning for inverse materials design under data scarcity","authors":"Yeyong Yu, Xilei Bian, Jie Xiong, Xing Wu, Quan Qian","doi":"10.1038/s41524-025-01894-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01894-x","url":null,"abstract":"","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"16 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135595","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 : 2026-02-05DOI: 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}
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