npj Computational Materials最新文献_第3页

Light-induced above-room-temperature Chern insulators in group-IV Xenes 光致室温以上的四组陈氏绝缘子

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-30 DOI: 10.1038/s41524-025-01662-x

Zhe Li, Haijun Cao, Sheng Meng

引用次数: 0

Human–AI collaboration for modeling heat conduction in nanostructures 纳米结构热传导建模的人机协作

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-28 DOI: 10.1038/s41524-025-01657-8

Wenyang Ding, Jiang Guo, Meng An, Koji Tsuda, Junichiro Shiomi

引用次数: 0

High-density natural active sites for efficient nitrogen reduction on Kagome surfaces promoted by flat bands 高密度的天然活性位点，在Kagome表面上通过平带促进氮的有效还原

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-28 DOI: 10.1038/s41524-025-01663-w

Yuyuan Huang, Yanru Chen, Shunhong Zhang, Zhenyu Zhang, Ping Cui

{"title":"High-density natural active sites for efficient nitrogen reduction on Kagome surfaces promoted by flat bands","authors":"Yuyuan Huang, Yanru Chen, Shunhong Zhang, Zhenyu Zhang, Ping Cui","doi":"10.1038/s41524-025-01663-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01663-w","url":null,"abstract":"Recent studies have shown that single- or few-atom catalysts, with local states near the Fermi level, can promote nitrogen activation and the entire electrocatalytic nitrogen reduction reaction (eNRR) process, but are facing limitations in loading densities and stability. Here, we conceptualize that the Kagome metals featuring naturally abundant surface sites and flat bands are promising candidates to catalyze eNRR. Using first-principles calculations, we first show that the Kagome termination of the prototypical FeSn is accompanied by the presence of flat bands from the Fe-dz² and dxz/dyz orbitals, and the exposed surface can strongly chemisorb N2 with an adsorption energy of ~−0.7 eV. The limiting potential of 0.31 V indicates superior eNRR catalytic activity. The mutual independence between neighboring reactive sites also ensures an exceptionally high 25% atomic utilization within the Kagome layer, with each active site possessing high selectivity of eNRR. Our detailed analysis further reveals the critical role of the flat bands in boosting catalytic activity, which is also generalized to the isostructural CoSn and FeGe Kagome systems. Collectively, this work not only enhances the functionalities of Kagome materials for applications but also integrates flat band physics with single-atom catalysis, offering new opportunities in catalyst design.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"3 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144153418","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

Layered multiple scattering approach to Hard X-ray photoelectron diffraction: theory and application 硬x射线光电子衍射的分层多次散射方法：理论与应用

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-28 DOI: 10.1038/s41524-025-01653-y

Trung-Phuc Vo, Olena Tkach, Sylvain Tricot, Didier Sébilleau, Jürgen Braun, Aki Pulkkinen, Aimo Winkelmann, Olena Fedchenko, Yaryna Lytvynenko, Dmitry Vasilyev, Hans-Joachim Elmers, Gerd Schönhense, Ján Minár

{"title":"Layered multiple scattering approach to Hard X-ray photoelectron diffraction: theory and application","authors":"Trung-Phuc Vo, Olena Tkach, Sylvain Tricot, Didier Sébilleau, Jürgen Braun, Aki Pulkkinen, Aimo Winkelmann, Olena Fedchenko, Yaryna Lytvynenko, Dmitry Vasilyev, Hans-Joachim Elmers, Gerd Schönhense, Ján Minár","doi":"10.1038/s41524-025-01653-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01653-y","url":null,"abstract":"Photoelectron diffraction (PED) is a powerful technique for resolving surface structures with sub-angstrom precision. At high photon energies, angle-resolved photoemission spectroscopy (ARPES) reveals PED effects, often challenged by small cross-sections, momentum transfer, and phonon scattering. X-ray PED (XPD) is not only an advantageous approach but also exhibits unexpected effects. We present a PED implementation for the spin-polarized relativistic Korringa-Kohn-Rostoker (SPRKKR) package to disentangle them, employing multiple scattering theory and a one-step photoemission model. Unlike conventional real-space approaches, our method uses a k-space formulation via the layer-KKR method, offering efficient and accurate calculations across a wide energy range (20-8000 eV) without angular momentum or cluster size convergence issues. Additionally, the alloy analogy model enables simulations of finite-temperature XPD and effects in soft/hard X-ray ARPES. Applications include modeling circular dichroism in angular distributions (CDAD) in core-level photoemission of Si(100) 2p and Ge(100) 3p, excited by 6000 eV photons with circular polarization.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"33 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165157","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

