Pub Date : 2026-01-13DOI: 10.1038/s41524-025-01939-1
William Thornley, Sam Sullivan-Allsop, Rongsheng Cai, Nick Clark, Roman Gorbachev, Sarah J. Haigh
The Noise2Void technique is demonstrated for successful denoising of atomic resolution scanning transmission electron microscopy (STEM) images. The technique is applied to denoising atomic resolution images and videos of gold adatoms on a graphene surface within a graphene liquid-cell, with the denoised experimental data qualitatively demonstrating improved visibility of both the Au adatoms and the graphene lattice. The denoising performance is quantified by comparison to similar simulated data and the approach is found to significantly outperform both total variation and simple Gaussian blurring. Compared to other denoising methods, the Noise2Void technique has the combined advantages that it requires no manual intervention during training or denoising, no prior knowledge of the sample and is compatible with real-time data acquisition rates of at least 45 frames per second.
{"title":"Noise2Void for denoising atomic resolution scanning transmission electron microscopy images","authors":"William Thornley, Sam Sullivan-Allsop, Rongsheng Cai, Nick Clark, Roman Gorbachev, Sarah J. Haigh","doi":"10.1038/s41524-025-01939-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01939-1","url":null,"abstract":"The Noise2Void technique is demonstrated for successful denoising of atomic resolution scanning transmission electron microscopy (STEM) images. The technique is applied to denoising atomic resolution images and videos of gold adatoms on a graphene surface within a graphene liquid-cell, with the denoised experimental data qualitatively demonstrating improved visibility of both the Au adatoms and the graphene lattice. The denoising performance is quantified by comparison to similar simulated data and the approach is found to significantly outperform both total variation and simple Gaussian blurring. Compared to other denoising methods, the Noise2Void technique has the combined advantages that it requires no manual intervention during training or denoising, no prior knowledge of the sample and is compatible with real-time data acquisition rates of at least 45 frames per second.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956349","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 : 2026-01-13DOI: 10.1038/s41524-025-01952-4
Bilvin Varughese, Troy D. Loeffler, Suvo Banik, Aditya Koneru, Sukriti Manna, Karthik Balasubramanian, Rohit Batra, Mathew J. Cherukara, Orcun Yildiz, Tom Peterka, Bobby G. Sumpter, Subramanian K.R.S. Sankaranarayanan
The development of next-generation molecular simulation models requires moving beyond predefined functional forms toward machine learning (ML) techniques that directly capture multiscale physics. Here, we demonstrate such an approach using symbolic regression (SR) with equation learner networks and a reinforcement learning search engine to derive interpretable equations for interatomic interactions. Training data were generated through nested ensemble sampling with density functional theory (DFT) energetics, spanning crystalline to highly disordered states. The optimization of the learner network employed continuous-action Monte Carlo Tree Search (MCTS) combined with gradient descent, enabling efficient exploration of function space. For copper as a representative transition metal, an unconstrained search produced models that outperformed fixed-form Sutton–Chen EAM potentials. The SR-derived models (SR1 and SR2) reproduced key material properties—lattice constants, cohesive energies, equations of state, elastic constants, phonon dispersion, defect formation energies, surface/bulk energetics, and phase transformation with significantly improved accuracy. Furthermore, stringent melting simulations using two-phase solid-amorphous interfaces confirmed that SR models accurately capture the interplay of vibrational entropy, cohesive energy, and structural dynamics, surpassing SC-EAM in both qualitative and quantitative predictions. This highlights the potential of SR to deliver fast, accurate, flexible, and physically meaningful potentials, advancing predictive modeling across scales.
