npj Computational Materials最新文献_第3页

Artificial catalyst generation for the oxygen reduction reaction using conditional variational autoencoder and atomistic calculations 用条件变分自编码器和原子计算生成氧还原反应的人工催化剂

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-14 DOI: 10.1038/s41524-026-02075-0

Taishiro Wakamiya, Atsushi Ishikawa

{"title":"Artificial catalyst generation for the oxygen reduction reaction using conditional variational autoencoder and atomistic calculations","authors":"Taishiro Wakamiya, Atsushi Ishikawa","doi":"10.1038/s41524-026-02075-0","DOIUrl":"https://doi.org/10.1038/s41524-026-02075-0","url":null,"abstract":"We developed a method that generates catalyst structures for the oxygen reduction reaction (ORR) by combining atomistic-scale calculations with a conditional variational autoencoder (CVAE). The CVAE was trained with overpotential (η) and alloy formation energy (Eform) as conditional labels and used to generate new structures. The neural-network potential (NNP) was used to evaluate η and Eform for the generated materials. This CVAE-generation and NNP-evaluation procedure enables iterative improvement of the dataset, as data for generated samples can be added to the previous dataset. We applied this method to Pt–Ni alloys. Across six iterations (128 initial and 128 added per iteration), the distributions shifted toward lower η and more negative Eform. The mean value of the dataset was varied from η = 1.126 to 0.520 V and from Eform = −0.027 to −0.047 eV/atom. This result demonstrates that both the activity and stability were improved simultaneously. Latent-space analysis revealed that the CVAE explored areas of the data space not present in the initial data, creating Pt-rich surface structures consistent with previously known ORR design principles. This method accelerates inverse design of alloy catalysts and provides a general approach for discovering structures that jointly satisfy high activity and thermodynamic stability.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"47 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685157","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

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations 非调和声子特性的数据库和深度学习可扩展性，自动化蛮力第一原理计算

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-13 DOI: 10.1038/s41524-026-02033-w

Masato Ohnishi, Tianqi Deng, Pol Torres, Zhihao Xu, Terumasa Tadano, Haoming Zhang, Wei Nong, Masatoshi Hanai, Zeyu Wang, Michimasa Morita, Zhiting Tian, Ming Hu, Xiulin Ruan, Ryo Yoshida, Toyotaro Suzumura, Lucas Lindsay, Alan J. H. McGaughey, Tengfei Luo, Kedar Hippalgaonkar, Junichiro Shiomi

{"title":"Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations","authors":"Masato Ohnishi, Tianqi Deng, Pol Torres, Zhihao Xu, Terumasa Tadano, Haoming Zhang, Wei Nong, Masatoshi Hanai, Zeyu Wang, Michimasa Morita, Zhiting Tian, Ming Hu, Xiulin Ruan, Ryo Yoshida, Toyotaro Suzumura, Lucas Lindsay, Alan J. H. McGaughey, Tengfei Luo, Kedar Hippalgaonkar, Junichiro Shiomi","doi":"10.1038/s41524-026-02033-w","DOIUrl":"https://doi.org/10.1038/s41524-026-02033-w","url":null,"abstract":"Understanding the anharmonic phonon properties of crystal compounds—such as phonon lifetimes and thermal conductivities—is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6500 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities—both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"65 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685158","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

Machine learning the entropy to estimate free energy differences without sampling transitions 机器学习的熵估计自由能差没有采样过渡

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-13 DOI: 10.1038/s41524-026-02076-z