Simulating the dynamics of NV− formation in diamond in the presence of carbon self-interstitials 模拟碳自间隙存在下金刚石中NV−形成的动力学

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-28 DOI: 10.1038/s41524-025-01605-6

Guangzhao Chen, Joseph C. A. Prentice, Jason M. Smith

引用次数: 0

Theory of the divacancy in 4H-SiC: impact of Jahn-Teller effect on optical properties 4H-SiC中距离理论：Jahn-Teller效应对光学性质的影响

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-28 DOI: 10.1038/s41524-025-01609-2

Vytautas Žalandauskas, Rokas Silkinis, Lasse Vines, Lukas Razinkovas, Marianne Etzelmüller Bathen

{"title":"Theory of the divacancy in 4H-SiC: impact of Jahn-Teller effect on optical properties","authors":"Vytautas Žalandauskas, Rokas Silkinis, Lasse Vines, Lukas Razinkovas, Marianne Etzelmüller Bathen","doi":"10.1038/s41524-025-01609-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01609-2","url":null,"abstract":"Understanding the optical properties of color centers in silicon carbide is essential for their use in quantum technologies, such as single-photon emission and spin-based qubits. In this work, first-principles calculations were employed using the r2SCAN density functional to investigate the electronic and vibrational properties of neutral divacancy configurations in 4H-SiC. Our approach addresses the dynamical Jahn–Teller effect in the excited states of axial divacancies. By explicitly solving the multimode dynamical Jahn–Teller problem, we compute emission and absorption lineshapes for axial divacancy configurations, providing insights into the complex interplay between electronic and vibrational degrees of freedom. The results show strong alignment with experimental data, underscoring the predictive power of the methodologies. Our calculations predict spontaneous symmetry breaking due to the pseudo Jahn–Teller effect in the excited state of the kh divacancy, accompanied by the lowest electron–phonon coupling among the four configurations and distinct polarizability. These unique properties facilitate its selective excitation, setting it apart from other divacancy configurations, and highlight its potential utility in quantum technology applications. These findings underscore the critical role of electron–phonon interactions and optical properties in spin defects with pronounced Jahn–Teller effects, offering valuable insights for the design and integration of quantum emitters for quantum technologies.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"25 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165159","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

Scalable training of neural network potentials for complex interfaces through data augmentation 基于数据增强的复杂界面神经网络电位的可扩展训练

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-28 DOI: 10.1038/s41524-025-01651-0

In Won Yeu, Annika Stuke, Jon López-Zorrilla, James M. Stevenson, David R. Reichman, Richard A. Friesner, Alexander Urban, Nongnuch Artrith

{"title":"Scalable training of neural network potentials for complex interfaces through data augmentation","authors":"In Won Yeu, Annika Stuke, Jon López-Zorrilla, James M. Stevenson, David R. Reichman, Richard A. Friesner, Alexander Urban, Nongnuch Artrith","doi":"10.1038/s41524-025-01651-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01651-0","url":null,"abstract":"Artificial neural network (ANN) potentials enable accurate atomistic simulations of complex materials at unprecedented scales, but training them for potential energy surfaces (PES) of diverse chemical environments remains computationally intensive, especially when the PES gradients are trained on atomic force data. Here, we present an efficient methodology incorporating forces into ANN training by translating them to synthetic energy data using Gaussian process regression (GPR), leading to accurate PES models with fewer additional first-principles calculations and a reduced computational effort for training. We evaluated the method on hybrid density-functional theory data for ethylene carbonate (EC) molecules and their interfaces with Li metal, which are relevant for Li-metal batteries. The GPR-ANN potentials achieved an accuracy comparable to fully force-trained ANN potentials with a significantly reduced computational and memory overhead, establishing the method as a powerful and scalable framework for constructing high-fidelity ANN potentials for complex materials systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"45 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144165158","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

