Pub Date : 2025-12-29DOI: 10.1038/s41524-025-01925-7
Namjung Kim, Dongseok Lee, Jongbin Yu, Sung Woong Cho, Dosung Lee, Yesol Park, Youngjoon Hong
Advances in material functionalities drive innovations across various fields, where metamaterials—defined by structure rather than composition—are leading the way. Despite the rise of artificial intelligence (AI)-driven design strategies, their impact is limited by task-specific retraining, poor out-of-distribution (OOD) generalization, and the need for separate models for forward and inverse design. To address these limitations, we introduce the Metamaterial FOundation Model (MetaFO), a Bayesian transformer-based foundation model inspired by large language models. MetaFO learns the underlying mechanics of metamaterials, enabling probabilistic, zero-shot predictions across diverse, unseen combinations of material properties and structural responses. It also excels in nonlinear inverse design, even under OOD conditions. By treating metamaterials as an operator that maps material properties to structural responses, MetaFO uncovers intricate structure-property relationships and significantly expands the design space. This scalable and generalizable framework marks a paradigm shift in AI-driven metamaterial discovery, paving the way for next-generation innovations.
{"title":"Toward a robust and generalizable metamaterial foundation model","authors":"Namjung Kim, Dongseok Lee, Jongbin Yu, Sung Woong Cho, Dosung Lee, Yesol Park, Youngjoon Hong","doi":"10.1038/s41524-025-01925-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01925-7","url":null,"abstract":"Advances in material functionalities drive innovations across various fields, where metamaterials—defined by structure rather than composition—are leading the way. Despite the rise of artificial intelligence (AI)-driven design strategies, their impact is limited by task-specific retraining, poor out-of-distribution (OOD) generalization, and the need for separate models for forward and inverse design. To address these limitations, we introduce the Metamaterial FOundation Model (MetaFO), a Bayesian transformer-based foundation model inspired by large language models. MetaFO learns the underlying mechanics of metamaterials, enabling probabilistic, zero-shot predictions across diverse, unseen combinations of material properties and structural responses. It also excels in nonlinear inverse design, even under OOD conditions. By treating metamaterials as an operator that maps material properties to structural responses, MetaFO uncovers intricate structure-property relationships and significantly expands the design space. This scalable and generalizable framework marks a paradigm shift in AI-driven metamaterial discovery, paving the way for next-generation innovations.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"36 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895481","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}
Sintered neodymium-iron-boron (NdFeB) magnets are indispensable in high-performance applications, but their optimization is challenged by complex structure-property relationships and limited data. In this work, we curate the first multi-domain database for this system (1994 industrial and academic samples) and systematically evaluate active learning (AL) strategies on classical and quantum-enhanced regressors. First, our “domain-aware” analysis reveals quantitative differences in design heuristics between industrial and academic data. Second, we present a methodological blueprint for integrating quantum kernel regression into an AL framework using a bootstrapped ensemble for uncertainty quantification. Finally, and most significantly, our results reveal AL effectiveness is strongly model-dependent. Its advantage ranges from significant acceleration (Random Forest, SVR) to being diminished (XGBoost), or even inverted—proving detrimental compared to random sampling—as shown in our quantum-enhanced SVR case study. This finding provides critical new insights for the strategic application of machine learning in materials discovery.
{"title":"A framework of active data selection and quantum-enhanced regression for predicting magnetic properties of sintered NdFeB magnets","authors":"Lianhua He, Qichao Liang, Kaifan Pan, Tianyan Li, Qiang Ma, Xin Wang, Haibo Xu, Yingjin Ma","doi":"10.1038/s41524-025-01914-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01914-w","url":null,"abstract":"Sintered neodymium-iron-boron (NdFeB) magnets are indispensable in high-performance applications, but their optimization is challenged by complex structure-property relationships and limited data. In this work, we curate the first multi-domain database for this system (1994 industrial and academic samples) and systematically evaluate active learning (AL) strategies on classical and quantum-enhanced regressors. First, our “domain-aware” analysis reveals quantitative differences in design heuristics between industrial and academic data. Second, we present a methodological blueprint for integrating quantum kernel regression into an AL framework using a bootstrapped ensemble for uncertainty quantification. Finally, and most significantly, our results reveal AL effectiveness is strongly model-dependent. Its advantage ranges from significant acceleration (Random Forest, SVR) to being diminished (XGBoost), or even inverted—proving detrimental compared to random sampling—as shown in our quantum-enhanced SVR case study. This finding provides critical new insights for the strategic application of machine learning in materials discovery.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"24 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145895519","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}
Theoretical simulation of phase change materials such as Ge-Sb-Te has suffered from two methodological issues. On the one hand, there is a lack of efficient band gap correction method for density functional theory that is suitable for these materials in both crystalline and amorphous phases, while maintaining the computational complexity comparable to local density approximation. On the other hand, analysis of the coordination number in amorphous phases relies on an integration involving the radial distribution function, which adds to the complexity. In this work, we find that the shell DFT-1/2 method offers overall band gap accuracy for phase-change materials comparable to that of the HSE06 hybrid functional, while its computational cost is orders of magnitude lower. Moreover, the mixed length-angle coordination number theory enables calculating the coordination numbers in the amorphous phase directly from the structure, with definite outcomes. The two methodologies could be helpful for high-throughput simulations of phase change materials.
