npj Computational Materials最新文献

Interpretable machine learning for atomic scale magnetic anisotropy in quantum materials 量子材料中原子尺度磁各向异性的可解释机器学习

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-18 DOI: 10.1038/s41524-025-01637-y

Jan Navrátil, Rafał Topolnicki, Michal Otyepka, Piotr Błoński

引用次数: 0

Crystal-like thermal transport in amorphous carbon 非晶碳的晶体样热输运

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-17 DOI: 10.1038/s41524-025-01625-2

Jaeyun Moon, Zhiting Tian

{"title":"Crystal-like thermal transport in amorphous carbon","authors":"Jaeyun Moon, Zhiting Tian","doi":"10.1038/s41524-025-01625-2","DOIUrl":"https://doi.org/10.1038/s41524-025-01625-2","url":null,"abstract":"Thermal transport in amorphous carbon has attracted immense attention due to its extreme thermal properties: It has been reported to have among the highest thermal conductivity for bulk amorphous solids up to ~37 W m−1 K−1, comparable to crystalline sapphire (α-Al2O3). However, mechanism behind the high thermal conductivity remains elusive due to many variables at play. In this work, we perform large-scale (~105 atoms) molecular dynamics simulations utilizing a machine learning potential based on neural networks with first-principles accuracy. Through spectral decomposition of thermal conductivity which enables a quantum correction to classical heat capacity, we find that propagating vibrational excitations govern thermal transport in amorphous carbon (~100 % of thermal conductivity) in sharp contrast to the convention that diffusive vibrational excitations dominate thermal transport in amorphous solids. This remarkable behavior resembles thermal transport in simple crystals. Our work, therefore, provides a perspective that deepens our understanding of intermediate thermal transport mechanisms between the two ends of spectrum of solids: crystalline and amorphous solids.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"54 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066985","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

AUGUR, a flexible and efficient optimization algorithm for identification of optimal adsorption sites AUGUR，一种灵活高效的最佳吸附位点识别优化算法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-17 DOI: 10.1038/s41524-025-01630-5

Ioannis Kouroudis, Poonam, Neel Misciasci, Felix Mayr, Leon Müller, Zhaosu Gu, Alessio Gagliardi

引用次数: 0

Predicting practical reduction potential of electrolyte solvents via computational hydrogen electrode and interpretable machine-learning models 通过计算氢电极和可解释的机器学习模型预测电解质溶剂的实际还原电位

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-16 DOI: 10.1038/s41524-025-01582-w

Zonglin Yi, Yi Zhou, Hao Liu, Li Li, Yan Zhao, Jiayuan Li, Yixuan Mao, Fangyuan Su, Cheng-Meng Chen

{"title":"Predicting practical reduction potential of electrolyte solvents via computational hydrogen electrode and interpretable machine-learning models","authors":"Zonglin Yi, Yi Zhou, Hao Liu, Li Li, Yan Zhao, Jiayuan Li, Yixuan Mao, Fangyuan Su, Cheng-Meng Chen","doi":"10.1038/s41524-025-01582-w","DOIUrl":"https://doi.org/10.1038/s41524-025-01582-w","url":null,"abstract":"Accurate prediction of practical reduction electrode potentials (Ered) of electrolyte solvents of electrochemical energy storage devices relies on calculating the Gibbs free energy in their reduction reaction. However, the emergence of new electrolyte solvents and additives leaves most of the reaction mechanisms unveiled. Here, we provide a machine-learning-assisted workflow of thermodynamically quantified Ered prediction for electrolyte solvents. A computational hydrogen electrode model based on density functional theory calculation is generalized for calculating the reaction free energy of electrochemical elementary steps. Machine-learning models are trained based on the organic and inorganic electrolyte solvents that possess experimentally identified reduction mechanisms. Validation of the best-scoring model is conducted by experimental validation of 6 additional solvents. Multiple thermodynamics features are found impactful on Ered through different chemical bonding with reaction intermediates. This workflow enables accurate Ered prediction for electrolyte solvents without identified reduction mechanisms, and is widely applicable in the electrochemical energy storage area.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144066986","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

Experimentally validated inverse design of FeNiCrCoCu MPEAs and unlocking key insights with explainable AI 通过实验验证FeNiCrCoCu mpea的逆设计，并通过可解释的AI解锁关键见解

