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}
Pub Date : 2025-12-20DOI: 10.1038/s41524-025-01919-5
Markus Stricker, Lars Banko, Nik Sarazin, Niklas Siemer, Jan Janssen, Lei Zhang, Jörg Neugebauer, Alfred Ludwig
Computational materials science increasingly benefits from data management, automation, and algorithm-based decision-making for the simulation of material properties and behavior. Experimental materials science also changes rapidly by incorporation of ‘machine learning’ in materials discovery campaigns. The benefits including automation, reproducibility, data provenance, and reusability of managed data, however, are not widely available in the experimental domain. We present an implementation of an Active Learning loop with an interface to an experimental measurement device in pyiron as a demonstrator how to combine experimental and simulated data in one framework. Apart from the acceleration provided through active learning, additional acceleration of the experimental characterization is achieved by using prior knowledge from density functional theory simulations as well as predictions based on text mining using correlations in word embeddings. With data from all domains in the same framework, an untapped potential for the acceleration of materials characterization and materials discovery campaigns becomes available.
{"title":"Computationally accelerated experimental materials characterization—drawing inspiration from high-throughput simulation workflows","authors":"Markus Stricker, Lars Banko, Nik Sarazin, Niklas Siemer, Jan Janssen, Lei Zhang, Jörg Neugebauer, Alfred Ludwig","doi":"10.1038/s41524-025-01919-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01919-5","url":null,"abstract":"Computational materials science increasingly benefits from data management, automation, and algorithm-based decision-making for the simulation of material properties and behavior. Experimental materials science also changes rapidly by incorporation of ‘machine learning’ in materials discovery campaigns. The benefits including automation, reproducibility, data provenance, and reusability of managed data, however, are not widely available in the experimental domain. We present an implementation of an Active Learning loop with an interface to an experimental measurement device in pyiron as a demonstrator how to combine experimental and simulated data in one framework. Apart from the acceleration provided through active learning, additional acceleration of the experimental characterization is achieved by using prior knowledge from density functional theory simulations as well as predictions based on text mining using correlations in word embeddings. With data from all domains in the same framework, an untapped potential for the acceleration of materials characterization and materials discovery campaigns becomes available.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"3 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796466","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 : 2025-12-19DOI: 10.1038/s41524-025-01904-y
Ruoshi Jiang, Fangyuan Gu, Wei Ku
Correlated materials are known to display qualitatively distinct emergent behaviors at low energy. Conveniently, upon absorbing rapid quantum fluctuations, these rich low-energy behaviors can always be effectively described by dressed particles with fully quantized charge, spin, and orbital structure. Such a powerful and simple description is, however, difficult to access through bare particles used in most many-body computations, especially when fluctuations are strong such as in 4d and 5d compounds. To decipher the dominant quantized structure, we propose an easy-to-implement ‘interaction annealing’ approach that utilizes suppressed charge fluctuation through enhancing ionic charging energy. We establish its theoretical foundation using an exactly treated two-site Hubbard model as a generic example. We then demonstrate its applications with more affordable density functional calculations to a representative 3d Mott insulator La2CuO4 and a highly fluctuating 5d semi-metal WTe2. In the latter, it reveals an emergent local electronic structure that makes possible an unprecedented explanation of several experimental observations. Finally, we demonstrate the effectiveness of this approach in studying competing local electronic structures in functional materials.