{"title":"Physically interpretable interatomic potentials via symbolic regression and reinforcement learning","authors":"Bilvin Varughese, Troy D. Loeffler, Suvo Banik, Aditya Koneru, Sukriti Manna, Karthik Balasubramanian, Rohit Batra, Mathew J. Cherukara, Orcun Yildiz, Tom Peterka, Bobby G. Sumpter, Subramanian K.R.S. Sankaranarayanan","doi":"10.1038/s41524-025-01952-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01952-4","url":null,"abstract":"The development of next-generation molecular simulation models requires moving beyond predefined functional forms toward machine learning (ML) techniques that directly capture multiscale physics. Here, we demonstrate such an approach using symbolic regression (SR) with equation learner networks and a reinforcement learning search engine to derive interpretable equations for interatomic interactions. Training data were generated through nested ensemble sampling with density functional theory (DFT) energetics, spanning crystalline to highly disordered states. The optimization of the learner network employed continuous-action Monte Carlo Tree Search (MCTS) combined with gradient descent, enabling efficient exploration of function space. For copper as a representative transition metal, an unconstrained search produced models that outperformed fixed-form Sutton–Chen EAM potentials. The SR-derived models (SR1 and SR2) reproduced key material properties—lattice constants, cohesive energies, equations of state, elastic constants, phonon dispersion, defect formation energies, surface/bulk energetics, and phase transformation with significantly improved accuracy. Furthermore, stringent melting simulations using two-phase solid-amorphous interfaces confirmed that SR models accurately capture the interplay of vibrational entropy, cohesive energy, and structural dynamics, surpassing SC-EAM in both qualitative and quantitative predictions. This highlights the potential of SR to deliver fast, accurate, flexible, and physically meaningful potentials, advancing predictive modeling across scales.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"52 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956350","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}
In this article, we present a first-principles field-effect transistors (FETs) contact study based on density functional theory and the non-equilibrium Green’s function method. We estimate device performance for three transition-metal-dichalcogenide (TMD) channel materials (WSe 2 , WS 2 , and MoS 2 ), including metal contacts (Ni) at source and drain for the first time. The results show that the variation in Rc has less impact on ION and IOFF at a given V DD than the variation in subthreshold swing ( SS ; with differences exceeding 30 mV/dec), suggesting SS may be more sensitive to the contacting material choice than previously realized at gate lengths below 15 nm. Among the channel and contact material combinations studied, Ni/WSe 2 FET leads to the best short-channel device performance. The quantum transport calculation shows the highest density of charge accumulation at the Ni/WSe 2 contact edge. Inspired by this first-principles study, we performed X-ray photoelectron spectroscopy and verified the bonding strength at the Ni/WSe 2 contact to be stronger than Ni/WS 2 and Ni/MoS 2 contacts. This supports the theoretical finding that the contact/channel materials need to be chosen to optimize SS and ION in short-channel TMD FETs.
{"title":"Investigating contact-limited scaling in sub-15-nm TMD FETs from first-principles","authors":"Kuan-Bo Lin, Hui-Ting Liu, Shin-Yuan Wang, Shu-Jui Chang, Chao-Cheng Kaun, Chenming Hu","doi":"10.1038/s41524-025-01947-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01947-1","url":null,"abstract":"In this article, we present a first-principles field-effect transistors (FETs) contact study based on density functional theory and the non-equilibrium Green’s function method. We estimate device performance for three transition-metal-dichalcogenide (TMD) channel materials (WSe <jats:sub>2</jats:sub> , WS <jats:sub>2</jats:sub> , and MoS <jats:sub>2</jats:sub> ), including metal contacts (Ni) at source and drain for the first time. The results show that the variation in <jats:italic>R</jats:italic> <jats:sub> <jats:italic>c</jats:italic> </jats:sub> has less impact on <jats:italic>I</jats:italic> <jats:sub> <jats:italic>ON</jats:italic> </jats:sub> and <jats:italic>I</jats:italic> <jats:sub> <jats:italic>OFF</jats:italic> </jats:sub> at a given V <jats:sub>DD</jats:sub> than the variation in subthreshold swing ( <jats:italic>SS</jats:italic> ; with differences exceeding 30 mV/dec), suggesting <jats:italic>SS</jats:italic> may be more sensitive to the contacting material choice than previously realized at gate lengths below 15 nm. Among the channel and contact material combinations studied, Ni/WSe <jats:sub>2</jats:sub> FET leads to the best short-channel device performance. The quantum transport calculation shows the highest density of charge accumulation at the Ni/WSe <jats:sub>2</jats:sub> contact edge. Inspired by this first-principles study, we performed X-ray photoelectron spectroscopy and verified the bonding strength at the Ni/WSe <jats:sub>2</jats:sub> contact to be stronger than Ni/WS <jats:sub>2</jats:sub> and Ni/MoS <jats:sub>2</jats:sub> contacts. This supports the theoretical finding that the contact/channel materials need to be chosen to optimize <jats:italic>SS</jats:italic> and <jats:italic>I</jats:italic> <jats:sub> <jats:italic>ON</jats:italic> </jats:sub> in short-channel TMD FETs.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"47 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938273","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 : 2026-01-10DOI: 10.1038/s41524-025-01951-5
