npj Computational Materials最新文献_第2页

Development of an atomic cluster expansion potential for iron and its oxides

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-26 DOI: 10.1038/s41524-025-01574-w

Baptiste Bienvenu, Mira Todorova, Jörg Neugebauer, Dierk Raabe, Matous Mrovec, Yury Lysogorskiy, Ralf Drautz

引用次数: 0

Applications of natural language processing and large language models in materials discovery

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-24 DOI: 10.1038/s41524-025-01554-0

Xue Jiang, Weiren Wang, Shaohan Tian, Hao Wang, Turab Lookman, Yanjing Su

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Phase-field modeling of coupled bulk photovoltaic effect and ferroelectric domain manipulation at ultrafast timescales 超快时间尺度下耦合体光伏效应和铁电畴操纵的相场建模

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-20 DOI: 10.1038/s41524-025-01556-y

Yi-De Liou, Kena Zhang, Ye Cao

{"title":"Phase-field modeling of coupled bulk photovoltaic effect and ferroelectric domain manipulation at ultrafast timescales","authors":"Yi-De Liou, Kena Zhang, Ye Cao","doi":"10.1038/s41524-025-01556-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01556-y","url":null,"abstract":"The bulk photovoltaic (BPV) effect, which generates steady photocurrents and above-bandgap photovoltages in non-centrosymmetric materials when exposed to light, holds great potential for advancing optoelectronic and photovoltaic technologies. However, its influence on the reconfiguration of ferroelectric domain structure remains underexplored. In this study, we developed a phase-field model to understand the BPV effect in ferroelectric oxides. Our model reveals that variations in BPV currents across domains create opposing charges at domain walls, enhancing the electric field within domains to ~1000 kV/cm. The strong electric fields can reorient the ferroelectric polarization and enable ultrafast domain wall movements and nonvolatile domain switching on the picosecond scale. Applying anisotropic strain can further strengthen this effect, enabling more precise control of domain switching. Our findings advance the fundamental understanding of BPV effect in ferroelectrics, paving the ways for developing opto-ferroelectric memory technologies and high-efficiency photovoltaic applications via precise domain engineering.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"20 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661148","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

Comment on “Machine learning enhanced analysis of EBSD data for texture representation”

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-19 DOI: 10.1038/s41524-025-01557-x

Helmut Schaeben, K. Gerald van den Boogaart

引用次数: 0

Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloys

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-18 DOI: 10.1038/s41524-025-01560-2

Yan Zhang, Cheng Wen, Pengfei Dang, Xue Jiang, Dezhen Xue, Yanjing Su

{"title":"Elemental numerical descriptions to enhance classification and regression model performance for high-entropy alloys","authors":"Yan Zhang, Cheng Wen, Pengfei Dang, Xue Jiang, Dezhen Xue, Yanjing Su","doi":"10.1038/s41524-025-01560-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01560-2","url":null,"abstract":"The machine learning-assisted design of new alloy compositions often relies on the physical and chemical properties of elements to describe the materials. In the present study, we propose a strategy based on an evolutionary algorithm to generate new elemental numerical descriptions for high-entropy alloys (HEAs). These newly defined descriptions significantly enhance classification accuracy, increasing it from 77% to ~97% for recognizing FCC, BCC, and dual phases, compared to traditional empirical features. Our experimental validation demonstrates that our classification model, utilizing these new elemental numerical descriptions, successfully predicted the phases of 8 out of 9 randomly selected alloys, outperforming the same model based on traditional empirical features, which correctly predicted 4 out of 9. By incorporating these descriptions derived from a simple logistic regression model, the performance of various classifiers improved by at least 15%. Moreover, these new numerical descriptions for phase classification can be directly applied to regression model predictions of HEAs, reducing the error by 22% and improving the R2 value from 0.79 to 0.88 in hardness prediction. Testing on six different materials datasets, including ceramics and functional alloys, demonstrated that the obtained numerical descriptions achieved higher prediction precision across various properties, indicating the broad applicability of our strategy.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"55 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641125","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

Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-17 DOI: 10.1038/s41524-025-01552-2

Alex K. Chew, Mohammad Atif Faiz Afzal, Zachary Kaplan, Eric M. Collins, Suraj Gattani, Mayank Misra, Anand Chandrasekaran, Karl Leswing, Mathew D. Halls

{"title":"Leveraging high-throughput molecular simulations and machine learning for the design of chemical mixtures","authors":"Alex K. Chew, Mohammad Atif Faiz Afzal, Zachary Kaplan, Eric M. Collins, Suraj Gattani, Mayank Misra, Anand Chandrasekaran, Karl Leswing, Mathew D. Halls","doi":"10.1038/s41524-025-01552-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01552-2","url":null,"abstract":"Mixtures of chemical ingredients, such as formulations, are ubiquitous in materials science, but optimizing their properties remains challenging due to the vast design space. Computational approaches offer a promising solution to traverse this space while minimizing trial-and-error experimentation. Using high-throughput classical molecular dynamics simulations, we generated a comprehensive dataset of over 30,000 solvent mixtures to evaluate three machine learning approaches that connect molecular structure and composition to property: formulation descriptor aggregation (FDA), formulation graph (FG), and Set2Set-based method (FDS2S). Our results demonstrate that our new FDS2S approach outperforms other approaches in predicting simulation-derived properties. Formulation-property relationships can reveal important substructures and identify promising formulations at least two to three times faster than random guessing. The models show robust transferability to experimental datasets, accurately predicting properties across energy, pharmaceutical, and petroleum applications. Our research demonstrates the utility of high-throughput simulations and machine learning tools to design formulations with promising properties.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"89 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635733","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

