npj Computational Materials最新文献

Synthesizability, hardness, and stacking order in multicomponent transition metal carbides from machine-learned potentials 基于机器学习势的多组分过渡金属碳化物的可合成性、硬度和堆叠顺序

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-17 DOI: 10.1038/s41524-026-02355-9

Xin Liu,Anirudh Raju Natarajan

{"title":"Synthesizability, hardness, and stacking order in multicomponent transition metal carbides from machine-learned potentials","authors":"Xin Liu,Anirudh Raju Natarajan","doi":"10.1038/s41524-026-02355-9","DOIUrl":"https://doi.org/10.1038/s41524-026-02355-9","url":null,"abstract":"Abstract Multicomponent transition metal carbides are promising for extreme-environment applications, but identifying compositions that are both synthesizable and hard remains challenging. We fine-tune the MACE machine-learned interatomic potential on approximately 28,000 density functional theory calculations spanning the composition space of groups 4–6 transition metals and carbon to predict the thermodynamic stability and elastic properties of multicomponent carbides. The fine-tuned model achieves formation energy errors of ~10 meV/atom for thermodynamically relevant structures with only 20% of the training data. We screen over 1500 equiatomic compositions across rocksalt, hexagonal, and hcp prototypes, combining free energy models with elasticity-based hardness surrogates. Synthesizability predictions at 1500 °C agree well with experimental reports for both single-phase and multiphase carbides. The group number of the constituent metals governs both stability and hardness. Short-range order contributes only a few meV/atom to the free energy, so the disordered phase can be treated as a random solid solution when screening rocksalt carbides at high temperature. For compositions mixing group 4/5 and group 6 metals, we identify a new family of stacking-ordered phases with formation energies well below those of disordered rocksalt or hexagonal structures. DFT calculations corroborate these predictions and suggest that stacking-ordered phases should be experimentally accessible in multicomponent carbides. This study provides a framework for screening synthesizable multicomponent materials with target properties, identifies promising carbide compositions across the full nine-component space, and reveals a new class of stacking-ordered carbides accessible only in multicomponent compositions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"19 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210090","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

Tracking the lithiation state of LixSi from machine-learned XPS binding energies 利用机器学习的XPS结合能跟踪LixSi的锂化状态

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-17 DOI: 10.1038/s41524-026-02302-8

Michael Alejandro Hernandez Bertran,Davide Tisi,Federico Grasselli,Michele Ceriotti,Elisa Molinari,Deborah Prezzi

{"title":"Tracking the lithiation state of LixSi from machine-learned XPS binding energies","authors":"Michael Alejandro Hernandez Bertran,Davide Tisi,Federico Grasselli,Michele Ceriotti,Elisa Molinari,Deborah Prezzi","doi":"10.1038/s41524-026-02302-8","DOIUrl":"https://doi.org/10.1038/s41524-026-02302-8","url":null,"abstract":"Abstract X-ray photoelectron spectroscopy (XPS) is a powerful technique to probe chemical states and interfacial processes in battery materials, but a quantitative interpretation is often hindered by the complex, heterogeneous microstructures that form during operation and dominate electrochemical cycling. Silicon-based anodes represent a paradigmatic example in Li batteries, as (de)lithiation proceeds through the formation of strongly disordered Li x Si phases and crystal-amorphous transformations that are hard to characterize. Here, we introduce a computational framework that combines machine-learning (ML) prediction of core-level binding energies to large-scale atomistic simulations—grand-canonical Monte Carlo (GCMC) complemented with molecular dynamics (MD), driven by an ML potential—for a systematic sampling of lithiation states and local atomic environments. This approach yields stoichiometry maps that capture the main spectroscopic trends observed in operando and ex situ XPS measurements, including the distinctive Si 2 p signatures associated with the crystal-to-amorphous disordering during early delithiation.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"37 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210089","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

Response-function-conditioned inverse design enables disorder as a design dimension in porous materials 响应函数条件逆设计使无序成为多孔材料的设计维度

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-16 DOI: 10.1038/s41524-026-02367-5

Wei Zhao,Ran Chen,Jiaxiu Zhang,Liangliang Nan,Song Wang,Honghua Chen,Hanyang Yu,Songcan Chen,Mingqiang Wei

引用次数: 0

Inverse materials design via joint generation of crystal structures and local electronic descriptors 通过晶体结构和局部电子描述符的联合生成逆材料设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-15 DOI: 10.1038/s41524-026-02322-4

Ibuki Okuda,Izumi Takahara,Teruyasu Mizoguchi

引用次数: 0

Switchable altermagnetism and Luttinger-compensated ferrimagnetism in graphene-MnPS3 heterostructures 石墨烯- mnps3异质结构中的可切换电磁学和luttinger补偿铁磁性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-14 DOI: 10.1038/s41524-026-02358-6

Wenqi Xiong,Yu Jia

引用次数: 0

Structural optimization in TensErLEED using a parameter tree and R-factor gradients 使用参数树和r因子梯度的TensErLEED结构优化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-14 DOI: 10.1038/s41524-026-02318-0