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-15 DOI: 10.1038/s41524-025-01600-x

Fangxi Wang, Allana G. Iwanicki, Abhishek T. Sose, Lucas A. Pressley, Tyrel M. McQueen, Sanket A. Deshmukh

{"title":"Experimentally validated inverse design of FeNiCrCoCu MPEAs and unlocking key insights with explainable AI","authors":"Fangxi Wang, Allana G. Iwanicki, Abhishek T. Sose, Lucas A. Pressley, Tyrel M. McQueen, Sanket A. Deshmukh","doi":"10.1038/s41524-025-01600-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01600-x","url":null,"abstract":"A computational workflow integrating a stacked ensemble machine learning (SEML) model and a convolutional neural network (CNN) model with evolutionary algorithms has been developed to identify new compositions of FeNiCrCoCu MPEAs with high bulk modulus and unstable stacking fault energies. The identified compositions were synthesized and tested for their crystal structures and mechanical properties (hardness and Young’s modulus), resulting in single-phase face-centered cubic (FCC) structures. Additionally, the measured Young’s moduli were in good qualitative agreement with computational predictions. The SHapley Additive exPlanations (SHAP) analysis of the SEML model revealed a relationship between elemental concentration and USFE. Meanwhile, SHAP analysis of the CNN models uncovered correlations between the local clustering of MPEA elements and their mechanical properties. This computational workflow, along with the fundamental insights gained, can be readily expanded and applied to the design of MPEAs with different elemental compositions, as well as to materials beyond MPEAs.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"115 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979364","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

Ferroelectric control of diverse hyperbolic polaritons in the visible spectrum 可见光谱中不同双曲极化的铁电控制

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-14 DOI: 10.1038/s41524-025-01644-z

Zailong Chen, Shengxuan Xia, Shuo Yang, Baomin Wang, Shiwei Tang, Yurong Yang, Tian Cui, Shi Liu, Hongwei Wang

{"title":"Ferroelectric control of diverse hyperbolic polaritons in the visible spectrum","authors":"Zailong Chen, Shengxuan Xia, Shuo Yang, Baomin Wang, Shiwei Tang, Yurong Yang, Tian Cui, Shi Liu, Hongwei Wang","doi":"10.1038/s41524-025-01644-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01644-z","url":null,"abstract":"Low-dimensional van der Waals materials have attracted tremendous attention due to their exceptional physical, chemical, and mechanical properties, particularly their strong anisotropy in structural, electronic, and optical behaviors. Herein, we comprehensively studied diverse hyperbolic polaritons in quasi-one-dimensional ferroelectric material WOBr4, including their propagation patterns and frequencies, most notably, the electric-field and strain-driven elliptic-to-hyperbolic topological transition. Under moderate uniaxial strain or electric field, the optical absorption along the chain direction displays a threefold modulation in intensity and an approximately 1 eV frequency shift, while showing minor variation in the direction perpendicular to the chain. The pronounced tunability of anisotropic optical absorption is achieved through the regulation of 1D ferroelectric polarization by external stimuli, which controls the symmetry breaking of atomic orbitals involved in the optical transitions. We propose WOBr4 as a versatile platform for ferroelectric control of hyperbolic polaritons, offering potential for advanced applications in photovoltaics, optoelectronics, and nanophotonics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"4 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143979365","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

A numerical method for designing topological superconductivity induced by s-wave pairing s波对诱导拓扑超导设计的数值方法

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-14 DOI: 10.1038/s41524-025-01621-6