{"title":"‘Interaction annealing’ to determine effective quantized valence and orbital structure: an illustration with ferro-orbital order in WTe2","authors":"Ruoshi Jiang, Fangyuan Gu, Wei Ku","doi":"10.1038/s41524-025-01904-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01904-y","url":null,"abstract":"Correlated materials are known to display qualitatively distinct emergent behaviors at low energy. Conveniently, upon absorbing rapid quantum fluctuations, these rich low-energy behaviors can always be effectively described by dressed particles with fully quantized charge, spin, and orbital structure. Such a powerful and simple description is, however, difficult to access through bare particles used in most many-body computations, especially when fluctuations are strong such as in 4d and 5d compounds. To decipher the dominant quantized structure, we propose an easy-to-implement ‘interaction annealing’ approach that utilizes suppressed charge fluctuation through enhancing ionic charging energy. We establish its theoretical foundation using an exactly treated two-site Hubbard model as a generic example. We then demonstrate its applications with more affordable density functional calculations to a representative 3d Mott insulator La2CuO4 and a highly fluctuating 5d semi-metal WTe2. In the latter, it reveals an emergent local electronic structure that makes possible an unprecedented explanation of several experimental observations. Finally, we demonstrate the effectiveness of this approach in studying competing local electronic structures in functional materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"3 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796460","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 : 2025-12-19DOI: 10.1038/s41524-025-01906-w
Jian Yao, Zi Wang, Juncheng Wang, Wanchan Yu, Yuxuan Chen, Weifu Li, Jianhui Wei, Yunxing Zhao, Yan Wang, Li Wang, Liming Tan, Lan Huang, Feng Liu, Yong Liu
The traditional design of single-crystal superalloys relies heavily on trial-and-error experimentation, which is time-consuming and costly. Here, we present an intelligent alloy design strategy that integrates natural language processing (NLP) and machine learning (ML). A domain-specific NLP model was developed to automatically extract γ′ solvus temperature data from scientific literature, enabling the construction of a high-quality database. Machine learning models trained on this data accurately predict both γ′ solvus temperature and creep life. Guided by these models, we screened over 340000 virtual compositions and successfully designed a new low-cost alloy, CSU-S1. Experimental validation shows that CSU-S1 achieves a γ′ solvus temperature near 1300 °C and a creep life of 224.7 h at 1100 °C/137 MPa, comparable to third-generation single-crystal superalloys, while using only 3.1 wt% Re and costing just 121 USD/kg. This work not only delivers a high-performance, cost-effective superalloy but also demonstrates a generalizable “knowledge-to-innovation” design paradigm, offering a powerful new route to accelerate the development of advanced engineering materials.
{"title":"Alloy design integrating natural language processing and machine learning: breakthrough development of low-cost, high-performance Ni-based single-crystal superalloys","authors":"Jian Yao, Zi Wang, Juncheng Wang, Wanchan Yu, Yuxuan Chen, Weifu Li, Jianhui Wei, Yunxing Zhao, Yan Wang, Li Wang, Liming Tan, Lan Huang, Feng Liu, Yong Liu","doi":"10.1038/s41524-025-01906-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01906-w","url":null,"abstract":"The traditional design of single-crystal superalloys relies heavily on trial-and-error experimentation, which is time-consuming and costly. Here, we present an intelligent alloy design strategy that integrates natural language processing (NLP) and machine learning (ML). A domain-specific NLP model was developed to automatically extract γ′ solvus temperature data from scientific literature, enabling the construction of a high-quality database. Machine learning models trained on this data accurately predict both γ′ solvus temperature and creep life. Guided by these models, we screened over 340000 virtual compositions and successfully designed a new low-cost alloy, CSU-S1. Experimental validation shows that CSU-S1 achieves a γ′ solvus temperature near 1300 °C and a creep life of 224.7 h at 1100 °C/137 MPa, comparable to third-generation single-crystal superalloys, while using only 3.1 wt% Re and costing just 121 USD/kg. This work not only delivers a high-performance, cost-effective superalloy but also demonstrates a generalizable “knowledge-to-innovation” design paradigm, offering a powerful new route to accelerate the development of advanced engineering materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"173 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796463","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 : 2025-12-18DOI: 10.1038/s41524-025-01912-y
Hui-Shi Yu, Xiao-Sheng Ni, Kun Cao
Understanding magnetoelectric coupling in emerging van der Waals multiferroics is crucial for developing atomically thin spintronic devices. Here, we present a comprehensive first-principles investigation of magnetoelectric coupling in orthorhombic CrI2. Monte Carlo simulations based on DFT-calculated magnetic exchange interactions suggest a proper-screw helimagnetic ground state with a Néel temperature consistent with experimental observations. A ferroelectric switching pathway driven by interlayer sliding is predicted, featuring a low switching energy barrier and out-of-plane ferroelectric polarization. To quantitatively characterize the magnetoelectric effect in orthorhombic CrI2 and its microscopic origin, we evaluate the spin-driven polarization using the paramagnetic phase as a reference alongside the magnetoelectric tensor method. The extracted spin-driven polarization aligns along the z-axis, with its origin dominated by the exchange-striction mechanism. Although in-plane components of the total polarization in the bulk vanish due to global symmetry constraints, each CrI2 single layer exhibits local electric polarization along the x direction, arising from the generalized spin-current mechanism, which couples spin chirality to the electric polarization. As a result, we further predict that a proper-screw helimagnetic state may persist in monolayer CrI2, with its charity reversable by switching the in-plane electric polarization through applying external electric field, providing another promising candidate for electrical control of two-dimensional multiferroics.
