npj Computational Materials最新文献_第9页

Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-09 DOI: 10.1038/s41524-024-01475-4

Jason B. Gibson, Ajinkya C. Hire, Philip M. Dee, Oscar Barrera, Benjamin Geisler, Peter J. Hirschfeld, Richard G. Hennig

{"title":"Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function","authors":"Jason B. Gibson, Ajinkya C. Hire, Philip M. Dee, Oscar Barrera, Benjamin Geisler, Peter J. Hirschfeld, Richard G. Hennig","doi":"10.1038/s41524-024-01475-4","DOIUrl":"https://doi.org/10.1038/s41524-024-01475-4","url":null,"abstract":"Integrating deep learning with the search for new electron-phonon superconductors represents a burgeoning field of research, where the primary challenge lies in the computational intensity of calculating the electron-phonon spectral function, α2F(ω), the essential ingredient of Midgal-Eliashberg theory of superconductivity. To overcome this challenge, we adopt a two-step approach. First, we compute α2F(ω) for 818 dynamically stable materials. We then train a deep-learning model to predict α2F(ω), using a training strategy tailored for limited data to temper the model’s overfitting, enhancing predictions. Specifically, we train a Bootstrapped Ensemble of Tempered Equivariant graph neural NETworks (BETE-NET), obtaining an MAE of 0.21, 45 K, and 43 K for the moments derived from α2F(ω): λ, ({omega }_{log }), and ω2, respectively, yielding an MAE of 2.5 K for the critical temperature, Tc. Further, we incorporate domain knowledge of the site-projected phonon density of states to impose inductive bias into the model’s node attributes and enhance predictions. This methodological innovation decreases the MAE to 0.18, 29 K, and 28 K, respectively, yielding an MAE of 2.1 K for Tc. We illustrate the practical application of our model in high-throughput screening for high-Tc materials. The model demonstrates an average precision nearly five times higher than random screening, highlighting the potential of ML in accelerating superconductor discovery. BETE-NET accelerates the search for high-Tc superconductors while setting a precedent for applying ML in materials discovery, particularly when data is limited.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"61 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937113","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

Understanding the intrinsic piezoelectric anisotropy of tetragonal ABO3 perovskites through a high-throughput study 通过高通量研究了解四方ABO3钙钛矿的压电各向异性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-08 DOI: 10.1038/s41524-024-01496-z

Fanhao Jia, Shaowen Xu, Shunbo Hu, Jianguo Chen, Yongchen Wang, Yuan Li, Wei Ren, Jinrong Cheng

{"title":"Understanding the intrinsic piezoelectric anisotropy of tetragonal ABO3 perovskites through a high-throughput study","authors":"Fanhao Jia, Shaowen Xu, Shunbo Hu, Jianguo Chen, Yongchen Wang, Yuan Li, Wei Ren, Jinrong Cheng","doi":"10.1038/s41524-024-01496-z","DOIUrl":"https://doi.org/10.1038/s41524-024-01496-z","url":null,"abstract":"A comprehensive understanding of the intrinsic piezoelectric anisotropy stemming from diverse chemical and physical factors is a key step for the rational design of highly anisotropic materials. We performed high-throughput calculations on tetragonal ABO3 perovskites to investigate the overall characteristics of their piezoelectricity and the interplay between lattice, displacement, polarization, and elasticity. Among the screened 123 types of perovskites, the structural tetragonality is naturally divided into two categories: normal tetragonal (c/a ratio < 1.1) and super-tetragonal (c/a ratio > 1.17), exhibiting distinct chemical features, ferroelectric, elastic, and piezoelectric properties. Charge analysis revealed the mechanisms underlying polarization saturation and piezoelectricity suppression in the super-tetragonal region, which also produces an inherent contradiction between high piezoelectric coefficient d33 and large piezoelectric anisotropy ratio |d33/d31|. Both the polarization axis and elastic softness direction are strongly correlated to piezoelectric anisotropy, which jointly determines the direction of maximum longitudinal piezoelectric response d33. The validity and deficiencies of the widely utilized |d33/d31| ratio for representing piezoelectric anisotropy were reevaluated.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"43 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935528","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

Cluster sliding ferroelectricity in trilayer Quasi-Hexagonal C60 三层准六方C60的团簇滑动铁电性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-08 DOI: 10.1038/s41524-024-01511-3

Xuefei Wang, Yanhan Ren, Shi Qiu, Fan Zhang, Xueao Li, Junfeng Gao, Weiwei Gao, Jijun Zhao

