npj Computational Materials最新文献_第5页

Accelerating discovery of next-generation power electronics materials via high-throughput ab initio screening 通过高通量从头算筛选加速下一代电力电子材料的发现

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-01 DOI: 10.1038/s41524-025-01745-9

Jiashu Chen, Mingzhu Liu, Minghui Liu, Xinzhong Wang, Yiwen Su, Guangping Zheng

{"title":"Accelerating discovery of next-generation power electronics materials via high-throughput ab initio screening","authors":"Jiashu Chen, Mingzhu Liu, Minghui Liu, Xinzhong Wang, Yiwen Su, Guangping Zheng","doi":"10.1038/s41524-025-01745-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01745-9","url":null,"abstract":"Power electronics (PEs) play a pivotal role in electrical energy conversion and regulation for applications spanning from consumer devices to industrial infrastructure. Wide-bandgap (WBG) semiconductors such as SiC, GaN, and Ga2O3 have emerged as high-performance materials in PEs. Nevertheless, the WBG materials have some limitations that there exists the proliferation of intrinsic defects, with prohibitively high fabrication costs. We identify next-generation PEs materials beyond SiC, GaN, and Ga2O3 based on a high-throughput computational methodology. A massive database affording 153,235 materials is screened by leveraging ab initio methods with the thorough evaluation of bandgap, electron mobility, thermal conductivity, and Baliga and Johnson figures of merit (BFOM and JFOM). The comprehensive and effective theoretical analysis identifies some promising candidates (B2O3, BeO, and BN) that possess high BFOM, JFOM, and lattice thermal conductivity. Our methodology could be extended to other application domains of electronics, simplifying the process of exploring new materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144763390","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}

引用次数: 0

Discovery of materials for solar thermochemical hydrogen combining machine learning, computational chemistry, experiments and system simulations 结合机器学习、计算化学、实验和系统模拟的太阳能热化学氢材料的发现

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-08-01 DOI: 10.1038/s41524-025-01726-y

Jonathan Perry, Laura Molina, Alberto de la Calle, Raul Peño, Timothy W. Jones, M. Verónica Ganduglia-Pirovano, Silvia Jiménez-Fernández, Scott W. Donne, Juan M. Coronado, Alicia Bayon

{"title":"Discovery of materials for solar thermochemical hydrogen combining machine learning, computational chemistry, experiments and system simulations","authors":"Jonathan Perry, Laura Molina, Alberto de la Calle, Raul Peño, Timothy W. Jones, M. Verónica Ganduglia-Pirovano, Silvia Jiménez-Fernández, Scott W. Donne, Juan M. Coronado, Alicia Bayon","doi":"10.1038/s41524-025-01726-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01726-y","url":null,"abstract":"This study integrates first-principles calculations, computational chemistry, system simulations, experiments, and machine learning to identify redox perovskite oxides for solar thermochemical hydrogen production. Using two random forest regressions and one classification model, the approach predicts materials’ stability and the enthalpy of oxygen vacancy formation ((Delta {h}_{o})), a critical property for selecting materials for thermochemical hydrogen production. B-site composition significantly influences (Delta {h}_{o}) predictions. The methodology led to the discovery of Ba0.875Ca0.125Zr0.875Mn0.125O3 (BCZM), which reduces at temperatures up to 250 °C lower than CeO2 and is expected to outperform other perovskites in water splitting. However, CeO2 remains the benchmark for solar thermochemical hydrogen production. The combined use of machine learning and DFT calculations refined (triangle {h}_{o}) predictions and provided insights into experimental results. This framework not only enhances database creation for material screening but also establishes a novel approach for perovskite discovery for hydrogen production applications.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"217 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144756613","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}

引用次数: 0

DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials DeepEMs-25：一种深度学习潜力，可以破译高能材料热稳定性的动力学拉锯战

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-29 DOI: 10.1038/s41524-025-01739-7

