npj Computational Materials最新文献

{"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":"<p>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.</p>","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}

{"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":"<p>Employing the density functional theory incorporating on-site and inter-site Coulomb interactions (DFT + <i>U</i> + <i>V</i>), 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 <i>d</i>-orbital occupation. We highlight the significant role and nonlocality of inter-site Coulomb interactions, <i>V</i>, comparable in magnitude to the local interaction, <i>U</i>. Our DFT + <i>U</i> + <i>V</i> results exemplarily show the representative band renormalization, and deviations from ideal extended Hubbard models due to increased hybridization between transition metal <i>d</i> and oxygen <i>p</i> orbitals as occupation increases. We further demonstrate that the inclusion of the inter-site <i>V</i> is essential for accurately reproducing the experimental magnetic order in transition metal oxides.</p>","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}

{"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":"<p>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 <span>(P{2}_{1})</span> 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 (<span>(P=30.3,mu C/c{m}^{2})</span>) 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.</p>","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}

{"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":"<p>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.</p>","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}

{"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":"<p>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.</p>","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}

{"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":"<p>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.</p>","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}

{"title":"Quantitative kinetic rules for plastic strain-induced α - ω phase transformation in Zr under high pressure","authors":"Achyut Dhar, Valery I. Levitas, K. K. Pandey, Changyong Park, Maddury Somayazulu, Nenad Velisavljevic","doi":"10.1038/s41524-024-01491-4","DOIUrl":"https://doi.org/10.1038/s41524-024-01491-4","url":null,"abstract":"<p>Plastic strain-induced phase transformations (PTs) and chemical reactions under high pressure are broadly spread in modern technologies, friction and wear, geophysics, and astrogeology. However, because of very heterogeneous fields of plastic strain <span>({{boldsymbol{E}}}^{p})</span> and stress <b><i>σ</i></b> tensors and volume fraction <i>c</i> of phases in a sample compressed in a diamond anvil cell (DAC) and impossibility of measurements of <b><i>σ</i></b> and <span>({{boldsymbol{E}}}^{p})</span>, there are no strict kinetic equations for them. Here, we develop a kinetic model, finite element method (FEM) approach, and combined FEM-experimental approaches to determine all fields in strongly plastically predeformed Zr compressed in DAC, and specific kinetic equation for α-ω PT consistent with experimental data for the entire sample. Since all fields in the sample are very heterogeneous, data are obtained for numerous complex 7D paths in the space of 3 components of the plastic strain tensor and 4 components of the stress tensor. Kinetic equation depends on accumulated plastic strain (instead of time) and pressure and is independent of plastic strain and deviatoric stress tensors, i.e., it can be applied for various above processes. Our results initiate kinetic studies of strain-induced PTs and provide efforts toward more comprehensive understanding of material behavior in extreme conditions.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"260 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849312","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}

{"title":"DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules","authors":"Hongwei Du, Jiamin Wang, Jian Hui, Lanting Zhang, Hong Wang","doi":"10.1038/s41524-024-01444-x","DOIUrl":"https://doi.org/10.1038/s41524-024-01444-x","url":null,"abstract":"<p>Modern generative models based on deep learning have made it possible to design millions of hypothetical materials. To screen these candidate materials and identify promising new materials, we need fast and accurate models to predict material properties. Graphical neural networks (GNNs) have become a current research focus due to their ability to directly act on the graphical representation of molecules and materials, enabling comprehensive capture of important information and showing excellent performance in predicting material properties. Nevertheless, GNNs still face several key problems in practical applications: First, although existing nested graph network strategies increase critical structural information such as bond angles, they significantly increase the number of trainable parameters in the model, resulting in a increase in training costs; Second, extending GNN models to broader domains such as molecules, crystalline materials, and catalysis, as well as adapting to small data sets, remains a challenge. Finally, the scalability of GNN models is limited by the over-smoothing problem. To address these issues, we propose the DenseGNN model, which combines Dense Connectivity Network (DCN), hierarchical node-edge-graph residual networks (HRN), and Local Structure Order Parameters Embedding (LOPE) strategies to create a universal, scalable, and efficient GNN model. We have achieved state-of-the-art performance (SOAT) on several datasets, including JARVIS-DFT, Materials Project, QM9, Lipop, FreeSolv, ESOL, and OC22, demonstrating the generality and scalability of our approach. By merging DCN and LOPE strategies into GNN models in computing, crystal materials, and molecules, we have improved the performance of models such as GIN, Schnet, and Hamnet on materials datasets such as Matbench. The LOPE strategy optimizes the embedding representation of atoms and allows our model to train efficiently with a minimal level of edge connections. This substantially reduces computational costs and shortens the time required to train large GNNs while maintaining accuracy. Our technique not only supports building deeper GNNs and avoids performance penalties experienced by other models, but is also applicable to a variety of applications that require large deep learning models. Furthermore, our study demonstrates that by using structural embeddings from pre-trained models, our model not only outperforms other GNNs in distinguishing crystal structures but also approaches the standard X-ray diffraction (XRD) method.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"27 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849313","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}

