npj Computational Materials最新文献

Machine learning on multiple topological materials datasets 基于多拓扑材料数据集的机器学习

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-13 DOI: 10.1038/s41524-025-01687-2

Yuqing He, Pierre-Paul De Breuck, Hongming Weng, Matteo Giantomassi, Gian-Marco Rignanese

引用次数: 0

A novel algorithm for circumventing the need to model large supercells of mismatched material interfaces 一种新的算法，避免了对不匹配材料界面的大型超级细胞进行建模的需要

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-13 DOI: 10.1038/s41524-025-01675-6

Noam Levi Hadari, Maytal Caspary Toroker

引用次数: 0

Bridging deep learning force fields and electronic structures with a physics-informed approach 连接深度学习力场和电子结构与物理知情的方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-12 DOI: 10.1038/s41524-025-01668-5

Yubo Qi, Weiyi Gong, Qimin Yan

{"title":"Bridging deep learning force fields and electronic structures with a physics-informed approach","authors":"Yubo Qi, Weiyi Gong, Qimin Yan","doi":"10.1038/s41524-025-01668-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01668-5","url":null,"abstract":"This work presents a physics-informed neural network approach bridging deep-learning force field and electronic structure simulations, illustrated through twisted two-dimensional large-scale material systems. The deep potential molecular dynamics model is adopted as the backbone, and the electronic structure simulation is integrated. Using Wannier functions as the basis, we categorize Wannier Hamiltonian elements based on physical principles to incorporate diverse information from a deep-learning force field model. This information-sharing mechanism streamlines the architecture of our dual-functional model, enhancing its efficiency and effectiveness. This Wannier-based dual-functional model for simulating electronic band and structural relaxation (WANDER) serves as a powerful tool to explore large-scale systems. By endowing a well-developed machine-learning force field with electronic structure simulation capabilities, the study marks a significant advancement in developing multimodal machine-learning-based computational methods that can achieve multiple functionalities traditionally exclusive to first-principles calculations. Moreover, utilizing Wannier functions as the basis lays the groundwork for predicting more physical quantities.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"43 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144269179","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

High-throughput alloy and process design for metal additive manufacturing 金属增材制造的高通量合金和工艺设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-12 DOI: 10.1038/s41524-025-01670-x

Sofia Sheikh, Brent Vela, Pejman Honarmandi, Peter Morcos, David Shoukr, Abdelrahman Mostafa Kotb, Ibrahim Karaman, Alaa Elwany, Raymundo Arróyave

{"title":"High-throughput alloy and process design for metal additive manufacturing","authors":"Sofia Sheikh, Brent Vela, Pejman Honarmandi, Peter Morcos, David Shoukr, Abdelrahman Mostafa Kotb, Ibrahim Karaman, Alaa Elwany, Raymundo Arróyave","doi":"10.1038/s41524-025-01670-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01670-x","url":null,"abstract":"Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive analysis of chemical composition and processing conditions. The complexity of experimental exploration drives the need for high-throughput computational frameworks. This study introduces a framework that integrates material properties, processing parameters, and melt pool profiles from three thermal models to assess process-induced defects, such as lack-of-fusion, balling, and keyholing. A deep learning surrogate model accelerates the printability assessment by 1000 times without losing accuracy. We validate the framework with printability maps for the equiatomic CoCrFeMnNi system and apply it to explore printable alloys in the Co-Cr-Fe-Mn-Ni high-entropy alloy space. Ensemble probabilistic printability maps further provide insights into defect likelihood and uncertainty, enhancing alloy design for AM by efficiently navigating vast design spaces.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"12 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144278731","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

Universal machine learning interatomic potentials are ready for phonons 通用机器学习原子间势已经为声子准备好了

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-12 DOI: 10.1038/s41524-025-01650-1

Antoine Loew, Dewen Sun, Hai-Chen Wang, Silvana Botti, Miguel A. L. Marques

引用次数: 0

A machine learning approach to predict tight-binding parameters for point defects via the projected density of states 一种通过状态投影密度预测点缺陷紧密结合参数的机器学习方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-11 DOI: 10.1038/s41524-025-01634-1

