npj Computational Materials最新文献_第5页

Microkinetic modeling of acidic corrosion from first principles and machine-learning molecular dynamics 基于第一性原理和机器学习分子动力学的酸性腐蚀微动力学建模

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-03-30 DOI: 10.1038/s41524-026-02047-4

Ergen Bao, Wenjing Xu, Hui Ma, Yueqi Si, Ijaz Shahid, Yutong Huo, Peitao Liu, Yan Sun, Xing-Qiu Chen

{"title":"Microkinetic modeling of acidic corrosion from first principles and machine-learning molecular dynamics","authors":"Ergen Bao, Wenjing Xu, Hui Ma, Yueqi Si, Ijaz Shahid, Yutong Huo, Peitao Liu, Yan Sun, Xing-Qiu Chen","doi":"10.1038/s41524-026-02047-4","DOIUrl":"https://doi.org/10.1038/s41524-026-02047-4","url":null,"abstract":"Acidic corrosion undermines infrastructure and energy systems, motivating quantitative models for corrosion prediction. However, existing models often lack accurate activation energies, neglect potential-dependent coverages, and oversimplify anodic dissolution as a concerted multi-electron event. Accordingly, we present a corrosion-prediction framework integrating first-principles calculations with machine-learning molecular dynamics. Our approach computes free-energy barriers at solid–liquid interfaces, incorporates competitive adsorption to determine potential-dependent surface coverages, and resolves Butler–Volmer kinetics of dissolution and hydrogen evolution. Applied to bcc-Fe(110), our model determines a 0.76 eV rate-determining barrier for anodic dissolution via an adsorbed FeOH intermediate, and identifies hydrogen evolution as Volmer-controlled. Predicted apparent activation energy, exchange current densities, corrosion potential, and corrosion current agree with experiment. Furthermore, the model successfully captures alloying effects, with Mn lowering both anodic and cathodic barriers and accelerating corrosion. Transferable and mechanism-based, our model offers a powerful tool for predicting corrosion across metals and guiding corrosion-resistant alloy design.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"19 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147586035","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

From ultrathin to bulk: decoding thickness-unrestricted ferroelectricity in Y:HfO2 via first-principles 从超薄到大块：通过第一性原理解码Y:HfO2中厚度不受限制的铁电性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-03-30 DOI: 10.1038/s41524-026-02046-5

Jin Huang, Jiangheng Yang, Shijie Jia, Junhui Wang, Fei Yan, Zhipeng Wang, Hua Chen, Jiajia Liao, Min Liao, Yichun Zhou

{"title":"From ultrathin to bulk: decoding thickness-unrestricted ferroelectricity in Y:HfO2 via first-principles","authors":"Jin Huang, Jiangheng Yang, Shijie Jia, Junhui Wang, Fei Yan, Zhipeng Wang, Hua Chen, Jiajia Liao, Min Liao, Yichun Zhou","doi":"10.1038/s41524-026-02046-5","DOIUrl":"https://doi.org/10.1038/s41524-026-02046-5","url":null,"abstract":"Doped hafnium oxide (HfO2) ferroelectrics show great potential in next-generation memory and compute-in-memory applications due to their compatibility with advanced silicon-based technology. Typically, HfO2 shows a reverse size effect, where the polar orthorhombic phase (PO, space group Pca21) is stabilized only at thicknesses of a few nanometers. Yttrium-doped hafnium oxide (Y:HfO2) exhibits a distinct behavior, maintaining robust polarization from ultrathin films to bulk crystals. However, the mechanism enabling Y:HfO2 ferroelectricity which is critical for expanding device scalability and performance remains unclear. In this work, the multi-field stabilization mechanisms of the PO phase are systematically investigated for bulk and thin film Y:HfO2 via first-principles calculations. The synergistic effect of composite defects combined with Y dopants and tetra-coordinated oxygen vacancies (Y+VO4), strain, and electric field significantly broadens the window of thermodynamically metastable PO phase. Notably, we find that the strain requirement can be significantly relaxed with increasing concentration of Y dopants or Y+VO4 defect pairs, revealing the feasibility of achieving ferroelectricity in bulk Y:HfO2 without substrate constraints. Moreover, we demonstrate an increase in critical thickness to stabilize the PO phase in Y:HfO2 thin film compared with pure HfO2, which is ascribed to the effects of Y+VO4 defects on the surface energy. These findings clarify the key role of Y+VO4 defects in realizing thickness-unrestricted ferroelectricity in Y: HfO2 and provide critical theoretical guidance for optimizing the fabrication process of high-performance HfO2-based ferroelectric devices.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"239 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147586034","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