{"title":"High-efficiency computational methodologies for electronic properties and structural characterization of Ge-Sb-Te based phase-change materials","authors":"Shanzhong Xie, Kan-Hao Xue, Shaojie Yuan, Zijian Zhou, Shengxin Yang, Heng Yu, Rongchuan Gu, Ming Xu, Xiangshui Miao","doi":"10.1038/s41524-025-01922-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01922-w","url":null,"abstract":"Theoretical simulation of phase change materials such as Ge-Sb-Te has suffered from two methodological issues. On the one hand, there is a lack of efficient band gap correction method for density functional theory that is suitable for these materials in both crystalline and amorphous phases, while maintaining the computational complexity comparable to local density approximation. On the other hand, analysis of the coordination number in amorphous phases relies on an integration involving the radial distribution function, which adds to the complexity. In this work, we find that the shell DFT-1/2 method offers overall band gap accuracy for phase-change materials comparable to that of the HSE06 hybrid functional, while its computational cost is orders of magnitude lower. Moreover, the mixed length-angle coordination number theory enables calculating the coordination numbers in the amorphous phase directly from the structure, with definite outcomes. The two methodologies could be helpful for high-throughput simulations of phase change materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"86 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893799","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}
With the rapid advancement of AI technologies, generative models have been increasingly employed in the exploration of novel materials. By integrating traditional computational approaches such as density functional theory (DFT) and molecular dynamics (MD), existing generative models — including diffusion models and autoregressive models — have demonstrated remarkable potential in the discovery of novel materials. However, their efficiency in goal-directed materials design remains suboptimal. In this work we developed a highly transferable, efficient and robust conditional generation framework, PODGen, by integrating a general generative model with multiple property prediction models. Based on PODGen, we designed a workflow for the high-throughput crystals conditional generation which is used to search new topological insulators (TIs). Our results show that the success rate of generating TIs using our framework is approximately 5 times higher than that of the unconstrained approach. This demonstrates that conditional generation significantly enhances the efficiency of targeted material discovery. Using this method, we generated tens of thousands of new topological materials and conducted further first-principles calculations on those with promising application potential. Furthermore, we identified promising, synthesizable topological (crystalline) insulators such as CsHgSb, NaLaB12, Bi4Sb2Se3, Be3Ta2Si and Be2W.
{"title":"Materials discovery acceleration by using conditional generative methodology","authors":"Caiyuan Ye, Yuzhi Wang, Xintian Xie, Tiannian Zhu, Jiaxuan Liu, Yuqing He, Lili Zhang, Junwei Zhang, Zhong Fang, Lei Wang, Zhipan Liu, Hongming Weng, Quansheng Wu","doi":"10.1038/s41524-025-01930-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01930-w","url":null,"abstract":"With the rapid advancement of AI technologies, generative models have been increasingly employed in the exploration of novel materials. By integrating traditional computational approaches such as density functional theory (DFT) and molecular dynamics (MD), existing generative models — including diffusion models and autoregressive models — have demonstrated remarkable potential in the discovery of novel materials. However, their efficiency in goal-directed materials design remains suboptimal. In this work we developed a highly transferable, efficient and robust conditional generation framework, PODGen, by integrating a general generative model with multiple property prediction models. Based on PODGen, we designed a workflow for the high-throughput crystals conditional generation which is used to search new topological insulators (TIs). Our results show that the success rate of generating TIs using our framework is approximately 5 times higher than that of the unconstrained approach. This demonstrates that conditional generation significantly enhances the efficiency of targeted material discovery. Using this method, we generated tens of thousands of new topological materials and conducted further first-principles calculations on those with promising application potential. Furthermore, we identified promising, synthesizable topological (crystalline) insulators such as CsHgSb, NaLaB12, Bi4Sb2Se3, Be3Ta2Si and Be2W.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"11 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893800","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 : 2025-12-24DOI: 10.1038/s41524-025-01920-y