Ming-Yu Guo, Yun-Fan Yan, Pin Chen, Wei-Xiong Zhang

{"title":"DeepEMs-25: a deep-learning potential to decipher kinetic tug-of-war dictating thermal stability in energetic materials","authors":"Ming-Yu Guo, Yun-Fan Yan, Pin Chen, Wei-Xiong Zhang","doi":"10.1038/s41524-025-01739-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01739-7","url":null,"abstract":"Atomic-scale insight into decompositions in energetic materials (EMs) is essential for harnessing energy release, which remains elusive due to both instrumental and computational limitations. Herein, we developed DeepEMs-25, a deep-learning potential trained on diverse EMs towards accurate and efficient simulations. Applying DeepEMs‑25 to an isostructural ABX3 molecular perovskites series, with A-site organic cations, B-site alkali or ammonium cations, and X-site perchlorate anions, we probe the effect of cation size on reactivity. Arrhenius analysis of 100-ps trajectories reveals that increasing B‑site ionic radius simultaneously decreases X–A collision’s activation energy (enhancing reaction rates) and decreases X–A collision’s pre‑exponential factor (reducing collision frequency), producing opposing kinetic effects. Such “kinetic tug‑of‑war” explains why an intermediate‑sized cation yields maximal thermal stability by optimally balancing reactivity and collision dissipation. A similarly sized reactive cation promotes additional hydrogen-transfer pathways causing accelerating decomposition. Our findings link atomistic kinetics to macroscopic stability, informing next-generation EMs design.<figure></figure>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"51 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144737472","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}

引用次数: 0

Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode 机器学习加速下锂金属阳极界面修饰机理探索

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-28 DOI: 10.1038/s41524-025-01747-7

Genming Lai, Ruiqi Zhang, Chi Fang, Juntao Zhao, Taowen Chen, Yunxing Zuo, Bo Xu, Jiaxin Zheng

{"title":"Machine-learning-accelerated mechanistic exploration of interface modification in lithium metal anode","authors":"Genming Lai, Ruiqi Zhang, Chi Fang, Juntao Zhao, Taowen Chen, Yunxing Zuo, Bo Xu, Jiaxin Zheng","doi":"10.1038/s41524-025-01747-7","DOIUrl":"https://doi.org/10.1038/s41524-025-01747-7","url":null,"abstract":"Although the electrode-electrolyte interface is a crucial electrochemical region, the comprehensive understanding of interface reactions is limited by the time and space scales of experimental tools. Theoretical simulations with this delicate interface also remain one of the most significant challenges for atomistic modeling, particularly for the stable long-timescale simulation of the interface. Here we introduce a novel scheme, hybrid ab initio molecular dynamics combined with machine learning potential (HAML), to accelerate the modeling of electrode-electrolyte interface reactions. We demonstrate its effectiveness in modeling the interfaces of Li metal with both liquid and solid-state electrolytes, capturing critical processes over extended time scales. Furthermore, we reveal the role of interface reaction kinetics in interface regulation through HAML simulations, combined with the similarity analysis method. It is demonstrated that element (Se, F, O) doping in the Li6PS5Cl system is an effective strategy for enhancing interface reaction kinetics, facilitating the formation of a more stable interface protective layer faster at room temperature. Moreover, moderate structural instability can positively contribute to interface stabilization. HAML offers a promising approach for addressing the challenge of designing stable interfaces while reducing computational costs. This work provides valuable insights for advancing the understanding and optimization of interface behaviors in Li metal batteries.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144719574","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}

引用次数: 0

Faithful novel machine learning for predicting quantum properties 忠实的新型机器学习预测量子特性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-26 DOI: 10.1038/s41524-025-01655-w

Gavin Nop, Micah Mundy, Jonathan D. H. Smith, Durga Paudyal

{"title":"Faithful novel machine learning for predicting quantum properties","authors":"Gavin Nop, Micah Mundy, Jonathan D. H. Smith, Durga Paudyal","doi":"10.1038/s41524-025-01655-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01655-w","url":null,"abstract":"Machine learning (ML) has accelerated the process of materials classification, particularly with crystal graph neural network (CGNN) architectures. However, advanced deep networks have hitherto proved challenging to build and train for quantum materials classification and property prediction. We show that faithful representations, which directly represent crystal structure and symmetry, both refine current ML and effectively implement advanced deep networks to accurately predict these materials and optimize their properties. Our new models reveal the previously hidden power of novel convolutional and pure attentional approaches to represent atomic connectivity and achieve strong performance in predicting topological properties, magnetic properties, and formation energies. With faithful representations, the state-of-the-art CGNN accurately predicts quantum chemistry materials and properties, accelerating the design and discovery and improving the implicit understanding of complex crystal structures and symmetries. On two separate benchmarks, our non-graphical neural networks achieve near parity with the CGNN architecture, making them viable alternatives.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144712459","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}