{"title":"Cluster sliding ferroelectricity in trilayer Quasi-Hexagonal C60","authors":"Xuefei Wang, Yanhan Ren, Shi Qiu, Fan Zhang, Xueao Li, Junfeng Gao, Weiwei Gao, Jijun Zhao","doi":"10.1038/s41524-024-01511-3","DOIUrl":"https://doi.org/10.1038/s41524-024-01511-3","url":null,"abstract":"Electric polarization typically originates from non-centrosymmetric charge distributions in compounds. In elemental crystalline materials, chemical bonds between atoms of the same element favor symmetrically distributed electron charges and centrosymmetric structures, making elemental ferroelectrics rare. Compared to atoms, elemental clusters are intrinsically less symmetric and can have various preferred orientations when they are assembled to form crystals. Consequently, the assembly of clusters with different orientations tends to break the inversion symmetry. By exploiting this concept, we show that sliding ferroelectricity naturally emerges in trilayer quasi-hexagonal phase (qHP) C60, a cluster-assembled carbon allotrope recently synthesized. Compared to many metallic or semi-metallic elemental ferroelectrics, trilayer qHP C60’s have sizable band gaps and several ferroelectric structures, which are distinguishable by measuring their second-harmonic generation (SHG) responses. Some of these phases show both switchable out-of-plane and in-plane polarizations on the order of 0.2 pC/m. The out-of-plane and in-plane polarizations can be switched independently and enable an easy-to-implement construction of Van der Waals homostructures with ferroelectrically switchable chirality.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"11 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142935526","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

Quantum annealing-assisted lattice optimization 量子退火辅助晶格优化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-07 DOI: 10.1038/s41524-024-01505-1

Zhihao Xu, Wenjie Shang, Seongmin Kim, Eungkyu Lee, Tengfei Luo

引用次数: 0

Unlikelihood of a phonon mechanism for the high-temperature superconductivity in La3Ni2O7 La3Ni2O7高温超导的声子机制的不可能性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-06 DOI: 10.1038/s41524-024-01483-4

Jing-Yang You, Zien Zhu, Mauro Del Ben, Wei Chen, Zhenglu Li

{"title":"Unlikelihood of a phonon mechanism for the high-temperature superconductivity in La3Ni2O7","authors":"Jing-Yang You, Zien Zhu, Mauro Del Ben, Wei Chen, Zhenglu Li","doi":"10.1038/s41524-024-01483-4","DOIUrl":"https://doi.org/10.1038/s41524-024-01483-4","url":null,"abstract":"The discovery of ~80 K superconductivity in nickelate La3Ni2O7 under pressure has ignited intense interest. Here, we present a comprehensive first-principles study of the electron-phonon (e-ph) coupling in La3Ni2O7 and its implications on the observed superconductivity. Our results conclude that the e-ph coupling is too weak (with a coupling constant λ ≲ 0.5) to account for the high Tc, albeit interesting many-electron correlation effects exist. While Coulomb interactions (via GW self-energy and Hubbard U) enhance the e-ph coupling strength, electron doping (oxygen vacancies) introduces no major changes. Additionally, different structural phases display varying characteristics near the Fermi level, but do not alter the conclusion. The e-ph coupling landscape of La3Ni2O7 is intrinsically different from that of infinite-layer nickelates. These findings suggest that a phonon-mediated mechanism is unlikely to be responsible for the observed superconductivity in La3Ni2O7, pointing instead to an unconventional nature.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"14 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929675","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

Artificial intelligence-driven phase stability evaluation and new dopants identification of hafnium oxide-based ferroelectric materials 人工智能驱动的氧化铪基铁电材料相稳定性评价及新掺杂剂鉴定

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-05 DOI: 10.1038/s41524-024-01510-4

Shaoan Yan, Pei Xu, Gang Li, Yuchun Li, Yingfang Zhu, Xiaona Zhu, Qiong Yang, Meng Li, Minghua Tang, Hongliang Lu, Sen Liu, Qingjiang Li, David Wei Zhang, Zhigang Chen

{"title":"Artificial intelligence-driven phase stability evaluation and new dopants identification of hafnium oxide-based ferroelectric materials","authors":"Shaoan Yan, Pei Xu, Gang Li, Yuchun Li, Yingfang Zhu, Xiaona Zhu, Qiong Yang, Meng Li, Minghua Tang, Hongliang Lu, Sen Liu, Qingjiang Li, David Wei Zhang, Zhigang Chen","doi":"10.1038/s41524-024-01510-4","DOIUrl":"https://doi.org/10.1038/s41524-024-01510-4","url":null,"abstract":"In this work, a multi-stage material design framework combining machine learning techniques with density functional theory is established to reveal the mechanism of phase stabilization in HfO2 based ferroelectric materials. The ferroelectric phase fractions based on a more stringent relationship of phase energy differences is proposed as an evaluation criterion for the ferroelectric performance of hafnium-based materials. Based on the Boltzmann distribution theory, the abstract phase energy difference is converted into an intuitive phase fraction distribution mapping. A large-scale prediction of unknown dopants is conducted within the material design framework, and gallium (Ga) is identified as a new dopant for HfO2. Both experiments and density functional theory calculations demonstrate that Ga is an excellent dopant for ferroelectric hafnium oxide, especially, the experimentally determined variation trends of ferroelectric phase fraction and polarization properties with Ga doping concentration are in good agreement with the predictions given by machine learning. This work provides a new perspective from machine learning to deepen the understanding of the ferroelectric properties of HfO2 materials, offering fresh insights into the design and performance prediction of HfO2 ferroelectric thin films.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"2 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925225","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