Materials-discovery workflow guided by symbolic regression for identifying acid-stable oxides for electrocatalysis 由符号回归指导的材料发现工作流程，用于识别电催化的酸稳定氧化物

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-26 DOI: 10.1038/s41524-025-01596-4

Akhil S. Nair, Lucas Foppa, Matthias Scheffler

{"title":"Materials-discovery workflow guided by symbolic regression for identifying acid-stable oxides for electrocatalysis","authors":"Akhil S. Nair, Lucas Foppa, Matthias Scheffler","doi":"10.1038/s41524-025-01596-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01596-4","url":null,"abstract":"The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are often unknown if the property is governed by a high intricacy of many atomistic processes. Here, we develop an AL workflow based on the sure-independence screening and sparsifying operator (SISSO) symbolic regression approach. SISSO identifies analytical expressions correlated with a given materials property. These expressions depend on a few, key physical parameters, out of many offered primary features. Crucially, we train ensembles of SISSO models in order to quantify mean predictions and their uncertainty, enabling the use of SISSO in AL. We combine bootstrap sampling with Monte-Carlo dropout of primary features to obtain different datasets, which are used to train multiple SISSO models of the ensembles. The ensemble strategy improves the model performance with the feature dropout procedure alleviating the overconfidence issues observed for the widely used bagging ensemble approach. We demonstrate the SISSO-guided AL workflow by identifying acid-stable oxides for water splitting using high-quality DFT-HSE06 calculations. From a pool of 1470 materials, 12 acid-stable materials are identified in only 30 AL iterations. The materials-property maps provided by SISSO along with the uncertainty estimates reduce the risk of missing promising portions of the materials space that were overlooked in the initial, possibly biased dataset.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"24 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136974","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

Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy 基于从头算精度的可扩展机器学习光诱导有序无序相变方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-26 DOI: 10.1038/s41524-025-01614-5

Andrea Corradini, Giovanni Marini, Matteo Calandra

{"title":"Scalable machine learning approach to light induced order disorder phase transitions with ab initio accuracy","authors":"Andrea Corradini, Giovanni Marini, Matteo Calandra","doi":"10.1038/s41524-025-01614-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01614-5","url":null,"abstract":"While machine learning excels in simulating material thermal properties, its application to order-disorder non-thermal phase transitions induced by visible light has been limited by challenges in accurately describing potential energy surfaces, forces, and vibrational properties in the presence of a photoexcited electron-hole plasma. Here, we present a novel approach that combines constrained density functional theory with machine learning, yielding highly reliable interatomic potentials capable of capturing electron-hole plasma effects on structural properties. Applied to photoexcited silicon, our potential accurately reproduces the phonon dispersion of the crystal phase and allows for molecular dynamics simulations of tens of thousands of atoms. We show that, at low enough temperatures, the non-thermal melting transition is driven by a soft phonon and the formation of a double-well potential, at odds with thermal melting being strictly first order. Our method paves the way to large-scale, long-time simulations of light-induced order-disorder phase transitions with ab initio accuracy.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"142 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136973","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

Unified multimodal multidomain polymer representation for property prediction 聚合物性能预测的统一多模态多畴表示

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-26 DOI: 10.1038/s41524-025-01652-z

Qi Huang, Yedi Li, Lei Zhu, Qibin Zhao, Wenjie Yu

引用次数: 0