npj Computational Materials最新文献_第3页

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 主动空间嵌入方法的一般框架及其在量子计算中的应用

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

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

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 基于gpt和基于扩散的生成模型的聚合物电解质从头设计

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 硅相容CeO2/HfO2人工超晶格中杂化反常铁电性

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 DPA-2：作为多任务学习器的大型原子模型

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

Exploring the role of nonlocal Coulomb interactions in perovskite transition metal oxides 探索过氧化物过渡金属氧化物中非局部库仑相互作用的作用

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-19 DOI: 10.1038/s41524-024-01454-9

Indukuru Ramesh Reddy, Chang-Jong Kang, Sooran Kim, Bongjae Kim

{"title":"Exploring the role of nonlocal Coulomb interactions in perovskite transition metal oxides","authors":"Indukuru Ramesh Reddy, Chang-Jong Kang, Sooran Kim, Bongjae Kim","doi":"10.1038/s41524-024-01454-9","DOIUrl":"https://doi.org/10.1038/s41524-024-01454-9","url":null,"abstract":"Employing the density functional theory incorporating on-site and inter-site Coulomb interactions (DFT + U + V), we have investigated the role of the nonlocal interactions on the electronic structures of the transition metal oxide perovskites. Using constrained random phase approximation calculations, we derived screened Coulomb interaction parameters and revealed a competition between localization and screening effects, which results in nonmonotonic behavior with d-orbital occupation. We highlight the significant role and nonlocality of inter-site Coulomb interactions, V, comparable in magnitude to the local interaction, U. Our DFT + U + V results exemplarily show the representative band renormalization, and deviations from ideal extended Hubbard models due to increased hybridization between transition metal d and oxygen p orbitals as occupation increases. We further demonstrate that the inclusion of the inter-site V is essential for accurately reproducing the experimental magnetic order in transition metal oxides.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"31 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849316","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

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity 从头算非晶体结构数据库：增强机器学习解码扩散性的能力

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-19 DOI: 10.1038/s41524-024-01469-2

Hui Zheng, Eric Sivonxay, Rasmus Christensen, Max Gallant, Ziyao Luo, Matthew McDermott, Patrick Huck, Morten M. Smedskjær, Kristin A. Persson

{"title":"The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity","authors":"Hui Zheng, Eric Sivonxay, Rasmus Christensen, Max Gallant, Ziyao Luo, Matthew McDermott, Patrick Huck, Morten M. Smedskjær, Kristin A. Persson","doi":"10.1038/s41524-024-01469-2","DOIUrl":"https://doi.org/10.1038/s41524-024-01469-2","url":null,"abstract":"Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"70 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858553","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

Data efficient machine learning potentials for modeling catalytic reactivity via active learning and enhanced sampling 通过主动学习和增强采样，为催化反应建模提供数据高效的机器学习潜力

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-12-19 DOI: 10.1038/s41524-024-01481-6

Simone Perego, Luigi Bonati

{"title":"Data efficient machine learning potentials for modeling catalytic reactivity via active learning and enhanced sampling","authors":"Simone Perego, Luigi Bonati","doi":"10.1038/s41524-024-01481-6","DOIUrl":"https://doi.org/10.1038/s41524-024-01481-6","url":null,"abstract":"Simulating catalytic reactivity under operative conditions poses a significant challenge due to the dynamic nature of the catalysts and the high computational cost of electronic structure calculations. Machine learning potentials offer a promising avenue to simulate dynamics at a fraction of the cost, but they require datasets containing all relevant configurations, particularly reactive ones. Here, we present a scheme to construct reactive potentials in a data-efficient manner. This is achieved by combining enhanced sampling methods first with Gaussian processes to discover transition paths and then with graph neural networks to obtain a uniformly accurate description. The necessary configurations are extracted via a Data-Efficient Active Learning (DEAL) procedure based on local environment uncertainty. We validated our approach by studying several reactions related to the decomposition of ammonia on iron-cobalt alloy catalysts. Our scheme proved to be efficient, requiring only ~1000 DFT calculations per reaction, and robust, sampling reactive configurations from the different accessible pathways. Using this potential, we calculated free energy profiles and characterized reaction mechanisms, showing the ability to provide microscopic insights into complex processes under dynamic conditions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"23 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849289","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