npj Computational Materials最新文献

A general approach for determining applicability domain of machine learning models

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-05 DOI: 10.1038/s41524-025-01573-x

Lane E. Schultz, Yiqi Wang, Ryan Jacobs, Dane Morgan

引用次数: 0

Machine learning and high-throughput computational guided development of high temperature oxidation-resisting Ni-Co-Cr-Al-Fe based high-entropy alloys

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-04 DOI: 10.1038/s41524-025-01568-8

Xingru Tan, William Trehern, Aditya Sundar, Yi Wang, Saro San, Tianwei Lu, Fan Zhou, Ting Sun, Youyuan Zhang, Yuying Wen, Zhichao Liu, Michael Gao, Shanshan Hu

{"title":"Machine learning and high-throughput computational guided development of high temperature oxidation-resisting Ni-Co-Cr-Al-Fe based high-entropy alloys","authors":"Xingru Tan, William Trehern, Aditya Sundar, Yi Wang, Saro San, Tianwei Lu, Fan Zhou, Ting Sun, Youyuan Zhang, Yuying Wen, Zhichao Liu, Michael Gao, Shanshan Hu","doi":"10.1038/s41524-025-01568-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01568-8","url":null,"abstract":"Ni-Co-Cr-Al-Fe-based high-entropy alloys (HEAs) have been demonstrated to possess exceptional oxidation resistance, rendering them promising candidates as bond coats to protect critical components in turbine power systems. However, with the conventional time-consuming alloy design approach, only a small fraction of Ni-Co-Cr-Al-Fe-based HEAs, focusing on equiatomic compositions, has been explored to date. In this study, we developed an effective design framework with the aid of machine learning (ML) and high throughput computations, enabling the rapid exploration of high-temperature oxidation-resistant non-equiatomic HEAs. This innovative approach leverages ML techniques to swiftly select candidates with superior oxidation resistance within the expansive high-entropy composition landscape. Complemented by a thermodynamic-informed ranking-based selection process, several novel non-equiatomic Ni-Co-Cr-Al-Fe HEA candidates surpassing the oxidation resistance of the state-of-the-art bond coat material MCrAlY have been identified and further experimentally demonstrated. Our findings offer a pathway for the development of advanced bond coats in the realm of next-generation turbine engine technology.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"20 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782426","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

Increased Curie temperature in lithium substituted ferroelectric niobate perovskite via soft polar mode enhancement

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-04 DOI: 10.1038/s41524-025-01584-8

Hao-Cheng Thong, Fang-Zhou Yao, Xian-Xian Cai, Ze Xu, Mao-Hua Zhang, Huazhang Zhang, Ben Xu, Yan Wei, Shi-Dong Wang, Ke Wang

{"title":"Increased Curie temperature in lithium substituted ferroelectric niobate perovskite via soft polar mode enhancement","authors":"Hao-Cheng Thong, Fang-Zhou Yao, Xian-Xian Cai, Ze Xu, Mao-Hua Zhang, Huazhang Zhang, Ben Xu, Yan Wei, Shi-Dong Wang, Ke Wang","doi":"10.1038/s41524-025-01584-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01584-8","url":null,"abstract":"The functionality of ferroelectrics is often constrained by their Curie temperature, above which depolarization occurs. Lithium (Li) is the only experimentally known substitute that can increase the Curie temperature in ferroelectric niobate-based perovskites, yet the mechanism remains unresolved. Here, the unique phenomenon in Li-substituted KNbO3 is investigated using first-principles density functional theory. Theoretical calculations show that Li substitution at the A-site of perovskite introduces compressive chemical pressure, reducing Nb–O hybridization and associated ferroelectric instability. However, the large off-center displacement of the Li cation compensates for this reduction and further enhances the soft polar mode, thereby raising the Curie temperature. In addition, the stability of the tetragonal phase over the orthorhombic phase is predicted upon Li substitution, which reasonably explains the experimental observation of a decreased orthorhombic-to-tetragonal phase transition temperature. Finally, a metastable anti-phase polar state in which the Li cation displaces oppositely to the Nb cation is revealed, which could also contribute to the variation of phase transition temperatures. These findings provide critical insights into the atomic-scale mechanisms governing Curie temperature enhancement in ferroelectrics and pave the way for designing advanced ferroelectric materials with improved thermal stability and functional performance.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"80 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775699","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

Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-04 DOI: 10.1038/s41524-025-01579-5

Qi Wang, Yonggang Yao

{"title":"Harnessing machine learning for high-entropy alloy catalysis: a focus on adsorption energy prediction","authors":"Qi Wang, Yonggang Yao","doi":"10.1038/s41524-025-01579-5","DOIUrl":"https://doi.org/10.1038/s41524-025-01579-5","url":null,"abstract":"High-entropy alloys (HEAs) have emerged as promising candidates for catalyst applications due to their inherent compositional, structural, and site-level diversities, which enable highly tunable catalytic properties. However, these complexities pose grand challenges for traditional “trial-and-error” experimentation or computationally expensive “brute-force” ab initio calculations. Machine learning (ML) demonstrates great potential to address these challenges by establishing efficient, scalable mappings from composition, structure or site environment to HEA properties. Among these properties, adsorption energy, which quantifies the binding strength between catalytic intermediates and surface sites, is a crucial indicator of catalytic activity. This review provides a comprehensive overview of ML-driven strategies for adsorption energy prediction in the context of HEAs. Two primary strategies are introduced: “direct” prediction from unrelaxed structure and “iterative” prediction via ML potential-guided relaxation modeling. Both strategies can leverage handcrafted features or end-to-end frameworks such as graph neural networks. We also discuss how pretrained models on large-scale databases can extend to out-of-domain HEA systems. Beyond methodology, we address key challenges and future directions, including benchmarking ML strategies, developing HEA-specific datasets, pretraining and fine-tuning, integrating chained ML models, advancing multi-objective optimization, and bridging ML predictions with experimental validation. By critically evaluating existing strategies and highlighting emerging trends, this review underscores the critical role of ML in advancing adsorption energy predictions, offering a foundation for accelerating the discovery and optimization of HEA catalysts.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"73 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775697","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

GAPF-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloys

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-04 DOI: 10.1038/s41524-025-01567-9

Mohamed Hendy, Okan K. Orhan, Homin Shin, Ali Malek, Mauricio Ponga

{"title":"GAPF-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloys","authors":"Mohamed Hendy, Okan K. Orhan, Homin Shin, Ali Malek, Mauricio Ponga","doi":"10.1038/s41524-025-01567-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01567-9","url":null,"abstract":"High-entropy alloys (HEAs) exhibit exceptional catalytic performance due to their complex surface structures. However, the vast number of active binding sites in HEAs, as opposed to conventional alloys, presents a significant computational challenge in catalytic applications. To tackle this challenge, robust methods must be developed to efficiently explore the configurational space of HEA catalysts. Here, we introduce a novel approach that combines alchemical perturbation density functional theory (APDFT) with a graph-based correction scheme to explore the binding energy landscape of HEAs. Our results demonstrate that APDFT can accurately predict binding energies for isoelectronic permutations in HEAs at minimal computational cost, significantly accelerating configurational space sampling. However, APDFT errors increase substantially when permutations occur near binding sites. To address this issue, we developed a graph-based Gaussian process regression model to correct discrepancies between APDFT and conventional density functional theory values. Our approach enables the prediction of binding energies for hundreds of thousands of configurations with a mean average error of 30 meV, requiring a handful of ab initio simulations.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782425","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

Identifying MOFs for electrochemical energy storage via density functional theory and machine learning 通过密度泛函理论和机器学习识别用于电化学储能的 MOFs

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-03 DOI: 10.1038/s41524-025-01590-w

Tian Sun, Zhenxiang Wang, Liang Zeng, Guang Feng

引用次数: 0

Efficient modelling of anharmonicity and quantum effects in PdCuH2 with machine learning potentials

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-02 DOI: 10.1038/s41524-025-01553-1