{"title":"Sliding multiferrocity in van der Waals layered CrI2","authors":"Hui-Shi Yu, Xiao-Sheng Ni, Kun Cao","doi":"10.1038/s41524-025-01912-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01912-y","url":null,"abstract":"Understanding magnetoelectric coupling in emerging van der Waals multiferroics is crucial for developing atomically thin spintronic devices. Here, we present a comprehensive first-principles investigation of magnetoelectric coupling in orthorhombic CrI2. Monte Carlo simulations based on DFT-calculated magnetic exchange interactions suggest a proper-screw helimagnetic ground state with a Néel temperature consistent with experimental observations. A ferroelectric switching pathway driven by interlayer sliding is predicted, featuring a low switching energy barrier and out-of-plane ferroelectric polarization. To quantitatively characterize the magnetoelectric effect in orthorhombic CrI2 and its microscopic origin, we evaluate the spin-driven polarization using the paramagnetic phase as a reference alongside the magnetoelectric tensor method. The extracted spin-driven polarization aligns along the z-axis, with its origin dominated by the exchange-striction mechanism. Although in-plane components of the total polarization in the bulk vanish due to global symmetry constraints, each CrI2 single layer exhibits local electric polarization along the x direction, arising from the generalized spin-current mechanism, which couples spin chirality to the electric polarization. As a result, we further predict that a proper-screw helimagnetic state may persist in monolayer CrI2, with its charity reversable by switching the in-plane electric polarization through applying external electric field, providing another promising candidate for electrical control of two-dimensional multiferroics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"23 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771616","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 : 2025-12-18DOI: 10.1038/s41524-025-01910-0
Aoyan Liang, Nicolas Bertin, Xinran Zhou, Sylvie Aubry, Vasily V. Bulatov
Solid solution strengthening (SSS) is widely used to enhance mechanical properties of metals. Originally developed for dilute alloys, classical SSS theories are presently challenged by the rise of complex concentrated alloys (CCA) with nearly equiatomic compositions. Here, we propose and develop a method of “computational alchemy” in which interatomic interactions are modified to systematically vary two key physical parameters defining SSS - atomic size misfit and elastic stiffness misfit - over a maximally wide range of two misfits. The resulting alchemical alloys are subjected to massive (~108 atoms) molecular dynamics (MD) simulations reproducing full complexity of plastic strength response. At variance with prevailing views, stiffness misfit is observed to contribute to SSS on par if not more than size misfit. Furthermore, depending on exactly how two misfits are combined, they result in synergistic (amplification) or antagonistic (compensation) effect on alloy strengthening. Unlike real CCAs in which each component element comes with its own specific size and stiffness, our alchemical model alloys span the space of two misfits continuously revealing trends in alloy strengthening unrecognized so far. Our study demonstrates unique value of intentionally unrealistic models for gaining deep physical insights into material behaviors that are difficult to reveal otherwise.