Cross-scale covariance for material property prediction 材料性能预测的跨尺度协方差

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2025-01-04 DOI: 10.1038/s41524-024-01453-w

Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta, Fei Zhou, Ellad B. Tadmor, Vincenzo Lordi, Vasily V. Bulatov

{"title":"Cross-scale covariance for material property prediction","authors":"Benjamin A. Jasperson, Ilia Nikiforov, Amit Samanta, Fei Zhou, Ellad B. Tadmor, Vincenzo Lordi, Vasily V. Bulatov","doi":"10.1038/s41524-024-01453-w","DOIUrl":"https://doi.org/10.1038/s41524-024-01453-w","url":null,"abstract":"A simulation can stand its ground against an experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale (~108 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤102 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale “strength-on-predictors” regression model. This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"15 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142925157","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

Optimal pre-train/fine-tune strategies for accurate material property predictions 最优预训练/微调策略，准确预测材料性能

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-20 DOI: 10.1038/s41524-024-01486-1

Reshma Devi, Keith T. Butler, Gopalakrishnan Sai Gautam

引用次数: 0

Shotgun crystal structure prediction using machine-learned formation energies 利用机器学习的地层能量预测散弹枪晶体结构

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-20 DOI: 10.1038/s41524-024-01471-8

Liu Chang, Hiromasa Tamaki, Tomoyasu Yokoyama, Kensuke Wakasugi, Satoshi Yotsuhashi, Minoru Kusaba, Artem R. Oganov, Ryo Yoshida

{"title":"Shotgun crystal structure prediction using machine-learned formation energies","authors":"Liu Chang, Hiromasa Tamaki, Tomoyasu Yokoyama, Kensuke Wakasugi, Satoshi Yotsuhashi, Minoru Kusaba, Artem R. Oganov, Ryo Yoshida","doi":"10.1038/s41524-024-01471-8","DOIUrl":"https://doi.org/10.1038/s41524-024-01471-8","url":null,"abstract":"Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy calculations, which is often impractical for large crystalline systems. Here, we present significant progress toward solving the crystal structure prediction problem: we performed noniterative, single-shot screening using a large library of virtually created crystal structures with a machine-learning energy predictor. This shotgun method (ShotgunCSP) has two key technical components: transfer learning for accurate energy prediction of pre-relaxed crystalline states, and two generative models based on element substitution and symmetry-restricted structure generation to produce promising and diverse crystal structures. First-principles calculations were performed only to generate the training samples and to refine a few selected pre-relaxed crystal structures. The ShotunCSP method is less computationally intensive than conventional methods and exhibits exceptional prediction accuracy, reaching 93.3% in benchmark tests with 90 different crystal structures.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"264 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858540","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

Predicting electronic screening for fast Koopmans spectral functional calculations 预测快速库普曼谱函数计算的电子筛选

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-20 DOI: 10.1038/s41524-024-01484-3

Yannick Schubert, Sandra Luber, Nicola Marzari, Edward Linscott

{"title":"Predicting electronic screening for fast Koopmans spectral functional calculations","authors":"Yannick Schubert, Sandra Luber, Nicola Marzari, Edward Linscott","doi":"10.1038/s41524-024-01484-3","DOIUrl":"https://doi.org/10.1038/s41524-024-01484-3","url":null,"abstract":"Koopmans spectral functionals are a powerful extension of Kohn-Sham density-functional theory (DFT) that enables the prediction of spectral properties with state-of-the-art accuracy. The success of these functionals relies on capturing the effects of electronic screening through scalar, orbital-dependent parameters. These parameters have to be computed for every calculation, making Koopmans spectral functionals more expensive than their DFT counterparts. In this work, we present a machine-learning model that—with minimal training—can predict these screening parameters directly from orbital densities calculated at the DFT level. We show in two prototypical use cases that using the screening parameters predicted by this model, instead of those calculated from linear response, leads to orbital energies that differ by less than 20 meV on average. Since this approach dramatically reduces run times with minimal loss of accuracy, it will enable the application of Koopmans spectral functionals to classes of problems that previously would have been prohibitively expensive, such as the prediction of temperature-dependent spectral properties. More broadly, this work demonstrates that measuring violations of piecewise linearity (i.e., curvature in total energies with respect to occupancies) can be done efficiently by combining frozen-orbital approximations and machine learning.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"10 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858549","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