Zhi Li, Huiju Lee, Chris Wolverton, Yi Xia
Accurate first-principles prediction of lattice thermal conductivity (κL) remains challenging in identifying materials with extreme thermal behavior. While the harmonic approximation with three-phonon scattering (HA + 3ph) is now routine, reliable κL prediction often requires higher-order anharmonic effects, including self-consistent phonon renormalization, three- and four-phonon scattering, and off-diagonal heat flux (SCPH + 3, 4ph + OD). We present a state-of-the-art high-throughput workflow that unifies these effects and apply it to 773 cubic and tetragonal crystals spanning diverse chemistries and structures. From 562 dynamically stable compounds, we assess the hierarchical impacts of higher-order anharmonicity. For around 60% of materials, HA + 3ph predictions closely match those from SCPH + 3, 4ph + OD. SCPH generally increases κL, by over 8 times in extreme cases, whereas four-phonon scattering universally suppresses κL, sometimes to 15% of the HA + 3ph value. Off-diagonal contributions are negligible in high-κL systems but can rival diagonal terms in highly anharmonic low-κL compounds. We highlight four case studies, Rb2TlAlH6, Cu3VSe4, CuBr, and KTlCl4, that exhibit distinct extreme behaviors. This work delivers not only a robust workflow for high-fidelity κL dataset but also a quantitative framework to determine when higher-order effects are essential. The hierarchy of κL results, from the HA + 3ph to SCPH + 3, 4ph + OD level, offers a scalable, interpretable route to discovering next-generation extreme thermal materials.
{"title":"High-throughput computational framework for high-order anharmonic thermal transport in cubic and tetragonal crystals","authors":"Zhi Li, Huiju Lee, Chris Wolverton, Yi Xia","doi":"10.1038/s41524-025-01920-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01920-y","url":null,"abstract":"Accurate first-principles prediction of lattice thermal conductivity (κL) remains challenging in identifying materials with extreme thermal behavior. While the harmonic approximation with three-phonon scattering (HA + 3ph) is now routine, reliable κL prediction often requires higher-order anharmonic effects, including self-consistent phonon renormalization, three- and four-phonon scattering, and off-diagonal heat flux (SCPH + 3, 4ph + OD). We present a state-of-the-art high-throughput workflow that unifies these effects and apply it to 773 cubic and tetragonal crystals spanning diverse chemistries and structures. From 562 dynamically stable compounds, we assess the hierarchical impacts of higher-order anharmonicity. For around 60% of materials, HA + 3ph predictions closely match those from SCPH + 3, 4ph + OD. SCPH generally increases κL, by over 8 times in extreme cases, whereas four-phonon scattering universally suppresses κL, sometimes to 15% of the HA + 3ph value. Off-diagonal contributions are negligible in high-κL systems but can rival diagonal terms in highly anharmonic low-κL compounds. We highlight four case studies, Rb2TlAlH6, Cu3VSe4, CuBr, and KTlCl4, that exhibit distinct extreme behaviors. This work delivers not only a robust workflow for high-fidelity κL dataset but also a quantitative framework to determine when higher-order effects are essential. The hierarchy of κL results, from the HA + 3ph to SCPH + 3, 4ph + OD level, offers a scalable, interpretable route to discovering next-generation extreme thermal materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"8 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145814067","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 : 2025-12-24DOI: 10.1038/s41524-025-01917-7
Yunhua Lu, Mingyue Chen, Qingwei Zhang, Junan Zhang, Chao Zhang, Shiai Xu, Qiuyan Bi
The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties. For boron-doped graphene (BDG), both the band gap and work function critically influence performance in electronic and catalytic applications, yet existing machine learning (ML) approaches typically focus on single-property prediction and rely on hand-crafted features, limiting their generality. Here we present an adaptive edge-aware graph convolutional neural network with multi-task learning (AEGCNN-MTL) for simultaneous prediction of multiple material properties. On a DFT-computed BDG dataset of 2613 structures, AEGCNN-MTL achieved high accuracy (R² = 0.9905 for band gap and 0.9778 for work function), and under identical training budgets, outperformed representative single-task GNN baselines. When transferred to the QM9 benchmark, the framework delivered competitive performance across 12 diverse quantum chemical properties, demonstrating strong generalization capability. These results highlight the potential of AEGCNN-MTL as a scalable and accurate tool for high-throughput, multi-property screening and the data-driven discovery of multifunctional materials.
