npj Computational Materials最新文献_第2页

Machine learning enabled accurate prediction of structural and magnetic properties of cobalt ferrite

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-17 DOI: 10.1038/s41524-025-01598-2

Ying Fang, Suraj Mullurkara, Keith M. Taddei, Paul R. Ohodnicki, Guofeng Wang

引用次数: 0

Magnons from time-dependent density-functional perturbation theory and nonempirical Hubbard functionals

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-16 DOI: 10.1038/s41524-025-01570-0

Luca Binci, Nicola Marzari, Iurii Timrov

{"title":"Magnons from time-dependent density-functional perturbation theory and nonempirical Hubbard functionals","authors":"Luca Binci, Nicola Marzari, Iurii Timrov","doi":"10.1038/s41524-025-01570-0","DOIUrl":"https://doi.org/10.1038/s41524-025-01570-0","url":null,"abstract":"Spin excitations play a fundamental role in understanding magnetic properties of materials, and have significant technological implications for magnonic devices. However, accurately modeling these in transition-metal and rare-earth compounds remains a formidable challenge. Here, we present a fully first-principles approach for calculating spin-wave spectra based on time-dependent (TD) density-functional perturbation theory (DFPT), using nonempirical Hubbard functionals. This approach is implemented in a general noncollinear formulation, enabling the study of magnons in both collinear and noncollinear magnetic systems. Unlike methods that rely on empirical Hubbard U parameters to describe the ground state, and Heisenberg Hamiltonians for describing magnetic excitations, the methodology developed here probes directly the dynamical spin susceptibility (efficiently evaluated with TDDFPT throught the Liouville-Lanczos approach), and treats the linear variation of the Hubbard augmentation (in itself calculated non-empirically) in full at a self-consistent level. Furthermore, the method satisfies the Goldstone condition without requiring empirical rescaling of the exchange-correlation kernel or explicit enforcement of sum rules, in contrast to existing state-of-the-art techniques. We benchmark the novel computational scheme on prototypical transition-metal monoxides NiO and MnO, showing remarkable agreement with experiments and highlighting the fundamental role of these newly implemented Hubbard corrections. The method holds great promise for describing collective spin excitations in complex materials containing localized electronic states.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"218 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836646","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

Optical line shapes of color centers in solids from classical autocorrelation functions

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-16 DOI: 10.1038/s41524-025-01565-x

Christopher Linderälv, Nicklas Österbacka, Julia Wiktor, Paul Erhart

{"title":"Optical line shapes of color centers in solids from classical autocorrelation functions","authors":"Christopher Linderälv, Nicklas Österbacka, Julia Wiktor, Paul Erhart","doi":"10.1038/s41524-025-01565-x","DOIUrl":"https://doi.org/10.1038/s41524-025-01565-x","url":null,"abstract":"Color centers play key roles in, e.g., solid state lighting and quantum information technology. Here, we describe an approach for predicting the optical line shapes of such emitters based on direct sampling of the underlying autocorrelation functions through molecular dynamics simulations (MD-ACF). The energy landscapes are represented by a machine-learned potential that describes both the ground and excited state landscapes through a single model, guaranteeing size-consistent predictions. We apply this methodology to the ({({{rm{V}}}_{{rm{Si}}}{{rm{V}}}_{{rm{C}}})}_{kk}^{0}) divacancy defect in 4H-SiC and demonstrate that at low temperatures, the present MD-ACF approach reproduces results from the traditional generating function approach. Unlike the latter, it is, however, also applicable at high temperatures as it avoids harmonic and parallel-mode approximations and can be applied to study non-crystalline materials. The MD-ACF methodology thus promises to substantially widen the range of computational predictions of the optical properties of color centers and related defects.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"42 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836647","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

Atomistic simulations of out-of-equilibrium quantum nuclear dynamics

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-16 DOI: 10.1038/s41524-025-01588-4

