npj Computational Materials最新文献

Optimal pre-train/fine-tune strategies for accurate material property predictions

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

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

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

Prediction of p-block-based ternary superconductors XC2H8 at low pressure 预测低压下基于 p 块的三元超导体 XC2H8

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-19 DOI: 10.1038/s41524-024-01490-5

Izabela A. Wrona, Paweł Niegodajew, Yinwei Li, Artur P. Durajski

{"title":"Prediction of p-block-based ternary superconductors XC2H8 at low pressure","authors":"Izabela A. Wrona, Paweł Niegodajew, Yinwei Li, Artur P. Durajski","doi":"10.1038/s41524-024-01490-5","DOIUrl":"https://doi.org/10.1038/s41524-024-01490-5","url":null,"abstract":"Achieving room-temperature superconductivity under ambient conditions is one of the most important goals in solid-state physics and material science. Recent discoveries of high-Tc superconductivity in binary hydrides H3S and LaH10 at high pressures have focused the search for room-temperature superconductors on dense hydrides with conventional phonon-mediated pairing mechanisms. In this study, we predict a novel family of superconducting ternary hydrides under moderate compression, XC2H8 (X = Ga, In, Tl, Sn, Pb, Sb, Bi, Te, Po). Unlike H3S and LaH10, these new materials are stable at just around 20 GPa. Among the analyzed compounds, SbC2H8 exhibits the highest critical temperature of 73 K at a pressure of 100 GPa, which is attributed to its energetically favorable high-symmetry crystal structure ((Fm{bar{3}}m)), high density of states at the Fermi level (1.27 states/eV) and strong electron–phonon coupling constant (1.02). We expect that our findings provide crucial insights into achieving high-temperature superconductivity at moderate pressures and accelerate the progress of experimental research.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"8 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849317","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

Chemical ordering and magnetism in face-centered cubic CrCoNi alloy 面心立方铬钴镍合金的化学有序性和磁性

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-19 DOI: 10.1038/s41524-024-01439-8

Sheuly Ghosh, Katharina Ueltzen, Janine George, Jörg Neugebauer, Fritz Körmann

引用次数: 0

A general framework for active space embedding methods with applications in quantum computing

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-19 DOI: 10.1038/s41524-024-01477-2

Stefano Battaglia, Max Rossmannek, Vladimir V. Rybkin, Ivano Tavernelli, Jürg Hutter

引用次数: 0

Deep reinforcement learning for inverse inorganic materials design 无机材料逆向设计的深度强化学习

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-19 DOI: 10.1038/s41524-024-01474-5

Christopher Karpovich, Elton Pan, Elsa A. Olivetti

{"title":"Deep reinforcement learning for inverse inorganic materials design","authors":"Christopher Karpovich, Elton Pan, Elsa A. Olivetti","doi":"10.1038/s41524-024-01474-5","DOIUrl":"https://doi.org/10.1038/s41524-024-01474-5","url":null,"abstract":"A major obstacle to the realization of novel inorganic materials with desirable properties is efficient materials discovery over both the materials property and synthesis spaces. In this work, we propose and compare two novel reinforcement learning (RL) approaches to inverse inorganic oxide materials design to target promising compounds using specified property and synthesis objectives. Our models successfully learn chemical guidelines such as negative formation energy, charge neutrality, and electronegativity balance while maintaining high chemical diversity and uniqueness. We demonstrate multi-objective RL algorithms that can generate novel compounds with both desirable materials properties (band gap, formation energy, bulk modulus, shear modulus) and synthesis objectives (low sintering and calcination temperatures). We apply template-based crystal structure prediction to suggest feasible crystal structure matches for target inorganic compositions identified by our machine learning (ML) algorithms to highlight the plausibility of the identified target compositions. We analyze the benefits and drawbacks of the ML approaches tested in this work in the context of accelerated inorganic materials design. This work isolates and evaluates the effects of different RL methodologies to suggest promising, valid compounds of interest by exploring the chemical design space for materials discovery.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"42 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849318","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

De novo design of polymer electrolytes using GPT-based and diffusion-based generative models

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-19 DOI: 10.1038/s41524-024-01470-9

Zhenze Yang, Weike Ye, Xiangyun Lei, Daniel Schweigert, Ha-Kyung Kwon, Arash Khajeh