Guanshihan Du, Yuanyuan Yao, Linming Zhou, Yuhui Huang, Mohit Tanwani, He Tian, Yu Chen, Kaishi Song, Juan Li, Yunjun Gao, Sujit Das, Yongjun Wu, Lu Chen, Zijian Hong
Ferroelectric oxide superlattices with complex topological structures, such as vortices, skyrmions, and flux-closure domains, have garnered significant attention due to their fascinating properties and wide potential applications. However, progress in this field is often impeded by challenges such as limited data-sharing mechanisms, redundant data generation efforts, high barriers between simulations and experiments, and the underutilization of existing datasets. To address these challenges, we have created the “Polar Topological Structure Toolkit and Database” (PTST). This community-driven repository compiles both standard datasets from high-throughput phase-field simulations and user-submitted nonstandard datasets. The PTST utilizes a Global–Local Transformer (GL-Transformer) to classify polarization states by dividing each sample into spatial sub-blocks and extracting hierarchical features, resulting in ten different polar structure categories. Through the PTST web interface, users can easily retrieve polarization data based on specific parameters or by matching experimental images. Additionally, a Binary Phase Diagram Generator allows users to create strain and electric field phase diagrams within seconds. By providing ready-to-use configurations and integrated machine-learning workflows, PTST significantly reduces computational load, streamlines reproducible research, and promotes deeper insights into ferroelectric topological transitions.
{"title":"PTST: a polar topological structure toolkit and database","authors":"Guanshihan Du, Yuanyuan Yao, Linming Zhou, Yuhui Huang, Mohit Tanwani, He Tian, Yu Chen, Kaishi Song, Juan Li, Yunjun Gao, Sujit Das, Yongjun Wu, Lu Chen, Zijian Hong","doi":"10.1038/s41524-025-01951-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01951-5","url":null,"abstract":"Ferroelectric oxide superlattices with complex topological structures, such as vortices, skyrmions, and flux-closure domains, have garnered significant attention due to their fascinating properties and wide potential applications. However, progress in this field is often impeded by challenges such as limited data-sharing mechanisms, redundant data generation efforts, high barriers between simulations and experiments, and the underutilization of existing datasets. To address these challenges, we have created the “Polar Topological Structure Toolkit and Database” (PTST). This community-driven repository compiles both standard datasets from high-throughput phase-field simulations and user-submitted nonstandard datasets. The PTST utilizes a Global–Local Transformer (GL-Transformer) to classify polarization states by dividing each sample into spatial sub-blocks and extracting hierarchical features, resulting in ten different polar structure categories. Through the PTST web interface, users can easily retrieve polarization data based on specific parameters or by matching experimental images. Additionally, a Binary Phase Diagram Generator allows users to create strain and electric field phase diagrams within seconds. By providing ready-to-use configurations and integrated machine-learning workflows, PTST significantly reduces computational load, streamlines reproducible research, and promotes deeper insights into ferroelectric topological transitions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"39 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145947726","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 : 2026-01-10DOI: 10.1038/s41524-025-01855-4
Max Großmann, Marc Thieme, Malte Grunert, Erich Runge
We benchmark many-body perturbation theory against density functional theory (DFT) for the band gaps of solids. We systematically compare four GW variants— G0W0 using the Godby-Needs plasmon-pole approximation ( G0W0 -PPA), full-frequency quasiparticle G0W0 (QP G0W0 ), full-frequency quasiparticle self-consistent GW (QS GW ), and QS GW augmented with vertex corrections in W (QS $$Ghat{W}$$GŴ )—against the currently best-performing and popular density functionals mBJ and HSE06. Our results show that G0W0 -PPA calculations offer only a marginal accuracy gain over the best DFT methods, however, at a higher cost. Replacing the PPA with a full-frequency integration of the dielectric screening improves the predictions dramatically, almost matching the accuracy of the QS $$Ghat{W}$$GŴ . The QS GW removes starting-point bias, but systematically overestimates experimental gaps by about 15%. Adding vertex corrections to the screened Coulomb interaction, i.e., performing a QS $$Ghat{W}$$GŴ calculation, eliminates the overestimation, producing band gaps that are so accurate that they even reliably flag questionable experimental measurements.