{"title":"Computational alchemy clarifies origins of alloy strengthening","authors":"Aoyan Liang, Nicolas Bertin, Xinran Zhou, Sylvie Aubry, Vasily V. Bulatov","doi":"10.1038/s41524-025-01910-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01910-0","url":null,"abstract":"Solid solution strengthening (SSS) is widely used to enhance mechanical properties of metals. Originally developed for dilute alloys, classical SSS theories are presently challenged by the rise of complex concentrated alloys (CCA) with nearly equiatomic compositions. Here, we propose and develop a method of “computational alchemy” in which interatomic interactions are modified to systematically vary two key physical parameters defining SSS - atomic size misfit and elastic stiffness misfit - over a maximally wide range of two misfits. The resulting alchemical alloys are subjected to massive (~108 atoms) molecular dynamics (MD) simulations reproducing full complexity of plastic strength response. At variance with prevailing views, stiffness misfit is observed to contribute to SSS on par if not more than size misfit. Furthermore, depending on exactly how two misfits are combined, they result in synergistic (amplification) or antagonistic (compensation) effect on alloy strengthening. Unlike real CCAs in which each component element comes with its own specific size and stiffness, our alchemical model alloys span the space of two misfits continuously revealing trends in alloy strengthening unrecognized so far. Our study demonstrates unique value of intentionally unrealistic models for gaining deep physical insights into material behaviors that are difficult to reveal otherwise.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"82 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145771623","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 : 2025-12-17DOI: 10.1038/s41524-025-01905-x
Kai Liu, Zixiong Wei, Wei Gao, Poulumi Dey, Marcel H. F. Sluiter, Fei Shuang
Universal machine-learning interatomic potentials (uMLIPs) are emerging as foundation models for atomistic simulation, offering near-ab initio accuracy at far lower cost. Their safe, broad deployment is limited by the absence of reliable, general uncertainty estimates. We present a unified, scalable uncertainty metric, U, built from a heterogeneous ensemble that reuses existing pretrained MLIPs. Across diverse chemistries and structures, U strongly tracks true prediction errors and robustly ranks configuration-level risk. Using U, we perform uncertainty-aware distillation to train system-specific potentials with far fewer labels: for tungsten, we match full density-functional-theory (DFT) training using 4% of the DFT data; for MoNbTaW, a dataset distilled by U supports high-accuracy potential training. By filtering numerical label noise, the distilled models can in some cases exceed the accuracy of the MLIPs trained on DFT data. This framework provides a practical reliability monitor and guides data selection and fine-tuning, enabling cost-efficient, accurate, and safer deployment of foundation models.
{"title":"Heterogeneous ensemble enables a universal uncertainty metric for atomistic foundation models","authors":"Kai Liu, Zixiong Wei, Wei Gao, Poulumi Dey, Marcel H. F. Sluiter, Fei Shuang","doi":"10.1038/s41524-025-01905-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01905-x","url":null,"abstract":"Universal machine-learning interatomic potentials (uMLIPs) are emerging as foundation models for atomistic simulation, offering near-ab initio accuracy at far lower cost. Their safe, broad deployment is limited by the absence of reliable, general uncertainty estimates. We present a unified, scalable uncertainty metric, U, built from a heterogeneous ensemble that reuses existing pretrained MLIPs. Across diverse chemistries and structures, U strongly tracks true prediction errors and robustly ranks configuration-level risk. Using U, we perform uncertainty-aware distillation to train system-specific potentials with far fewer labels: for tungsten, we match full density-functional-theory (DFT) training using 4% of the DFT data; for MoNbTaW, a dataset distilled by U supports high-accuracy potential training. By filtering numerical label noise, the distilled models can in some cases exceed the accuracy of the MLIPs trained on DFT data. This framework provides a practical reliability monitor and guides data selection and fine-tuning, enabling cost-efficient, accurate, and safer deployment of foundation models.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145765588","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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