Electron-phonon physics at the exascale: a hybrid MPI-GPU-OpenMP framework for scalable Wannier interpolation 百亿亿级的电子-声子物理：用于可扩展万尼尔插值的MPI-GPU-OpenMP混合框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-03-28 DOI: 10.1038/s41524-026-02042-9

Tae Yun Kim, Zhe Liu, Sabyasachi Tiwari, Elena R. Margine, Feliciano Giustino

{"title":"Electron-phonon physics at the exascale: a hybrid MPI-GPU-OpenMP framework for scalable Wannier interpolation","authors":"Tae Yun Kim, Zhe Liu, Sabyasachi Tiwari, Elena R. Margine, Feliciano Giustino","doi":"10.1038/s41524-026-02042-9","DOIUrl":"https://doi.org/10.1038/s41524-026-02042-9","url":null,"abstract":"We demonstrate a highly efficient GPU implementation of the Wannier interpolation of electron-phonon matrix elements in the EPW code. Building on a systematic analysis of the computational complexity of the algorithm for electron-phonon interpolation, we designed a GPU porting strategy that integrates naturally into the current EPW implementation and is seamlessly portable to NVIDIA, AMD, and Intel GPUs. We demonstrate this development via extensive benchmarks on conventional semiconductors such as silicon and monolayer MoS2, as well as a large-scale application to topological stanene nanoribbons of width as large as 20 nm, which was intractable with previous implementations. Compared to the single MPI parallelization scheme of EPW v5.9, the resulting hybrid MPI-GPU-OpenMP scheme achieves up to 29-fold speedup on leadership-class supercomputers equipped with NVIDIA and Intel accelerators, namely Vista at the Texas Advanced Computing Center, Perlmutter at the National Energy Research Scientific Computing Center, and Aurora at the Argonne Leadership Computing Facility. This framework also achieves nearly ideal scalability up to thousands of GPU nodes on the Aurora supercomputer. With this development, EPW is ready to support electron-phonon physics calculations on exascale platforms.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"16 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147536131","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

Prediction of ambient-pressure high-temperature superconductivity in electronically modified transition-metal hydrides 电子修饰过渡金属氢化物的常压高温超导性预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-03-28 DOI: 10.1038/s41524-026-02040-x

Haowei Xu, Olivia Schneble, Rafael Jaramillo, Marek Polański, Ju Li

{"title":"Prediction of ambient-pressure high-temperature superconductivity in electronically modified transition-metal hydrides","authors":"Haowei Xu, Olivia Schneble, Rafael Jaramillo, Marek Polański, Ju Li","doi":"10.1038/s41524-026-02040-x","DOIUrl":"https://doi.org/10.1038/s41524-026-02040-x","url":null,"abstract":"The search for conventional superconductors with high transition temperatures (({T}_{c})) has largely focused on intrinsically metallic compounds. In this work, we explore the potential of intrinsically non-metallic compounds to exhibit high-({T}_{c}) superconductivity under ambient pressure through carrier doping. We identify MgAlFeH6, a representative of carrier-doped transition-metal hydrides like Mg2FeH6, as a promising example with a predicted ({T}_{c}approx 130,{rm{K}}). We propose that the average projected electron density of states (DOS), defined as the geometric mean of the total and hydrogen-projected DOS at the Fermi level, serves as a simple and computationally inexpensive indicator of high-({T}_{c}) behavior. Notably, the correlation between ({T}_{c}) and the average projected DOS is stronger than that between ({T}_{c}) and either total DOS or hydrogen-projected DOS. We also highlight the tradeoff between high-({T}_{c}) and dynamic stability, both of which depend on the electron DOS at the Fermi level. Our findings thus expand the pool of potential superconducting materials and offer a practical route for accelerating the discovery of superconductors suitable for real-world applications.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"18 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147536132","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 general purposed machine learning interatomic potential for Mg-Al-Si-O system suitable for Earth materials at high pressure and temperature conditions 适用于高压和高温条件下地球材料的Mg-Al-Si-O体系的通用机器学习原子间势

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-03-27 DOI: 10.1038/s41524-026-02056-3