Francesco Belli, Eva Zurek

{"title":"Efficient modelling of anharmonicity and quantum effects in PdCuH2 with machine learning potentials","authors":"Francesco Belli, Eva Zurek","doi":"10.1038/s41524-025-01553-1","DOIUrl":"https://doi.org/10.1038/s41524-025-01553-1","url":null,"abstract":"Quantum nuclear effects and anharmonicity impact a wide range of functional materials and their properties. One of the most powerful techniques to model these effects is the Stochastic Self-Consistent Harmonic Approximation (SSCHA). Unfortunately, the SSCHA is extremely computationally expensive, prohibiting its routine use. We propose a protocol that pairs machine learning interatomic potentials, which can be tailored for the system at hand via active learning, with the SSCHA. Our method leverages an upscaling procedure that allows for the treatment of supercells of up to thousands of atoms with practically minimal computational effort. The protocol is applied to PdCuHx (x = 0−2) compounds, chosen because previous experimental studies have reported superconducting critical temperatures, Tcs, as high as 17 K at ambient pressures in an unknown hydrogenated PdCu phase. We identify a P4/mmm PdCuH2 structure, which is shown to be dynamically stable only upon the inclusion of quantum fluctuations, as being a key contributor to the measured superconductivity. For this system, our methodology is able to reduce the computational expense for the SSCHA calculations by ~96%. The proposed protocol opens the door towards the routine inclusion of quantum nuclear motion and anharmonicity in materials discovery.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"33 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758331","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

APEX: an automated cloud-native material property explorer

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-02 DOI: 10.1038/s41524-025-01580-y

Zhuoyuan Li, Tongqi Wen, Yuzhi Zhang, Xinzijian Liu, Chengqian Zhang, A. S. L. Subrahmanyam Pattamatta, Xiaoguo Gong, Beilin Ye, Han Wang, Linfeng Zhang, David J. Srolovitz

{"title":"APEX: an automated cloud-native material property explorer","authors":"Zhuoyuan Li, Tongqi Wen, Yuzhi Zhang, Xinzijian Liu, Chengqian Zhang, A. S. L. Subrahmanyam Pattamatta, Xiaoguo Gong, Beilin Ye, Han Wang, Linfeng Zhang, David J. Srolovitz","doi":"10.1038/s41524-025-01580-y","DOIUrl":"https://doi.org/10.1038/s41524-025-01580-y","url":null,"abstract":"The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials identification and design. This depends on the availability of accurate descriptions of atomic bonding and an efficient means for determining materials properties. We present an efficient, robust platform for calculating materials properties from a wide-range of atomic bonding descriptions, i.e., APEX, the Alloy Property Explorer. APEX enables the rapid evolution of interatomic potential development and optimization, which is of particular importance in fine-tuning new classes of general AI-based foundation models for applications in materials science and engineering. APEX is an open-source, extendable, cloud-native platform for material property calculations using a range of atomistic simulation methodologies that effectively manages diverse computational resources and is built upon user-friendly features including automatic results visualization, a web-based platform and a NoSQL database client. It is designed for expert and non-specialist users, lowering the barrier to entry for interdisciplinary research within an “AI for Materials” framework. We describe the foundation and use of APEX, as well as provide two examples of its application to properties of titanium and 179 metals and alloys for a wide-range of bonding descriptions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"31 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143758333","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

Application-oriented design of machine learning paradigms for battery science

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-02 DOI: 10.1038/s41524-025-01575-9

Ying Wang

引用次数: 0

Ab initio dynamical mean field theory with natural orbitals renormalization group impurity solver

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-03-31 DOI: 10.1038/s41524-025-01586-6

Jia-Ming Wang, Jing-Xuan Wang, Rong-Qiang He, Li Huang, Zhong-Yi Lu

{"title":"Ab initio dynamical mean field theory with natural orbitals renormalization group impurity solver","authors":"Jia-Ming Wang, Jing-Xuan Wang, Rong-Qiang He, Li Huang, Zhong-Yi Lu","doi":"10.1038/s41524-025-01586-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01586-6","url":null,"abstract":"In this study, we introduce a novel implementation of density functional theory integrated with single-site dynamical mean-field theory to investigate the complex properties of strongly correlated materials. This ab initio many-body computational toolkit, termed Zen, utilizes the VASP and Quantum ESPRESSO codes to perform first-principles calculations and generate band structures for realistic materials. The challenges associated with correlated electron systems are addressed through two distinct yet complementary quantum impurity solvers: the natural orbitals renormalization group solver for zero temperature and the hybridization expansion continuous-time quantum Monte Carlo solver for finite temperatures. To validate the performance of this toolkit, we examine three representative cases: correlated metal SrVO3, unconventional superconductor La3Ni2O7, and Mott insulator MnO. The calculated results exhibit excellent agreement with previously available experimental and theoretical findings. Thus, it is suggested that the Zen toolkit is proficient in accurately describing the electronic structures of d-electron correlated materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"102 4 Pt 1 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736981","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