Yamin Ben-Shimon, Barak Hirshberg, Yohai Bar-Sinai

{"title":"Machine learning the entropy to estimate free energy differences without sampling transitions","authors":"Yamin Ben-Shimon, Barak Hirshberg, Yohai Bar-Sinai","doi":"10.1038/s41524-026-02076-z","DOIUrl":"https://doi.org/10.1038/s41524-026-02076-z","url":null,"abstract":"Thermodynamic phase transitions, a central concept in physics and chemistry, are typically controlled by an interplay of enthalpic and entropic contributions. In most cases, the estimation of the enthalpy in simulations is straightforward but evaluating the entropy is notoriously hard. As a result, it is common to induce transitions between the metastable states and estimate their relative occupancies, from which the free energy difference can be inferred. However, for systems with large free energy barriers, sampling these transitions is a significant computational challenge. Dedicated enhanced sampling algorithms require significant prior knowledge of the slow modes governing the transition, which is typically unavailable. We present an alternative approach, which only uses short simulations of each phase separately. We achieve this by employing a recently developed deep learning model for estimating the entropy and hence the free energy of each metastable state. We benchmark our approach by calculating the free energies of crystalline and liquid metals. Our method features state-of-the-art precision in estimating the melting transition temperature in Na and Al without requiring any prior information or simulation of the transition pathway itself.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"16 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685159","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

Breaking equiatomic constraints: knowledge-enhanced AI framework for function-oriented single-phase high-entropy alloy design 打破等原子约束：面向功能的单相高熵合金设计的知识增强AI框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-13 DOI: 10.1038/s41524-026-02071-4

Huiting Huang, Yeyong Yu, Weilun Deng, Quan Qian, Hongxing Zheng

{"title":"Breaking equiatomic constraints: knowledge-enhanced AI framework for function-oriented single-phase high-entropy alloy design","authors":"Huiting Huang, Yeyong Yu, Weilun Deng, Quan Qian, Hongxing Zheng","doi":"10.1038/s41524-026-02071-4","DOIUrl":"https://doi.org/10.1038/s41524-026-02071-4","url":null,"abstract":"High-entropy alloys exhibit exceptional performance under extreme environments; however, conventional equiatomic or near-equiatomic design paradigms restrict exploration of single-phase solid-solution spaces, leaving many non-equiatomic compositions unexplored. Here, we propose a knowledge-enhanced AI framework integrating physics-guided machine learning with large language model (LLM) agents to explore compositions beyond equiatomic constraints. Machine learning models trained on physics-based descriptors achieve F1-scores of 0.96 and 0.94 for face-centered cubic (FCC) and body-centered cubic (BCC) phase prediction, respectively. Two substantially non-equiatomic alloys—Al28Cr22Fe26Ni15Mo9 (BCC) and Co23Cr15Fe25Ni31Mo6 (FCC)—experimentally confirm reliable phase prediction within the investigated compositional domain. LLM-guided reasoning on oxidation mechanisms further enables the design of Co22Cr24Fe20Ni25Mo5Mn4, which exhibits a steady-state oxidation rate constant of 1.41 × 10−8 mgn cm−2n h−1 (50–100 h, n = 45.35) at 900 °C, indicative of self-limiting oxidation kinetics. This superior performance is attributed to the formation of a stable Cr2O3/(Mn,Fe)Cr2O4 bilayer oxide scale operating via a synergistic barrier–buffer protection mechanism. This study presents a data-efficient, AI-assisted methodology for intelligent HEA design in high-dimensional compositional spaces.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"22 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685160","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

Genetic algorithm coupled with thermodynamic calculations for high-strength nickel-based corrosion-resistant alloys design 遗传算法与热力学计算相结合的高强度镍基耐蚀合金设计

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-13 DOI: 10.1038/s41524-026-02068-z

Yingjie Sun, Yong Lian, Rongrong Chen, Piao Qian, Jin Zhang, Yinghu Wang, Qubo He, Dadi Zhou, Hengcan Yang