引用次数: 0

Navigating chemical design spaces for metal-ion batteries via machine-learning-guided phase-field simulations 通过机器学习引导相场模拟导航金属离子电池的化学设计空间

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-24 DOI: 10.1038/s41524-025-01735-x

Hamed Taghavian, Viktor Vanoppen, Erik Berg, Peter Broqvist, Jens Sjölund

引用次数: 0

Design principles for density functionals using a linear expansion 使用线性展开的密度泛函的设计原则

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-22 DOI: 10.1038/s41524-025-01712-4

Ayoub Aouina, Matteo Gatti, Lucia Reining

引用次数: 0

Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine 有限角度x射线纳米层析成像与机器学习支持迭代重建引擎

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-19 DOI: 10.1038/s41524-025-01724-0

Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan

{"title":"Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine","authors":"Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan","doi":"10.1038/s41524-025-01724-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01724-0","url":null,"abstract":"A long-standing challenge in tomography is the ‘missing wedge’ problem, which arises when the acquisition of projection images within a certain angular range is restricted due to geometrical constraints. This incomplete dataset results in significant artifacts and poor resolution in the reconstructed image. To tackle this challenge, we propose an approach dubbed Perception Fused Iterative Tomography Reconstruction Engine, which integrates a convolutional neural network (CNN) with perceptional knowledge as a smart regularizer into an iterative solving engine. We employ the Alternating Direction Method of Multipliers to optimize the solution in both physics and image domains, thereby achieving a physically coherent and visually enhanced result. We demonstrate the effectiveness of the proposed approach using various experimental datasets obtained with different x-ray microscopy techniques. All show significantly improved reconstruction even with a missing wedge of over 100 degrees−a scenario where conventional methods fail. Notably, it also improves the reconstruction in case of sparse projections, despite the network not being specifically trained for that. This demonstrates the robustness and generality of our method of addressing commonly occurring challenges in 3D x-ray imaging applications for real-world problems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"32 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664632","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}

引用次数: 0

Multiscale, mechanistic modeling of cesium transport in silicon carbide for TRISO fuel performance prediction 用于TRISO燃料性能预测的碳化硅中铯输运的多尺度、机制建模

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-19 DOI: 10.1038/s41524-025-01734-y

Pierre-Clément A. Simon, Jia-Hong Ke, Chao Jiang, Larry K. Aagesen, Wen Jiang, Stephen Novascone

{"title":"Multiscale, mechanistic modeling of cesium transport in silicon carbide for TRISO fuel performance prediction","authors":"Pierre-Clément A. Simon, Jia-Hong Ke, Chao Jiang, Larry K. Aagesen, Wen Jiang, Stephen Novascone","doi":"10.1038/s41524-025-01734-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01734-y","url":null,"abstract":"Understanding cesium (Cs) transport in TRistructural ISOtropic (TRISO) particle fuel is crucial for predicting fission product release in high-temperature reactors. However, current challenges include significant scatter in diffusivity data and unexplained temperature-dependent diffusion regimes in the silicon carbide layer. This study addresses these challenges by developing a multiscale, mechanistic Cs transport model integrating atomistic simulations and phase field modeling. Our model quantifies temperature and grain size effects on Cs diffusivity, attributing experimentally observed regimes to a transition from bulk-dominated diffusivity at high temperatures to grain boundary-dominated diffusivity at lower temperatures. The model, validated against diffusion measurements and advanced gas reactor (AGR)-1 and AGR-2 post-irradiation fission product release data, enhances the predictive capability of the BISON fuel performance code. This study advances our understanding of Cs release from TRISO particles and its dependence on temperature and silicon carbide grain size, with implications for the safety and efficiency of high-temperature nuclear reactors.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144664633","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}

引用次数: 0

Coupled lattice-charge-magnetic fluctuations for nonlocal flux mediated pairing in cuprate superconductors 铜超导体中非局域磁介导对的晶格-电荷-磁耦合波动

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-07-19 DOI: 10.1038/s41524-025-01697-0

Xi Chen, Yuchuang Cao, Jianghui Pan, Jiahao Dong, Changkai Luo, Xin Li

引用次数: 0