{"title":"De novo design of polymer electrolytes using GPT-based and diffusion-based generative models","authors":"Zhenze Yang, Weike Ye, Xiangyun Lei, Daniel Schweigert, Ha-Kyung Kwon, Arash Khajeh","doi":"10.1038/s41524-024-01470-9","DOIUrl":"https://doi.org/10.1038/s41524-024-01470-9","url":null,"abstract":"Solid polymer electrolytes offer promising advancements for next-generation batteries, boasting superior safety, enhanced specific energy, and extended lifespans over liquid electrolytes. However, low ionic conductivity and the vast polymer space hinder commercialization. This study leverages generative AI for de novo polymer electrolyte design, comparing GPT-based and diffusion-based models with extensive hyperparameter tuning. We evaluate these models using various metrics and full-atom molecular dynamics simulations. Among 46 candidates tested, 17 exhibit superior ionic conductivity, surpassing existing polymers in our database, with some doubling the conductivity values. Additionally, by adopting pretraining and fine-tuning methodologies, we significantly enhance our generative models, achieving quicker convergence, better performance with limited data, and greater diversity. Our method efficiently generates a large number of novel, diverse, and valid polymers, with a high likelihood of synthesizability, enabling the identification of promising candidates with markedly improved efficiency and effectiveness for practical applications.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"56 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858552","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

Hybrid improper ferroelectricity in a Si-compatible CeO2/HfO2 artificial superlattice

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-19 DOI: 10.1038/s41524-024-01487-0

Pawan Kumar, Jun Hee Lee

{"title":"Hybrid improper ferroelectricity in a Si-compatible CeO2/HfO2 artificial superlattice","authors":"Pawan Kumar, Jun Hee Lee","doi":"10.1038/s41524-024-01487-0","DOIUrl":"https://doi.org/10.1038/s41524-024-01487-0","url":null,"abstract":"Hybrid improper ferroelectrics (HIFs), characterized by ferroelectric polarization arising from the rotation of two symmetry inequivalent antiferrodistortive modes, exhibit exotic properties such as T-independent dielectric constants and robustness against depolarizing field. Here, using first-principles simulations, we report a new (P{2}_{1}) phase in a Si-compatible CeO2/HfO2 superlattice that exhibits remarkably robust hybrid improper ferroelectricity, induced by the in-plane oxygen rotations of two antiferrodistortive distortion modes. These non-polar distortions are coupled with a polar distortion through a trilinear coupling in the superlattice, stabilizing ferroelectricity as the competing ground state with the assistance of epitaxial strain. The estimated out-of-plane polarization ((P=30.3,mu C/c{m}^{2})) is switchable with a remarkably small energy barrier of 8.5 meV/atom and relatively smaller coercive field relative to bulk HfO2, expected to reduce the operational voltage of ferroelectric devices. Our discovery may offer unexpected opportunities for innovating high-performance, low-voltage devices, and promising advancements in next-generation CMOS compatible oxide-based electronics.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"20 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858551","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

DPA-2: a large atomic model as a multi-task learner

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-19 DOI: 10.1038/s41524-024-01493-2

Duo Zhang, Xinzijian Liu, Xiangyu Zhang, Chengqian Zhang, Chun Cai, Hangrui Bi, Yiming Du, Xuejian Qin, Anyang Peng, Jiameng Huang, Bowen Li, Yifan Shan, Jinzhe Zeng, Yuzhi Zhang, Siyuan Liu, Yifan Li, Junhan Chang, Xinyan Wang, Shuo Zhou, Jianchuan Liu, Xiaoshan Luo, Zhenyu Wang, Wanrun Jiang, Jing Wu, Yudi Yang, Jiyuan Yang, Manyi Yang, Fu-Qiang Gong, Linshuang Zhang, Mengchao Shi, Fu-Zhi Dai, Darrin M. York, Shi Liu, Tong Zhu, Zhicheng Zhong, Jian Lv, Jun Cheng, Weile Jia, Mohan Chen, Guolin Ke, Weinan E, Linfeng Zhang, Han Wang

{"title":"DPA-2: a large atomic model as a multi-task learner","authors":"Duo Zhang, Xinzijian Liu, Xiangyu Zhang, Chengqian Zhang, Chun Cai, Hangrui Bi, Yiming Du, Xuejian Qin, Anyang Peng, Jiameng Huang, Bowen Li, Yifan Shan, Jinzhe Zeng, Yuzhi Zhang, Siyuan Liu, Yifan Li, Junhan Chang, Xinyan Wang, Shuo Zhou, Jianchuan Liu, Xiaoshan Luo, Zhenyu Wang, Wanrun Jiang, Jing Wu, Yudi Yang, Jiyuan Yang, Manyi Yang, Fu-Qiang Gong, Linshuang Zhang, Mengchao Shi, Fu-Zhi Dai, Darrin M. York, Shi Liu, Tong Zhu, Zhicheng Zhong, Jian Lv, Jun Cheng, Weile Jia, Mohan Chen, Guolin Ke, Weinan E, Linfeng Zhang, Han Wang","doi":"10.1038/s41524-024-01493-2","DOIUrl":"https://doi.org/10.1038/s41524-024-01493-2","url":null,"abstract":"The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"91 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858554","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