{"title":"Adaptive edge-aware graph convolutional with multi-task learning for simultaneous prediction of material properties","authors":"Yunhua Lu, Mingyue Chen, Qingwei Zhang, Junan Zhang, Chao Zhang, Shiai Xu, Qiuyan Bi","doi":"10.1038/s41524-025-01917-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01917-7","url":null,"abstract":"The targeted design of functional materials often requires the concurrent optimization of multiple interdependent properties. For boron-doped graphene (BDG), both the band gap and work function critically influence performance in electronic and catalytic applications, yet existing machine learning (ML) approaches typically focus on single-property prediction and rely on hand-crafted features, limiting their generality. Here we present an adaptive edge-aware graph convolutional neural network with multi-task learning (AEGCNN-MTL) for simultaneous prediction of multiple material properties. On a DFT-computed BDG dataset of 2613 structures, AEGCNN-MTL achieved high accuracy (R² = 0.9905 for band gap and 0.9778 for work function), and under identical training budgets, outperformed representative single-task GNN baselines. When transferred to the QM9 benchmark, the framework delivered competitive performance across 12 diverse quantum chemical properties, demonstrating strong generalization capability. These results highlight the potential of AEGCNN-MTL as a scalable and accurate tool for high-throughput, multi-property screening and the data-driven discovery of multifunctional materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"28 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145814066","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}
Point defects in solid-state quantum systems are vital for enabling single-photon emission at specific wavelengths, making their precise identification essential for advancing applications in quantum technologies. However, pinpointing the microscopic origins of these defects remains a challenge. In this work, we propose Raman spectroscopy as a robust strategy for defect identification. Using density functional theory, we characterize the Raman signatures of 100 defects in hexagonal boron nitride (hBN) spanning periodic groups III to VI, encompassing around 30,000 phonon modes. Our findings reveal that the local atomic environment plays a pivotal role in shaping the Raman lineshape. Furthermore, we demonstrate that Raman spectroscopy can differentiate defects based on their spin and charge states as well as strain-induced variations. The ability to resolve spin configurations offers a pathway to identifying defects with spins suitable for quantum sensing. Finally, an experimental concept using tip-enhanced Raman spectroscopy has been proposed in this work. Therefore, this study not only provides a comprehensive theoretical database of Raman spectra for hBN defects but also establishes a novel experimental framework to identify point defects. More broadly, our approach offers a universal method for defect identification in any quantum materials with spin configurations specific to any quantum application.
{"title":"Raman signatures of single point defects in hexagonal boron nitride quantum emitters","authors":"Chanaprom Cholsuk, Aslí Çakan, Volker Deckert, Sujin Suwanna, Tobias Vogl","doi":"10.1038/s41524-025-01921-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01921-x","url":null,"abstract":"Point defects in solid-state quantum systems are vital for enabling single-photon emission at specific wavelengths, making their precise identification essential for advancing applications in quantum technologies. However, pinpointing the microscopic origins of these defects remains a challenge. In this work, we propose Raman spectroscopy as a robust strategy for defect identification. Using density functional theory, we characterize the Raman signatures of 100 defects in hexagonal boron nitride (hBN) spanning periodic groups III to VI, encompassing around 30,000 phonon modes. Our findings reveal that the local atomic environment plays a pivotal role in shaping the Raman lineshape. Furthermore, we demonstrate that Raman spectroscopy can differentiate defects based on their spin and charge states as well as strain-induced variations. The ability to resolve spin configurations offers a pathway to identifying defects with spins suitable for quantum sensing. Finally, an experimental concept using tip-enhanced Raman spectroscopy has been proposed in this work. Therefore, this study not only provides a comprehensive theoretical database of Raman spectra for hBN defects but also establishes a novel experimental framework to identify point defects. More broadly, our approach offers a universal method for defect identification in any quantum materials with spin configurations specific to any quantum application.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"172 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145808141","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 : 2025-12-22DOI: 10.1038/s41524-025-01911-z
Peichen Zhong, Dongjin Kim, Daniel S. King, Bingqing Cheng
Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and scalable alternative to quantum mechanical methods, but do not by themselves incorporate electrical response. Here, we show that polarization and Born effective charge (BEC) tensors can be directly extracted from long-range MLIPs within the Latent Ewald Summation (LES) framework, solely by learning from energy and force data. Using this approach, we predict the infrared spectra of bulk water under zero or finite external electric fields, ionic conductivities of high-pressure superionic ice, and the phase transition and hysteresis in ferroelectric PbTiO3 perovskite. This work thus extends the capability of MLIPs to predict electrical response –without training on charges or polarization or BECs– and enables accurate modeling of electric-field-driven processes in diverse systems at scale.