Xin Zhong, Yifan Li, Timm John

{"title":"A general purposed machine learning interatomic potential for Mg-Al-Si-O system suitable for Earth materials at high pressure and temperature conditions","authors":"Xin Zhong, Yifan Li, Timm John","doi":"10.1038/s41524-026-02056-3","DOIUrl":"https://doi.org/10.1038/s41524-026-02056-3","url":null,"abstract":"Accurate phase diagrams and thermodynamic properties of Earth materials are essential for advancing geophysical, geodynamical and geological studies. Apart from experiment, atomistic simulations, particularly molecular dynamics, can be used to obtain thermodynamic data, but they often fail to reproduce correct phase relations. In this study, we develop a machine learning interatomic potential for the Mg–Al–Si–O system, optimized for accuracy and computational speed. Among several tested functionals, the r2SCAN exchange-correlation functional proves most suitable for generating training data encompassing over 20 minerals and melts. To enhance accuracy, a pairwise Gaussian correction is applied, reducing the energy error from 5.2 kJ/mol to 1.2 kJ/mol. Predicted isochemical phase diagrams show good agreement with experiments. Beyond phase diagrams, we calculate solid-melt interfacial free energy for periclase and forsterite and find that the anisotropy of solid-melt interfacial free energy is low (6%) for periclase and moderate (12%) for forsterite. The influence of nonhydrostatic stress on the α-β quartz transition is systematically examined, demonstrating that mean stress serves as a reliable proxy with about 17% error. This work illustrates that molecular dynamics simulations powered by machine learning interatomic potentials offer a powerful approach to investigating the physical properties of deep Earth materials.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"29 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147536133","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

Rational design of polymeric dielectrics guided by insightful understanding of electron transfer/transport in aperiodic systems 在对非周期系统中电子转移/输运的深刻理解的指导下，合理设计聚合物介电体

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-03-26 DOI: 10.1038/s41524-026-02052-7

Shixun Hu, Li Meng, Mingti Wang, Jiahui Zhang, Jun Hu, Hao Yuan, Qi Li, Jinliang He

{"title":"Rational design of polymeric dielectrics guided by insightful understanding of electron transfer/transport in aperiodic systems","authors":"Shixun Hu, Li Meng, Mingti Wang, Jiahui Zhang, Jun Hu, Hao Yuan, Qi Li, Jinliang He","doi":"10.1038/s41524-026-02052-7","DOIUrl":"https://doi.org/10.1038/s41524-026-02052-7","url":null,"abstract":"Electron transfer and transport constitute the fundamental mechanisms governing the performance of polymeric dielectrics, yet their microscopic nature remains elusive due to the intrinsic complexity of aperiodic condensed states. This investigation presents, for the first time, a computational-experimental exploration on quantitative, real-space orbital electron transfer and quantum electron transport, which have been largely overlooked in aperiodic systems. The energy barrier and spatial confinement of unoccupied frontier orbitals play a pivotal role in regulating electron transfer, which is predictable and experimentally characterizable, and dictates dielectric performance. Additionally, the structure-dependent quantum current strongly influences the macroscopic conduction characteristics. These insights enable precise regulation of dielectric performance via chemically superseding frontier orbitals. We hence apply this approach to a typical polymeric system and propose a design principle comprising three ab-initio descriptors. Our results substantiate the validity of this rationale, offering renewed insights into electron dynamics in aperiodic systems and guiding future dielectric design.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"5 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147536135","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

Mechanical property prediction of superalloys with microporosity defects using a multi-source deep learning framework 基于多源深度学习框架的微孔缺陷高温合金力学性能预测

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-03-26 DOI: 10.1038/s41524-026-02055-4

Huipeng Yu, Chenyang Ding, Maodong Kang, Yunting Li, Yahui Liu, Jun Wang, Wei Xiong, Baode Sun

{"title":"Mechanical property prediction of superalloys with microporosity defects using a multi-source deep learning framework","authors":"Huipeng Yu, Chenyang Ding, Maodong Kang, Yunting Li, Yahui Liu, Jun Wang, Wei Xiong, Baode Sun","doi":"10.1038/s41524-026-02055-4","DOIUrl":"https://doi.org/10.1038/s41524-026-02055-4","url":null,"abstract":"This study proposed a novel multi-source deep learning framework for predicting the tensile properties of superalloys containing microporosity defects by simultaneously incorporating both microstructure and defect features. A comprehensive multi-source dataset was constructed using multi-source microstructure and microporosity defect images obtained from tensile specimens extracted from cast plates with diverse microstructural and defect characteristics. The target mechanical properties include ultimate tensile strength (UTS), yield strength (YS), and elongation (EL). Compared to models trained using only microstructure or defect images, the proposed multi-source framework achieved superior prediction accuracy, with R2 values exceeding 0.93 for all three properties. In addition, the mean absolute error (MAE) decreased with an increasing number of microstructural image channels, indicating that the incorporation of multi-source microstructural features significantly enhances model performance. Furthermore, an explainable AI methodology was applied to reveal the underlying mechanisms by which microstructural and defect features govern tensile behavior. This framework presents a data-driven methodology for uncovering microstructure-defect-property relationships, providing a potential pathway for accurate mechanical property prediction of defect-containing superalloys. Its modular architecture can also be readily applied to other alloys.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"21 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147536134","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