{"title":"Genetic algorithm coupled with thermodynamic calculations for high-strength nickel-based corrosion-resistant alloys design","authors":"Yingjie Sun, Yong Lian, Rongrong Chen, Piao Qian, Jin Zhang, Yinghu Wang, Qubo He, Dadi Zhou, Hengcan Yang","doi":"10.1038/s41524-026-02068-z","DOIUrl":"https://doi.org/10.1038/s41524-026-02068-z","url":null,"abstract":"As oil and gas exploration expands to deep-sea, ultra-deep, and unconventional reservoirs, demands for nickel-based corrosion-resistant alloys' mechanical performance grow stricter. Traditional trial-and-error alloy development is time-consuming and costly, making them inadequate for rapid alloy design. This study developed an interpretable composition–microstructure–property optimization model by integrating genetic algorithm, machine learning, and thermodynamic calculations to enable fast design of ultra-high-strength age-hardened nickel-based corrosion-resistant alloys. Thermo-Calc (via TC-Python) calculated γ′/γ″ phase volume fractions and precipitation driving forces under different compositions, used as input for a machine learning-based yield strength prediction model. An AdaBoost regressor was trained and embedded into the genetic algorithm as the fitness function to perform constrained composition optimization. The optimized alloy, ML1, exhibited excellent mechanical performance under various heat treatment conditions. Notably, after solution treatment at 1030 °C followed by aging, it achieved a yield strength of 1365 MPa and an ultimate tensile strength of 1539 MPa—significantly outperforming most commercial precipitation-hardened nickel-based corrosion-resistant alloys. The proposed optimization framework effectively reduces development time and cost compared to conventional methods and is extendable to other precipitation-hardened alloy systems. It offers a new strategy for data-driven alloy design with both scientific value and engineering applicability.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"280 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147685194","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

CALPHAD-based cross-system knowledge transfer for rapid discovery of high-performance Al–Mg–Zn alloys 基于calphad的跨系统知识转移快速发现高性能Al-Mg-Zn合金

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-10 DOI: 10.1038/s41524-026-02073-2

Wuwei Mo, Qiang Lu, Xiaoyu Zheng, Mengchu Yang, Yinping Zeng, Kai Li, Bo Li, Yuling Liu, Yong Du

{"title":"CALPHAD-based cross-system knowledge transfer for rapid discovery of high-performance Al–Mg–Zn alloys","authors":"Wuwei Mo, Qiang Lu, Xiaoyu Zheng, Mengchu Yang, Yinping Zeng, Kai Li, Bo Li, Yuling Liu, Yong Du","doi":"10.1038/s41524-026-02073-2","DOIUrl":"https://doi.org/10.1038/s41524-026-02073-2","url":null,"abstract":"Machine learning is widely used to accelerate materials design, but its performance remains limited in small-data scenarios, reducing effectiveness in guiding novel materials development. Here, we developed a novel representation transfer framework based on the Calculation of Phase Diagrams (CALPHAD) method to address the challenge. By integrating CALPHAD with feature engineering, the thermodynamics-informed descriptors related to strength and ductility were constructed and selected. These descriptors capture the coupled effects of composition and temperature on material properties, offering strong physical interpretability and enabling a knowledge transfer from well-studied 2xxx, 6xxx and 7xxx series aluminum alloys to the underexplored Al–Mg–Zn alloys. Subsequently, by coupling high-throughput CALPHAD calculations with the NSGA-II algorithm, the strength–ductility Pareto front is efficiently identified to guide alloy design. Experimental validation confirmed that the two designed alloys achieved ultimate tensile strengths of 472 ± 7 MPa and 569 ± 12 MPa, with elongations of 23.5 ± 0.5% and 14.9 ± 0.3%, respectively, demonstrating improved strength–ductility synergy in the Al–Mg–Zn system. This framework enhances model generalization and interpretability in small-data scenarios, offering a versatile strategy for rapid discovery of high-performance materials across diverse systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"21 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147655988","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 domain-specific machine learning potential model for metallic materials spanning 53 elements 涵盖53个元素的金属材料的特定领域机器学习潜力模型

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-10 DOI: 10.1038/s41524-026-02072-3

Xin-Yang Li, Jing Li, Yi-Nan Wang, Na-Min Xiao, Wen-Yue Zhao, Zhang-Zhi Shi, Xin-Fu Gu, Fu-Zhi Dai, Lu-Ning Wang