Francesco Libbi, Anders Johansson, Lorenzo Monacelli, Boris Kozinsky

{"title":"Atomistic simulations of out-of-equilibrium quantum nuclear dynamics","authors":"Francesco Libbi, Anders Johansson, Lorenzo Monacelli, Boris Kozinsky","doi":"10.1038/s41524-025-01588-4","DOIUrl":"https://doi.org/10.1038/s41524-025-01588-4","url":null,"abstract":"The rapid advancements in ultrafast laser technology have paved the way for pumping and probing the out-of-equilibrium dynamics of nuclei in crystals. However, interpreting these experiments is extremely challenging due to the complex nonlinear responses in systems where lattice excitations interact, particularly in crystals composed of light atoms or at low temperatures where the quantum nature of ions becomes significant. In this work, we address the nonequilibrium quantum ionic dynamics from first principles. Our approach is general and can be applied to simulate any crystal, in combination with a first-principles treatment of electrons or external machine-learning potentials. It is implemented by leveraging the nonequilibrium time-dependent self-consistent harmonic approximation (TD-SCHA), with a stable, energy-conserving, correlated stochastic integration scheme that achieves an accuracy of ({mathcal{O}}(d{t}^{3})). We benchmark the method with both a simple one-dimensional model to test its accuracy and a realistic 40-atom cell of SrTiO3 under THz laser pump, paving the way for simulations of ultrafast THz-Xray pump-probe spectroscopy like those performed in synchrotron facilities.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"108 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841389","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

Transforming machine learning model knowledge into material insights for multi-principal-element superalloy phase design

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-14 DOI: 10.1038/s41524-025-01578-6

Qiuling Tao, Xintong Yang, Longke Bao, Yuexin Zhou, Tao Yang, Yilu Zhao, Rongpei Shi, Zhifu Yao, Xingjun Liu

{"title":"Transforming machine learning model knowledge into material insights for multi-principal-element superalloy phase design","authors":"Qiuling Tao, Xintong Yang, Longke Bao, Yuexin Zhou, Tao Yang, Yilu Zhao, Rongpei Shi, Zhifu Yao, Xingjun Liu","doi":"10.1038/s41524-025-01578-6","DOIUrl":"https://doi.org/10.1038/s41524-025-01578-6","url":null,"abstract":"Machine learning (ML) is a powerful tool for the accelerated design and development of various materials. However, the constructed ML models are often difficult to use by researchers other than the creator, that is, model sharing is a challenge. Here, we propose a method to avoid this issue by transforming the knowledge learned from ML models into material rules to obtain a generic design strategy. Specifically, we take the prediction of phase formation in multi-principal-element superalloys (MPESAs) as an example. First, we construct two classification models using ML algorithms to predict the presence or absence of the L12 phase and other phases, respectively. Then, the Shapley additive explanation method is used to extract knowledge from the models and transform them into understandable material insights. Based on this method, we obtain a generic design strategy for rapidly determining the phase formation of MPESAs, specifically the combination of (overline{{VEC}}) > 8, −16.0 < ∆Hmix < −9.7 J∙mol−1 ∙ K−1, and 1671 < (bar{{T}_{m}}) < 1822 K. This strategy enables the rapid and highly accurate (>98%) design of alloys with an “FCC + L12” dual-phase microstructure. We used this strategy to randomly select 12 candidates composed of different elements from the large design space for experimental preparation. The experimental results show that all these alloys exhibit the ideal “FCC + L12” dual-phase microstructure, verifying the accuracy of the design strategy. Notably, one of the alloys has a good combination of high solvus temperature (1218 °C) and very low density (7.77 g‧cm−3), superior to most MPESAs.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"17 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143827653","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

Interpretable multimodal machine learning analysis of X-ray absorption near-edge spectra and pair distribution functions 对 X 射线吸收近缘光谱和线对分布函数进行可解释的多模态机器学习分析

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-11 DOI: 10.1038/s41524-025-01589-3