Scalable foundation interatomic potentials via message-passing pruning and graph partitioning 通过消息传递修剪和图划分可伸缩的基础原子间势

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-03-26 DOI: 10.1038/s41524-026-02001-4

Lingyu Kong, Jaeheon Shim, Guoxiang Hu, Victor Fung

{"title":"Scalable foundation interatomic potentials via message-passing pruning and graph partitioning","authors":"Lingyu Kong, Jaeheon Shim, Guoxiang Hu, Victor Fung","doi":"10.1038/s41524-026-02001-4","DOIUrl":"https://doi.org/10.1038/s41524-026-02001-4","url":null,"abstract":"Atomistic foundation models (AFMs) have great promise as accurate interatomic potentials, and have enabled data-efficient molecular dynamics simulations with near quantum mechanical accuracy. However, AFMs remain markedly slower at inference and are far more memory-intensive than conventional interatomic potentials, due to the need to capture a wide range of chemical and structural motifs in pre-training datasets requiring deep, parameter-rich model architectures. These deficiencies currently limit the practical use of AFMs in molecular dynamics (MD) simulations at extended temporal and spatial scales. To address this problem, we propose a general workflow for accelerating and scaling AFMs containing message-passing architectures. We find that removing low-contribution message-passing layers from AFM backbones serves as an effective pruning method, significantly reducing the parameter count while preserving the accuracy and data-efficiency of AFMs. Once pruned, these models become more accessible for large scale simulations via a graph-partitioned, GPU-distributed strategy, which we implement and demonstrate within the AFM fine-tuning platform MatterTune. We show that this approach supports million-atom simulations on both single and multiple GPUs, and enables task-specific large-scale simulations at nanosecond timescales with AFM-level accuracy.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"4 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147536136","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

Incorporating long-range interactions via the multipole expansion into ground and excited-state molecular simulations 结合远程相互作用，通过多极膨胀到基态和激发态分子模拟

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-03-25 DOI: 10.1038/s41524-026-02048-3

Rhyan Barrett, Johannes C. B. Dietschreit, Julia Westermayr

{"title":"Incorporating long-range interactions via the multipole expansion into ground and excited-state molecular simulations","authors":"Rhyan Barrett, Johannes C. B. Dietschreit, Julia Westermayr","doi":"10.1038/s41524-026-02048-3","DOIUrl":"https://doi.org/10.1038/s41524-026-02048-3","url":null,"abstract":"Simulating long-range interactions remains a significant challenge for molecular machine learning (ML) potentials due to the need to accurately capture interactions over large spatial regions. In this work, we integrate the multipole expansion into equivariant ML potentials to model long-range interactions present in QM/MM simulations more accurately. By incorporating the multipole expansion, we are able to effectively capture environmental long-range effects in both ground and excited states. Benchmark evaluations demonstrate the superior performance of including higher-order features from atoms in the environment. To showcase the efficacy of our model, we accurately predict properties such as energies and forces for nickel complex systems and simulate the nonadiabatic excited-state dynamics of a ring-opening reaction in solution. Furthermore, we show that transfer learning from foundational models trained without any explicit environment enhances data efficiency, reducing the need to generate large QM/MM datasets before training. These examples demonstrate the versatility of our approach, paving the way for efficient, accurate, and scalable simulations of complex molecular systems and materials across electronic states.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147506163","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

Publisher Correction: Active learning enables generation of molecules that advance the known Pareto front 发布者更正：主动学习能够生成推进已知帕累托前沿的分子

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2026-03-24 DOI: 10.1038/s41524-026-02039-4

Evan R. Antoniuk, Peggy Li, Nathan Keilbart, Stephen Weitzner, Bhavya Kailkhura, Anna M. Hiszpanski

引用次数: 0