Spin-splitting above room-temperature in Janus Mn2ClSeH antiferromagnetic semiconductor with a large out-of-plane piezoelectricity

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-17 DOI: 10.1038/s41524-025-01566-w

Haiming Lu, Sitong Bao, Bocheng Lei, Sutao Sun, Linglu Wu, Jian Zhou, Lili Zhang

{"title":"Spin-splitting above room-temperature in Janus Mn2ClSeH antiferromagnetic semiconductor with a large out-of-plane piezoelectricity","authors":"Haiming Lu, Sitong Bao, Bocheng Lei, Sutao Sun, Linglu Wu, Jian Zhou, Lili Zhang","doi":"10.1038/s41524-025-01566-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01566-w","url":null,"abstract":"Two-dimensional (2D) antiferromagnets have garnered considerable research interest due to their robustness against external magnetic perturbation, ultrafast dynamics, and magneto-transport effects. However, the lack of spin-splitting in antiferromagnetic (AFM) materials severely limits their potential in spintronics applications. Inspired by inherent out-of-plane potential gradient of Janus structure, we predict three stable AFM Janus Mn2ClXH (X = O, S, and Se) monolayers with spontaneous spin-splitting based on first-principles calculations. Notably, Janus Mn2ClSeH exhibits a high Néel temperature of up to 510 K, robust perpendicular magnetocrystalline anisotropy, outstanding out-of-plane piezoelectricity of 0.454 × 10−10 C/m, and sizeable spontaneous valley polarization of 17.2 meV. Moreover, the spin-splitting can be significantly enhanced through appropriate synergistic regulation of biaxial strain and external electric field. These results demonstrate that the Janus Mn2ClSeH monolayer is a very potential candidate for designing intriguing antiferromagnet-based devices with fantastic piezoelectric and valleytronic characteristics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"39 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143635732","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

An NV− center in magnesium oxide as a spin qubit for hybrid quantum technologies

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-17 DOI: 10.1038/s41524-025-01558-w

Vrindaa Somjit, Joel Davidsson, Yu Jin, Giulia Galli

{"title":"An NV− center in magnesium oxide as a spin qubit for hybrid quantum technologies","authors":"Vrindaa Somjit, Joel Davidsson, Yu Jin, Giulia Galli","doi":"10.1038/s41524-025-01558-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01558-w","url":null,"abstract":"Recent predictions suggest that oxides, such as MgO and CaO, could serve as hosts of spin defects with long coherence times and thus be promising materials for quantum applications. However, in most cases, specific defects have not yet been identified. Here, by using a high-throughput first-principles framework and advanced electronic structure methods, we identify a negatively charged complex between a nitrogen interstitial and a magnesium vacancy in MgO with favorable electronic and optical properties for hybrid quantum technologies. We show that this NV− center has stable triplet ground and excited states, with singlet shelving states enabling optical initialization and spin-dependent readout. We predict several properties, including absorption, emission, and zero-phonon line energies, as well as zero-field splitting tensor, and hyperfine interaction parameters, which can aid in the experimental identification of this defect. Our calculations show that due to a strong pseudo-Jahn Teller effect and low-frequency phonon modes, the NV− center in MgO is subject to a substantial vibronic coupling. We discuss design strategies to reduce such coupling and increase the Debye-Waller factor, including the effect of strain and the localization of the defect states. We propose that the favorable properties of the NV− defect, along with the technological maturity of MgO, could enable hybrid classical-quantum applications, such as spintronic quantum sensors and single qubit gates.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"25 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143641126","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

Reply to: Comment on “Machine learning enhanced analysis of EBSD data for texture representation”

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-17 DOI: 10.1038/s41524-025-01562-0

J. Wanni, C. A. Bronkhorst, D. J. Thoma

引用次数: 0

Active learning of effective Hamiltonian for super-large-scale atomic structures

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-14 DOI: 10.1038/s41524-025-01563-z

Xingyue Ma, Hongying Chen, Ri He, Zhanbo Yu, Sergei Prokhorenko, Zheng Wen, Zhicheng Zhong, Jorge Íñiguez-González, L. Bellaiche, Di Wu, Yurong Yang

{"title":"Active learning of effective Hamiltonian for super-large-scale atomic structures","authors":"Xingyue Ma, Hongying Chen, Ri He, Zhanbo Yu, Sergei Prokhorenko, Zheng Wen, Zhicheng Zhong, Jorge Íñiguez-González, L. Bellaiche, Di Wu, Yurong Yang","doi":"10.1038/s41524-025-01563-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01563-z","url":null,"abstract":"The first-principles-based effective Hamiltonian scheme provides one of the most accurate modeling techniques for large-scale structures, especially for ferroelectrics. However, the parameterization of the effective Hamiltonian is complicated and can be difficult for some complex systems such as high-entropy perovskites. Here, we propose a general form of effective Hamiltonian and develop an active machine-learning approach to parameterize the effective Hamiltonian based on Bayesian linear regression. The parameterization is employed in molecular dynamics simulations with the prediction of energy, forces, stress and their uncertainties at each step, which decides whether first-principles calculations are executed to retrain the parameters. Structures of BaTiO3, PbTiO3, Pb(Zr0.75Ti0.25)O3, and (Pb,Sr)TiO3 system are taken as examples to show the accuracy of this approach, as compared with conventional parametrization method and experiments. This machine-learning approach provides a universal and automatic way to compute the effective Hamiltonian parameters for any considered complex systems with super-large-scale (more than 107 atoms) atomic structures.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"54 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618823","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