Tanaporn Na Narong, Zoe N. Zachko, Steven B. Torrisi, Simon J. L. Billinge

{"title":"Interpretable multimodal machine learning analysis of X-ray absorption near-edge spectra and pair distribution functions","authors":"Tanaporn Na Narong, Zoe N. Zachko, Steven B. Torrisi, Simon J. L. Billinge","doi":"10.1038/s41524-025-01589-3","DOIUrl":"https://doi.org/10.1038/s41524-025-01589-3","url":null,"abstract":"We used interpretable machine learning to combine information from multiple heterogeneous spectra: X-ray absorption near-edge spectra (XANES) and atomic pair distribution functions (PDFs) to extract local structural and chemical environments of transition metal cations in oxides. Random forest models were trained on simulated XANES, PDF, and both combined to extract oxidation state, coordination number, and mean nearest-neighbor bond length. XANES-only models generally outperformed PDF-only models, even for structural tasks, although using the metal’s differential-PDFs (dPDFs) instead of total-PDFs narrowed this gap. When combined with PDFs, information from XANES often dominates the prediction. Our results demonstrate that XANES contains rich structural information and highlight the utility of species-specificity. This interpretable, multimodal approach is quick to implement with suitable databases and offers valuable insights into the relative strengths of different modalities, guiding researchers in experiment design and identifying when combining complementary techniques adds meaningful information to a scientific investigation.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"80 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819347","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

Generative deep learning for predicting ultrahigh lattice thermal conductivity materials

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-11 DOI: 10.1038/s41524-025-01592-8

Liben Guo, Yuanbin Liu, Zekun Chen, Hongao Yang, Davide Donadio, Bingyang Cao

{"title":"Generative deep learning for predicting ultrahigh lattice thermal conductivity materials","authors":"Liben Guo, Yuanbin Liu, Zekun Chen, Hongao Yang, Davide Donadio, Bingyang Cao","doi":"10.1038/s41524-025-01592-8","DOIUrl":"https://doi.org/10.1038/s41524-025-01592-8","url":null,"abstract":"Developing materials with ultrahigh thermal conductivity is crucial for thermal management and energy conversion. The recent development of generative models and machine learning (ML) holds great promise for predicting new functional materials. However, these data-driven methods are not tailored to identifying energetically stable structures and accurately predicting their thermal properties, as they lack physical constraints and information about the complexity of atomic many-body interactions. Here, we show how combining deep generative models of crystal structures with quantum-accurate, fast ML interatomic potentials can accelerate the prediction of materials with ultrahigh lattice thermal conductivity while ensuring energy optimality. We exploit structural symmetry and similarity metrics derived from atomic coordination environments to enable fast exploration of the structural space produced by the generative model. Additionally, we propose an active-learning-based protocol for the on-the-fly training of ML potentials to achieve high-fidelity predictions of stability and lattice thermal conductivity in prospective materials. Applying this method to carbon materials, we screen 100,000 candidates and identify 34 carbon polymorphs, approximately a quarter of which had not been previously predicted, to have lattice thermal conductivity above 800 W m−1 K−1, reaching up to 2,400 W m−1 K−1 aside from diamond. These findings provide a viable pathway toward the ML-assisted prediction of periodic materials with exceptional thermal properties.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"246 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143819348","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

Bayesian exploration of the composition space of CuZrAl metallic glasses for mechanical properties

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-04-07 DOI: 10.1038/s41524-025-01591-9

Tero Mäkinen, Anshul D. S. Parmar, Silvia Bonfanti, Mikko J. Alava

{"title":"Bayesian exploration of the composition space of CuZrAl metallic glasses for mechanical properties","authors":"Tero Mäkinen, Anshul D. S. Parmar, Silvia Bonfanti, Mikko J. Alava","doi":"10.1038/s41524-025-01591-9","DOIUrl":"https://doi.org/10.1038/s41524-025-01591-9","url":null,"abstract":"Designing metallic glasses in silico is a major challenge in materials science given their disordered atomic structure and the vast compositional space to explore. Here, we tackle this challenge by finding optimal compositions for target mechanical properties. We apply Bayesian exploration for the CuZrAl composition, a paradigmatic metallic glass known for its good glass forming ability. We exploit an automated loop with an online database, a Bayesian optimization algorithm, and molecular dynamics simulations. From the ubiquitous 50/50 CuZr starting point, we map the composition landscape, changing the ratio of elements and adding aluminum, to characterize the yield stress and the shear modulus. This approach demonstrates with relatively modest effort that the system has an optimal composition window for the yield stress around aluminum concentration cAl = 15% and zirconium concentration cZr = 30%. We also explore several cooling rates (“process parameters”) and find that the best mechanical properties for a composition result from being most affected by the cooling procedure. Our Bayesian approach paves the novel way for the design of metallic glasses with “small data”, with an eye toward both future in silico design and experimental applications exploiting this toolbox.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"20 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789961","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 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