我们对固体带隙的多体微扰理论和密度泛函理论（DFT）进行了基准测试。我们系统地比较了四种gw变体-使用goby - needs等离子体极近似（g0w0 - ppa）的g0w0，全频率准粒子g0w0 (qpg0w0)，全频率准粒子自一致gw (QS gw W)，以及在W中增强顶点修正的QS gw (QS $$Ghat{W}$$ gw W) -与目前性能最好和最流行的密度泛函数mBJ和HSE06。我们的研究结果表明，g0 w0 -PPA计算只提供一个边际精度增益比最好的DFT方法，然而，在更高的成本。用电介质屏蔽的全频率集成取代PPA大大提高了预测精度，几乎与QS $$Ghat{W}$$ gw´的精度相匹配。QS gw消除了起点偏差，但系统地高估了实验差距约15%. Adding vertex corrections to the screened Coulomb interaction, i.e., performing a QS $$Ghat{W}$$ G W ̂ calculation, eliminates the overestimation, producing band gaps that are so accurate that they even reliably flag questionable experimental measurements.
{"title":"Many-body perturbation theory vs. density functional theory: a systematic benchmark for band gaps of solids","authors":"Max Großmann, Marc Thieme, Malte Grunert, Erich Runge","doi":"10.1038/s41524-025-01855-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01855-4","url":null,"abstract":"We benchmark many-body perturbation theory against density functional theory (DFT) for the band gaps of solids. We systematically compare four <jats:italic>G</jats:italic> <jats:italic>W</jats:italic> variants— <jats:italic>G</jats:italic> <jats:sub>0</jats:sub> <jats:italic>W</jats:italic> <jats:sub>0</jats:sub> using the Godby-Needs plasmon-pole approximation ( <jats:italic>G</jats:italic> <jats:sub>0</jats:sub> <jats:italic>W</jats:italic> <jats:sub>0</jats:sub> -PPA), full-frequency quasiparticle <jats:italic>G</jats:italic> <jats:sub>0</jats:sub> <jats:italic>W</jats:italic> <jats:sub>0</jats:sub> (QP <jats:italic>G</jats:italic> <jats:sub>0</jats:sub> <jats:italic>W</jats:italic> <jats:sub>0</jats:sub> ), full-frequency quasiparticle self-consistent <jats:italic>G</jats:italic> <jats:italic>W</jats:italic> (QS <jats:italic>G</jats:italic> <jats:italic>W</jats:italic> ), and QS <jats:italic>G</jats:italic> <jats:italic>W</jats:italic> augmented with vertex corrections in <jats:italic>W</jats:italic> (QS <jats:inline-formula> <jats:alternatives> <jats:tex-math>$$Ghat{W}$$</jats:tex-math> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>G</mml:mi> <mml:mover> <mml:mrow> <mml:mi>W</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>̂</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> </mml:math> </jats:alternatives> </jats:inline-formula> )—against the currently best-performing and popular density functionals mBJ and HSE06. Our results show that <jats:italic>G</jats:italic> <jats:sub>0</jats:sub> <jats:italic>W</jats:italic> <jats:sub>0</jats:sub> -PPA calculations offer only a marginal accuracy gain over the best DFT methods, however, at a higher cost. Replacing the PPA with a full-frequency integration of the dielectric screening improves the predictions dramatically, almost matching the accuracy of the QS <jats:inline-formula> <jats:alternatives> <jats:tex-math>$$Ghat{W}$$</jats:tex-math> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>G</mml:mi> <mml:mover> <mml:mrow> <mml:mi>W</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>̂</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> </mml:math> </jats:alternatives> </jats:inline-formula> . The QS <jats:italic>G</jats:italic> <jats:italic>W</jats:italic> removes starting-point bias, but systematically overestimates experimental gaps by about 15%. Adding vertex corrections to the screened Coulomb interaction, i.e., performing a QS <jats:inline-formula> <jats:alternatives> <jats:tex-math>$$Ghat{W}$$</jats:tex-math> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mrow> <mml:mi>G</mml:mi> <mml:mover> <mml:mrow> <mml:mi>W</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>̂</mml:mo> </mml:mrow> </mml:mover> </mml:mrow> </mml:math> </jats:alternatives> </jats:inline-formula> calculation, eliminates the overestimation, producing band gaps that are so accurate that they even reliably flag questionable experimental measurements.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"5 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938271","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 : 2026-01-09DOI: 10.1038/s41524-025-01866-1
Yu-Jie Cen, Sandro Wieser, Georg K. H. Madsen, Jesús Carrete
Nanotubes, with their high aspect ratio and tunable thermal conductivities, are promising nanoscale heat-management components. However, their performance is often constrained by thermal resistance arising from structural defects or interfaces. Here, we examine how structural symmetry influences thermal transport through defect-laden sections. We introduce a framework that integrates representation theory with a mode-resolved Green’s function approach, enabling symmetry-resolved analysis of phonon transmission in quasi-1D systems. To capture the intrinsic symmetries of such systems and avoid artifacts, we employ line-group theory, which introduces quantum numbers that partition phonon branches into symmetry-defined subsets for clearer mode classification. Force constants and phonons are obtained using an Allegro-based machine-learning potential with near-ab initio accuracy. Applying this to single- and multi-wall MoS 2 -WS 2 nanotubes, we link transmission probabilities of individual modes to structural symmetry. Counterintuitively, strong symmetry breaking can enhance heat transport by relaxing selection rules and opening additional transmission channels. Molecular dynamics confirms that this behavior persists even when anharmonicity is considered. The fact that higher disorder introduced through defects can enhance thermal transport, and not just suppress it, demonstrates the critical role of symmetry in deciphering the nuances of nanoscale thermal transport.