Henry Phillip Fried, Daniel Barragan-Yani, Florian Libisch, Ludger Wirtz

{"title":"A machine learning approach to predict tight-binding parameters for point defects via the projected density of states","authors":"Henry Phillip Fried, Daniel Barragan-Yani, Florian Libisch, Ludger Wirtz","doi":"10.1038/s41524-025-01634-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01634-1","url":null,"abstract":"Calculating the impact of point defects on the macroscopic properties of technologically relevant semiconductors remains a considerable challenge. Semi-empirical approaches, such as the tight-binding method, are very efficient in calculating the electronic structure of large supercells containing one or several defects. However, the accuracy of these calculations depends on the quality of the parameters. Obtaining reliable parameters by fitting to the large number of entangled bands in defective supercells is a demanding task. We therefore present an alternative way by fitting to the atom and orbital projected densities of states. Starting with a tight-binding fit of the pristine material, we only need a few physically motivated parameters for the fitting of defects. The training is done on data sets generated purely with parameter variations of tight-binding Hamiltonians. We demonstrate the efficiency of our approach for the calculation of the carbon monomer and the carbon dimer substitutions in hexagonal boron nitride. The method opens a path towards understanding complicated defect landscapes using a computationally affordable semi-empirical approach without sacrificing accuracy.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144260194","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

Efficiently charting the space of mixed vacancy-ordered perovskites by machine-learning encoded atomic-site information 利用机器学习编码的原子位信息有效地绘制混合空位有序钙钛矿的空间图

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-10 DOI: 10.1038/s41524-025-01667-6

Fan Zhang, Li Fu, Weiwei Gao, Peihong Zhang, Jijun Zhao

{"title":"Efficiently charting the space of mixed vacancy-ordered perovskites by machine-learning encoded atomic-site information","authors":"Fan Zhang, Li Fu, Weiwei Gao, Peihong Zhang, Jijun Zhao","doi":"10.1038/s41524-025-01667-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01667-6","url":null,"abstract":"Vacancy-ordered double perovskites (VODPs) are promising alternatives to three-dimensional lead halide perovskites for optoelectronic applications. Mixing these materials creates a vast compositional space for tunable properties but complicates efficient screening of target candidates. Here, we illustrate the diverse electronic and optical characteristics as well as the nonlinear mixing effects within mixed VODPs. Furthermore, inspired by the observation that all physical properties of mixed systems with limited local environment options can be uniquely determined by the information regarding atomic-site occupation, we developed a method combining data augmentation and a transformer-inspired graph neural network to effectively encodes atomic-site information in mixed systems. This approach accurately predicts band gaps and formation energies for mixed VODPs, achieving Root Mean Square Errors of 21 meV and 3.9 meV/atom, respectively. Trained with samples with up-to three mixed elements and small supercells (<72 atoms), the model not only can be generalized to medium- and high-entropy systems and larger supercells (>200 atoms), but also well reproduces the bandgap bowing effect in Sn-based mixed VODPs.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"4 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144252770","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

SeeBand: a highly efficient, interactive tool for analyzing electronic transport data SeeBand：用于分析电子传输数据的高效交互式工具

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-07 DOI: 10.1038/s41524-025-01645-y

Michael Parzer, Alexander Riss, Fabian Garmroudi, Johannes de Boor, Takao Mori, Ernst Bauer

引用次数: 0

Defect inducing large spin orbital coupling enhances magnetic recovery dynamics in CrI3 monolayer 缺陷诱导的大自旋轨道耦合增强了CrI3单层的磁恢复动力学

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-07 DOI: 10.1038/s41524-025-01665-8

Yu Zhou, Ke Zhao, Zhenfa Zheng, Huiwen Xiang, Jin Zhao, Chengyan Liu

引用次数: 0

Nanoscale confinement of phonon flow and heat transport 声子流动和热输运的纳米尺度约束

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-06-07 DOI: 10.1038/s41524-025-01593-7

Albert Beardo, Weinan Chen, Brendan McBennett, Tara Karimzadeh Sabet, Emma E. Nelson, Theodore H. Culman, Henry C. Kapteyn, Joshua L. Knobloch, Margaret M. Murnane, Ismaila Dabo

引用次数: 0