{"title":"A domain-specific machine learning potential model for metallic materials spanning 53 elements","authors":"Xin-Yang Li, Jing Li, Yi-Nan Wang, Na-Min Xiao, Wen-Yue Zhao, Zhang-Zhi Shi, Xin-Fu Gu, Fu-Zhi Dai, Lu-Ning Wang","doi":"10.1038/s41524-026-02072-3","DOIUrl":"https://doi.org/10.1038/s41524-026-02072-3","url":null,"abstract":"Alloys have been the cornerstone of human societal progress, from the Bronze Age to modern sustainable technologies. Yet, their atomic-scale behavior remains poorly understood, impeding their targeted optimization. Thus, reliable and efficient material design tools are urgently needed to accelerate alloy development. To address this demand, we develop a domain-specific machine learning potential (MLP) model spanning 53 metallic elements with balanced accuracy and efficiency. The model achieves DFT-level precision: energy mean absolute error (MAE) = 12 meV/atom, force MAE = 144 meV/Å, accurately predicts lattice parameters, elastic constants, and equation of states. We further validate its versatility through four alloy systems: (1) negative thermal expansion in Ti-Nb orthorhombic phases, (2) the Elinvar effect in Co25Ni25(TiZrHf)50 intermetallic compound, (3) grain boundary segregation and high-temperature deformation in NbTaMoW multi-principal element alloy, and (4) precipitation pathway and θ′/Al interface segregation in Al-Cu-based alloy. This model provides a foundational tool for atomic-scale simulation, advancing materials research and accelerating alloy design.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"67 3 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147655989","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

Automated modeling of polarons: defects and reactivity on TiO2(110) surfaces 极化子的自动建模：TiO2（110）表面的缺陷和反应性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-09 DOI: 10.1038/s41524-026-01983-5

Firat Yalcin, Carla Verdi, Viktor C. Birschitzky, Matthias Meier, Michael Wolloch, Michele Reticcioli

{"title":"Automated modeling of polarons: defects and reactivity on TiO2(110) surfaces","authors":"Firat Yalcin, Carla Verdi, Viktor C. Birschitzky, Matthias Meier, Michael Wolloch, Michele Reticcioli","doi":"10.1038/s41524-026-01983-5","DOIUrl":"https://doi.org/10.1038/s41524-026-01983-5","url":null,"abstract":"Polarons are widespread in functional materials and are key to device performance in several technological applications. However, their effective impact on material behavior remains elusive, as condensed matter studies struggle to capture their intricate interplay with atomic defects in the crystal. In this work, we present an automated workflow for modeling polarons within density functional theory (DFT). Our approach enables a fully automatic identification of the most favorable polaronic configurations in the system. Machine learning techniques accelerate predictions, allowing for an efficient exploration of the defect-polaron configuration space. We apply this methodology to Nb-doped TiO2(110) surfaces, providing new insights into the role of defects in surface reactivity. Using CO adsorbates as a probe, we find that Nb doping has minimal impact on reactivity, whereas oxygen vacancies contribute significantly depending on their local arrangement via the stabilization of polarons on the surface atomic layer. Our package streamlines the modeling of charge trapping and polaron localization with high efficiency, enabling systematic, large-scale investigations of polaronic effects across complex material systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147655990","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

PRISM: periodic representation with multiscale and similarity graph modelling for enhanced crystal structure property prediction PRISM：基于多尺度和相似图模型的周期表示，用于增强晶体结构性质预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-08 DOI: 10.1038/s41524-026-02074-1

Àlex Solé, Albert Mosella-Montoro, Joan Cardona, Daniel Aravena, Silvia Gómez-Coca, Eliseo Ruiz, Javier Ruiz-Hidalgo

引用次数: 0

Collecting diverse near-optimal samples via nested Thompson sampling 通过嵌套汤普森抽样收集各种接近最优的样本

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-04-07 DOI: 10.1038/s41524-026-02067-0

Ryosuke Shibukawa, Shoichi Matsuda, Kazuha Nakamura, Ryo Tamura, Koji Tsuda

引用次数: 0