Jingnan Hu, Aiyun Luo, Zhijun Wang, Jingyu Zou, Quansheng Wu, Gang Xu

{"title":"A numerical method for designing topological superconductivity induced by s-wave pairing","authors":"Jingnan Hu, Aiyun Luo, Zhijun Wang, Jingyu Zou, Quansheng Wu, Gang Xu","doi":"10.1038/s41524-025-01621-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01621-6","url":null,"abstract":"Topological superconductors have garnered significant attention due to their potential for realizing topological quantum computation. However, a universal computational tool based on first-principles calculations for predicting topological superconductivity has not yet been fully developed, posing substantial challenges in identifying topological superconducting materials. In this paper, we present a numerical method to characterize the superconducting spectrum and topological invariants of two-dimensional (2D) slab systems using first-principles band structure, implemented in the open-source software WannierTools. To more accurately model the superconducting proximity effect, we integrate a phenomenological theory of SC pairing decay module into the program. Our approach can be applied to classical superconductor-topological insulator (SC-TI) heterostructures, SC-semiconductor heterostructures, and intrinsic topological superconductors. The program’s validity is demonstrated using the topological crystal insulator SnTe, the Rashba semiconductor InSb, and the superconductor NbSe2 as examples. We anticipate that this tool will accelerate the discovery of topological superconductor candidates.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"43 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143945772","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

Benchmarking machine learning models for predicting lithium ion migration 预测锂离子迁移的基准机器学习模型

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-13 DOI: 10.1038/s41524-025-01571-z

Artem D. Dembitskiy, Innokentiy S. Humonen, Roman A. Eremin, Dmitry A. Aksyonov, Stanislav S. Fedotov, Semen A. Budennyy

{"title":"Benchmarking machine learning models for predicting lithium ion migration","authors":"Artem D. Dembitskiy, Innokentiy S. Humonen, Roman A. Eremin, Dmitry A. Aksyonov, Stanislav S. Fedotov, Semen A. Budennyy","doi":"10.1038/s41524-025-01571-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01571-z","url":null,"abstract":"The development of fast ionic conductors to improve the performance of electrochemical devices relies on expensive high-throughput (HT) density functional theory (DFT) calculations of transport properties. Machine learning (ML) can accelerate HT workflows but requires high-quality data to ensure accurate predictions from trained models. In this study, we introduce the LiTraj dataset, which comprises 13,000 percolation and 122,000 migration barriers, and 1700 migration trajectories, calculated for Li-ion in diverse crystal structures using empirical force fields and DFT, respectively. With LiTraj, we demonstrate that classical ML models and graph neural networks (GNNs) for structure-to-property prediction of percolation and migration barriers can distinguish between “fast” and “poor” ionic conductors. Furthermore, we evaluate the capability of GNN-based universal ML interatomic potentials (uMLIPs) to identify optimal Li-ion migration trajectories. Fine-tuned uMLIPs achieve near-DFT accuracy in predicting migration barriers, significantly accelerating HT screenings of new ionic conductors.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"3 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143940672","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

Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning 通过高效和稳健的主动学习，绘制跨分子的多态学习和间隙驱动动力学的电子态流形

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-13 DOI: 10.1038/s41524-025-01636-z

Mikołaj Martyka, Lina Zhang, Fuchun Ge, Yi-Fan Hou, Joanna Jankowska, Mario Barbatti, Pavlo O. Dral

{"title":"Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning","authors":"Mikołaj Martyka, Lina Zhang, Fuchun Ge, Yi-Fan Hou, Joanna Jankowska, Mario Barbatti, Pavlo O. Dral","doi":"10.1038/s41524-025-01636-z","DOIUrl":"https://doi.org/10.1038/s41524-025-01636-z","url":null,"abstract":"We present a robust protocol for affordable learning of electronic states to accelerate photophysical and photochemical molecular simulations. The protocol solves several issues precluding the widespread use of machine learning (ML) in excited-state simulations. We introduce a novel physics-informed multi-state ML model that can learn an arbitrary number of excited states across molecules, with accuracy better or similar to the accuracy of learning ground-state energies, where information on excited-state energies improves the quality of ground-state predictions. We also present gap-driven dynamics for accelerated sampling of the small-gap regions, which proves crucial for stable surface-hopping dynamics. Together, multi-state learning and gap-driven dynamics enable efficient active learning, furnishing robust models for surface-hopping simulations and helping to uncover long-time-scale oscillations in cis-azobenzene photoisomerization. Our active-learning protocol includes sampling based on physics-informed uncertainty quantification, ensuring the quality of each adiabatic surface, low error in energy gaps, and precise calculation of the hopping probability.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143945773","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 magnetic transition states via string method in first principle calculation 用第一性原理计算中的弦法模拟磁跃迁态

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-05-12 DOI: 10.1038/s41524-025-01603-8

Wenlong Tang, Yanbo Li, Ben Xu

引用次数: 0