{"title":"Machine learning interatomic potential can infer electrical response","authors":"Peichen Zhong, Dongjin Kim, Daniel S. King, Bingqing Cheng","doi":"10.1038/s41524-025-01911-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01911-z","url":null,"abstract":"Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and scalable alternative to quantum mechanical methods, but do not by themselves incorporate electrical response. Here, we show that polarization and Born effective charge (BEC) tensors can be directly extracted from long-range MLIPs within the Latent Ewald Summation (LES) framework, solely by learning from energy and force data. Using this approach, we predict the infrared spectra of bulk water under zero or finite external electric fields, ionic conductivities of high-pressure superionic ice, and the phase transition and hysteresis in ferroelectric PbTiO3 perovskite. This work thus extends the capability of MLIPs to predict electrical response –without training on charges or polarization or BECs– and enables accurate modeling of electric-field-driven processes in diverse systems at scale.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"114 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145801599","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 : 2025-12-20DOI: 10.1038/s41524-025-01892-z
Augusto L. Araújo, Pedro H. Sophia, F. Crasto de Lima, Adalberto Fazzio
Twisted two-dimensional van der Waals heterostructures provide a fertile ground for tailoring electronic and structural properties. However, their vast configurational space poses challenges for systematic study. Here, we introduce SAMBA, an open-source, high-throughput Python workflow that automates the generation, simulation, and analysis of twisted bilayers. Using the coincidence lattice method, we generate a comprehensive set of over 18,000 quasi-commensurable homo- and heterobilayer structures based on 63 experimentally reported monolayers, and perform DFT simulations on a growing subset. The resulting database includes symmetry, interlayer energetics, band alignment, and charge transfer. A detailed case study on graphene-jacutingaite illustrates the framework’s capabilities. This platform offers a robust foundation for data-driven discovery and the rational design of 2D materials with tunable properties.
{"title":"A high-throughput framework and database for twisted 2D van der Waals bilayers","authors":"Augusto L. Araújo, Pedro H. Sophia, F. Crasto de Lima, Adalberto Fazzio","doi":"10.1038/s41524-025-01892-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01892-z","url":null,"abstract":"Twisted two-dimensional van der Waals heterostructures provide a fertile ground for tailoring electronic and structural properties. However, their vast configurational space poses challenges for systematic study. Here, we introduce SAMBA, an open-source, high-throughput Python workflow that automates the generation, simulation, and analysis of twisted bilayers. Using the coincidence lattice method, we generate a comprehensive set of over 18,000 quasi-commensurable homo- and heterobilayer structures based on 63 experimentally reported monolayers, and perform DFT simulations on a growing subset. The resulting database includes symmetry, interlayer energetics, band alignment, and charge transfer. A detailed case study on graphene-jacutingaite illustrates the framework’s capabilities. This platform offers a robust foundation for data-driven discovery and the rational design of 2D materials with tunable properties.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"13 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796459","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 : 2025-12-20DOI: 10.1038/s41524-025-01874-1
Axel Forslund, Jong Hyun Jung, Yuji Ikeda, Blazej Grabowski
We propose a free-energy-perturbation approach accelerated by machine-learning potentials to efficiently compute transition temperatures and entropies for all rungs of Jacob’s ladder. We apply the approach to the dynamically stabilized phases of SiO2, which are characterized by challengingly small transition entropies. All investigated functionals from rungs 1–4 fail to predict an accurate transition temperature by 25–200%. Only by ascending to the fifth rung, within the random phase approximation, an accurate prediction is possible, giving a relative error of 5%. We provide a clear-cut procedure and relevant data to the community for, e.g., developing and evaluating new functionals.
{"title":"Free-energy perturbation in the exchange-correlation space accelerated by machine learning: application to silica polymorphs","authors":"Axel Forslund, Jong Hyun Jung, Yuji Ikeda, Blazej Grabowski","doi":"10.1038/s41524-025-01874-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01874-1","url":null,"abstract":"We propose a free-energy-perturbation approach accelerated by machine-learning potentials to efficiently compute transition temperatures and entropies for all rungs of Jacob’s ladder. We apply the approach to the dynamically stabilized phases of SiO2, which are characterized by challengingly small transition entropies. All investigated functionals from rungs 1–4 fail to predict an accurate transition temperature by 25–200%. Only by ascending to the fifth rung, within the random phase approximation, an accurate prediction is possible, giving a relative error of 5%. We provide a clear-cut procedure and relevant data to the community for, e.g., developing and evaluating new functionals.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"85 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796461","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：