{"title":"Platinum-based catalysts for oxygen reduction reaction simulated with a quantum computer","authors":"Cono Di Paola, Evgeny Plekhanov, Michal Krompiec, Chandan Kumar, Emanuele Marsili, Fengmin Du, Daniel Weber, Jasper Simon Krauser, Elvira Shishenina, David Muñoz Ramo","doi":"10.1038/s41524-024-01460-x","DOIUrl":"https://doi.org/10.1038/s41524-024-01460-x","url":null,"abstract":"<p>Hydrogen has emerged as a promising energy source for low-carbon and sustainable mobility purposes. However, its applications are still limited by modest conversion efficiency in the electrocatalytic oxygen reduction reaction (ORR) within fuel cells. The complex nature of the ORR and the presence of strong electronic correlations present challenges to atomistic modelling using classical computers. This scenario opens new avenues for the implementation of novel quantum computing workflows. Here, we present a state-of-the-art study that combines classical and quantum computational approaches to investigate ORR on platinum-based surfaces. Our research demonstrates, for the first time, the feasibility of implementing this workflow on the H1-series trapped-ion quantum computer and identify the challenges of the quantum chemistry modelling of this reaction. The results highlight the great potentiality of quantum computers in solving notoriously difficult systems with strongly correlated electronic structures and suggest platinum/cobalt as ideal candidate for showcasing quantum advantage in future applications.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"64 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849311","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}

{"title":"Excitations in layered materials from a non-empirical Wannier-localized optimally- tuned screened range-separated hybrid functional","authors":"María Camarasa-Gómez, Stephen E. Gant, Guy Ohad, Jeffrey B. Neaton, Ashwin Ramasubramaniam, Leeor Kronik","doi":"10.1038/s41524-024-01478-1","DOIUrl":"https://doi.org/10.1038/s41524-024-01478-1","url":null,"abstract":"<p>Accurate prediction of electronic and optical excitations in van der Waals (vdW) materials is a long-standing challenge for density functional theory. The recent Wannier-localized optimally-tuned screened range-separated hybrid (WOT-SRSH) functional has proven successful in non-empirical determination of electronic band gaps and optical absorption spectra for covalent and ionic crystals. However, for vdW materials the tuning of the material- and structure-dependent functional parameters has only been attained semi-empirically. Here, we present a non-empirical WOT-SRSH approach applicable to vdW materials, with the optimal functional parameters transferable between monolayer and bulk. We apply this methodology to prototypical vdW materials: black phosphorus, molybdenum disulfide, and hexagonal boron nitride (in the latter case including zero-point renormalization). We show that the WOT-SRSH approach consistently achieves accuracy levels comparable to experiments and many-body perturbation theory (MBPT) calculations for band structures and optical absorption spectra, both on its own and as an optimal starting point for MBPT calculations.</p>","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"79 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142849315","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}