Alexandra M. Imre,Paul Haidegger,Florian Kraushofer,Ralf Wanzenböck,Tobias Hable,Sarah Tobisch,Marie Kienzer,Florian Buchner,Jesús Carrete,Georg K. H. Madsen,Michael Schmid,Ulrike Diebold,Michele Riva

{"title":"Structural optimization in TensErLEED using a parameter tree and R-factor gradients","authors":"Alexandra M. Imre,Paul Haidegger,Florian Kraushofer,Ralf Wanzenböck,Tobias Hable,Sarah Tobisch,Marie Kienzer,Florian Buchner,Jesús Carrete,Georg K. H. Madsen,Michael Schmid,Ulrike Diebold,Michele Riva","doi":"10.1038/s41524-026-02318-0","DOIUrl":"https://doi.org/10.1038/s41524-026-02318-0","url":null,"abstract":"Abstract Quantitative low-energy electron diffraction [LEED I ( V )] is a powerful method for surface-structure determination, based on a direct comparison of I ( V ) data between experiment and computations for a structure model. As the diffraction intensities I are highly sensitive to subtle structural changes, local structure optimization is essential for assessing the validity of a structure model and finding the best-fit structure. Even when using the tensor-LEED approximation—which accelerates optimization by applying a perturbative treatment of small deviations from a reference structure—the optimization process is computationally expensive. In the new tensor-LEED implementation presented in this work, the problem of surface-structure optimization is reformulated using a tree-based data structure, which helps to avoid redundant function evaluations. Intensities are computed on the fly, eliminating limitations of previous algorithms using grid-based search with precomputed values. This enables the use of state-of-the-art optimization algorithms. Implemented in Python with the JAX library, the method provides access to gradients of the R factor and supports execution on graphics processing units (GPUs). These developments reduce the computing time by more than an order of magnitude.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"236 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210095","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

Quantifying electronic insulation of solid-electrolyte interphases through electrolyte decomposition kinetics 通过电解质分解动力学定量分析固体-电解质界面的电子绝缘

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-14 DOI: 10.1038/s41524-026-02362-w

Yuan Fang,Zhenyu Zhu,Siqi Shi,Yu Gu,Sai Duan,Xin Xu

引用次数: 0

Irreducible sub-nm2 ferroelectric domains by 2D-flat polar band in perovskite superlattices 钙钛矿超晶格中二维平极性带的不可约亚nm2铁电畴

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-13 DOI: 10.1038/s41524-026-02354-w

Pawan Kumar,Jun Hee Lee

引用次数: 0

A hybrid quantum mechanics machine learning forcefield framework for accurate solute-dislocation interaction simulations 用于精确模拟溶质-位错相互作用的混合量子力学机器学习力场框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-13 DOI: 10.1038/s41524-026-02341-1

Junting Zhang,Colleen Reynolds,Ryan S. Stroud,Jana Smutna,Daniel J. M. King,Andrew P. Horsfield,Mark R. Wenman

{"title":"A hybrid quantum mechanics machine learning forcefield framework for accurate solute-dislocation interaction simulations","authors":"Junting Zhang,Colleen Reynolds,Ryan S. Stroud,Jana Smutna,Daniel J. M. King,Andrew P. Horsfield,Mark R. Wenman","doi":"10.1038/s41524-026-02341-1","DOIUrl":"https://doi.org/10.1038/s41524-026-02341-1","url":null,"abstract":"Abstract Solute–dislocation interactions play a central role in controlling microstructural evolution and mechanical behaviour of structural materials, yet conventional atomistic modelling approaches struggle to combine the chemical accuracy with computational scalability. In the nuclear industry, these challenges become particularly acute, as experiments reveal strong correlations between solute segregation and irradiation-induced dislocation loops. However, theoretical insight remains limited because density functional theory (DFT) simulations are prohibitively expensive at relevant length scales, while traditional semi-empirical interatomic potentials lack the chemical fidelity required for predictive solute–defect calculations. Here, we introduce a hybrid quantum-mechanics/machine-learning (QM/ML) simulation framework that couples DFT with neural-network machine learning interatomic potentials (MLIPs), enabling DFT-informed atomistic dislocation simulations at reduced computational cost. We demonstrate the QM/ML framework’s capability by reproducing the experimentally observed Sn and Fe segregation to dislocation loops in Zr and investigating magnetically complex solute-dislocation interactions in steel. These results demonstrate the potential of the approach as a transferable framework for modelling irradiation-induced defect structures and benchmarking emerging MLIPs.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"33 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149210097","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 exploration of refractory high-entropy alloys for strength and plasticity 耐火高熵合金强度和塑性的高通量探索

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-10-10 DOI: 10.1038/s41524-026-02351-z

Stephen A. Giles,Hugh Shortt,Peter K. Liaw,Debasis Sengupta

引用次数: 0