{"title":"Ab-initio heat transport in defect-laden quasi-1D systems from a symmetry-adapted perspective","authors":"Yu-Jie Cen, Sandro Wieser, Georg K. H. Madsen, Jesús Carrete","doi":"10.1038/s41524-025-01866-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01866-1","url":null,"abstract":"Nanotubes, with their high aspect ratio and tunable thermal conductivities, are promising nanoscale heat-management components. However, their performance is often constrained by thermal resistance arising from structural defects or interfaces. Here, we examine how structural symmetry influences thermal transport through defect-laden sections. We introduce a framework that integrates representation theory with a mode-resolved Green’s function approach, enabling symmetry-resolved analysis of phonon transmission in quasi-1D systems. To capture the intrinsic symmetries of such systems and avoid artifacts, we employ line-group theory, which introduces quantum numbers that partition phonon branches into symmetry-defined subsets for clearer mode classification. Force constants and phonons are obtained using an Allegro-based machine-learning potential with near-ab initio accuracy. Applying this to single- and multi-wall MoS <jats:sub>2</jats:sub> -WS <jats:sub>2</jats:sub> nanotubes, we link transmission probabilities of individual modes to structural symmetry. Counterintuitively, strong symmetry breaking can enhance heat transport by relaxing selection rules and opening additional transmission channels. Molecular dynamics confirms that this behavior persists even when anharmonicity is considered. The fact that higher disorder introduced through defects can enhance thermal transport, and not just suppress it, demonstrates the critical role of symmetry in deciphering the nuances of nanoscale thermal transport.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"3 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938168","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}
Next-generation fission and fusion reactors impose unprecedented demands on structural materials, requiring simultaneous resistance to high temperatures, high-dose irradiation, and aggressive corrosion. Designing materials that harness the intrinsic properties of multiple elements and their synergistic interactions has emerged as a key strategy to achieve such integrated performance. To guide this design paradigm, a mechanistic understanding of chemically and structurally complex systems is essential. However, such understanding is currently constrained by the lack of high-fidelity interatomic potentials (IAPs) that enable predictive, large-scale atomistic simulations. Here, we employ, for the first time, a multi-task, physics-informed pretraining strategy with the large atomic model (LAM) to systematically evaluate the construction and predictive capability of IAPs for complex nuclear alloy systems. Using Ta-Nb-W-Mo-V as a representative case, the resulting DPA2-5E model—trained solely on the quinary dataset—significantly outperforms conventional machine learning IAPs, demonstrates superior transferability to lower-order subsystems, and accurately reproduces cascade damage and stress-strain behavior. Furthermore, this approach extends to nuclear-relevant structures and corrosive/oxide environments, enabling high-fidelity IAPs and large-scale simulations at reactor extremes.
{"title":"Scaling reliable interatomic potentials to complex nuclear alloys via pretrained atomic models","authors":"Mingxuan Jiang, Biao Xu, Yixin Deng, Shihua Ma, Ji-Jung Kai, Fei Gao, Huiqiu Deng","doi":"10.1038/s41524-025-01950-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01950-6","url":null,"abstract":"Next-generation fission and fusion reactors impose unprecedented demands on structural materials, requiring simultaneous resistance to high temperatures, high-dose irradiation, and aggressive corrosion. Designing materials that harness the intrinsic properties of multiple elements and their synergistic interactions has emerged as a key strategy to achieve such integrated performance. To guide this design paradigm, a mechanistic understanding of chemically and structurally complex systems is essential. However, such understanding is currently constrained by the lack of high-fidelity interatomic potentials (IAPs) that enable predictive, large-scale atomistic simulations. Here, we employ, for the first time, a multi-task, physics-informed pretraining strategy with the large atomic model (LAM) to systematically evaluate the construction and predictive capability of IAPs for complex nuclear alloy systems. Using Ta-Nb-W-Mo-V as a representative case, the resulting DPA2-5E model—trained solely on the quinary dataset—significantly outperforms conventional machine learning IAPs, demonstrates superior transferability to lower-order subsystems, and accurately reproduces cascade damage and stress-strain behavior. Furthermore, this approach extends to nuclear-relevant structures and corrosive/oxide environments, enabling high-fidelity IAPs and large-scale simulations at reactor extremes.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"85 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938277","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 : 2026-01-09DOI: 10.1038/s41524-025-01863-4
Rebecca K. Lindsey, Awwal D. Oladipupo, Sorin Bastea, Bradley A. Steele, I-Feng W. Kuo, Nir Goldman
{"title":"Hierarchical transfer learning: an agile and equitable strategy for machine-learning interatomic models","authors":"Rebecca K. Lindsey, Awwal D. Oladipupo, Sorin Bastea, Bradley A. Steele, I-Feng W. Kuo, Nir Goldman","doi":"10.1038/s41524-025-01863-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01863-4","url":null,"abstract":"","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"82 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145938275","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 : 2026-01-09DOI: 10.1038/s41524-025-01944-4
Jiaxuan Li, Nikita Rybin, Taowei Wang, Alexander Shapeev, Xiaotong Chen, Bing Liu
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