npj Computational Materials最新文献_第2页

Deep learning generative model for crystal structure prediction 晶体结构预测的深度学习生成模型

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-11-12 DOI: 10.1038/s41524-024-01443-y

Xiaoshan Luo, Zhenyu Wang, Pengyue Gao, Jian Lv, Yanchao Wang, Changfeng Chen, Yanming Ma

{"title":"Deep learning generative model for crystal structure prediction","authors":"Xiaoshan Luo, Zhenyu Wang, Pengyue Gao, Jian Lv, Yanchao Wang, Changfeng Chen, Yanming Ma","doi":"10.1038/s41524-024-01443-y","DOIUrl":"https://doi.org/10.1038/s41524-024-01443-y","url":null,"abstract":"Recent advances in deep learning generative models (GMs) have created high capabilities in accessing and assessing complex high-dimensional data, allowing superior efficiency in navigating vast material configuration space in search of viable structures. Coupling such capabilities with physically significant data to construct trained models for materials discovery is crucial to moving this emerging field forward. Here, we present a universal GM for crystal structure prediction (CSP) via a conditional crystal diffusion variational autoencoder (Cond-CDVAE) approach, which is tailored to allow user-defined material and physical parameters such as composition and pressure. This model is trained on an expansive dataset containing over 670,000 local minimum structures, including a rich spectrum of high-pressure structures, along with ambient-pressure structures in Materials Project database. We demonstrate that the Cond-CDVAE model can generate physically plausible structures with high fidelity under diverse pressure conditions without necessitating local optimization, accurately predicting 59.3% of the 3547 unseen ambient-pressure experimental structures within 800 structure samplings, with the accuracy rate climbing to 83.2% for structures comprising fewer than 20 atoms per unit cell. These results meet or exceed those achieved via conventional CSP methods based on global optimization. The present findings showcase substantial potential of GMs in the realm of CSP.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"18 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142599376","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

High-speed and low-power molecular dynamics processing unit (MDPU) with ab initio accuracy 高速、低功耗分子动力学处理单元 (MDPU)，具有原子序数精度

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-11-07 DOI: 10.1038/s41524-024-01422-3

Pinghui Mo, Yujia Zhang, Zhuoying Zhao, Hanhan Sun, Junhua Li, Dawei Guan, Xi Ding, Xin Zhang, Bo Chen, Mengchao Shi, Duo Zhang, Denghui Lu, Yinan Wang, Jianxing Huang, Fei Liu, Xinyu Li, Mohan Chen, Jun Cheng, Bin Liang, Weinan E, Jiayu Dai, Linfeng Zhang, Han Wang, Jie Liu

{"title":"High-speed and low-power molecular dynamics processing unit (MDPU) with ab initio accuracy","authors":"Pinghui Mo, Yujia Zhang, Zhuoying Zhao, Hanhan Sun, Junhua Li, Dawei Guan, Xi Ding, Xin Zhang, Bo Chen, Mengchao Shi, Duo Zhang, Denghui Lu, Yinan Wang, Jianxing Huang, Fei Liu, Xinyu Li, Mohan Chen, Jun Cheng, Bin Liang, Weinan E, Jiayu Dai, Linfeng Zhang, Han Wang, Jie Liu","doi":"10.1038/s41524-024-01422-3","DOIUrl":"https://doi.org/10.1038/s41524-024-01422-3","url":null,"abstract":"Molecular dynamics (MD) is an indispensable atomistic-scale computational tool widely-used in various disciplines. In the past decades, nearly all ab initio MD and machine-learning MD have been based on the general-purpose central/graphics processing units (CPU/GPU), which are well-known to suffer from their intrinsic “memory wall” and “power wall” bottlenecks. Consequently, nowadays MD calculations with ab initio accuracy are extremely time-consuming and power-consuming, imposing serious restrictions on the MD simulation size and duration. To solve this problem, here we propose a special-purpose MD processing unit (MDPU), which could reduce MD time and power consumption by about 103 times (109 times) compared to state-of-the-art machine-learning MD (ab initio MD) based on CPU/GPU, while keeping ab initio accuracy. With significantly-enhanced performance, the proposed MDPU may pave a way for the accurate atomistic-scale analysis of large-size and/or long-duration problems which were impossible/impractical to compute before.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"5 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142596520","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

An automated computational framework to construct printability maps for additively manufactured metal alloys 构建增材制造金属合金可印刷性图的自动化计算框架

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-11-06 DOI: 10.1038/s41524-024-01436-x

Sofia Sheikh, Brent Vela, Pejman Honarmandi, Peter Morcos, David Shoukr, Ibrahim Karaman, Alaa Elwany, Raymundo Arróyave

{"title":"An automated computational framework to construct printability maps for additively manufactured metal alloys","authors":"Sofia Sheikh, Brent Vela, Pejman Honarmandi, Peter Morcos, David Shoukr, Ibrahim Karaman, Alaa Elwany, Raymundo Arróyave","doi":"10.1038/s41524-024-01436-x","DOIUrl":"https://doi.org/10.1038/s41524-024-01436-x","url":null,"abstract":"In metal additive manufacturing (AM), processing parameters can affect the probability of macroscopic defect formation (lack-of-fusion, keyholing, balling), which can, in turn, jeopardize the final product’s integrity. A printability map classifies regions in the processing space where an alloy can be printed with or without porosity defects. However, the creation of these printability maps is resource-intensive. Previous efforts to generate printability maps have required single-track experiments on pre-alloyed powder, limiting the utilization of these printability maps for the high-throughput design of printable alloys. We address these challenges in the case of Laser Powder Bed Fusion AM (L-PBF-AM) by introducing a fully computational, predictive approach to create printability maps for arbitrary alloys. Our framework uses physics-based thermal models and a variety of defect formation criteria. We benchmark the predictive ability of the proposed framework against literature data for the following commonly printed alloys: 316 Stainless Steel, Inconel 718, Ti-6Al-4V, AF96, and Ni-5Nb. Furthermore, we deploy the framework on NiTi-based Shape Memory Alloys (SMAs) as a case study. We scrutinize the accuracy of various sets of defect criteria and use these accuracy measurements to create an uncertainty-aware probabilistic framework capable of predicting the printability maps of arbitrary alloys. This framework has the potential to guide alloy designers to potentially easy-to-print alloys, enabling the co-design of high-performing printable alloys.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"46 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142588781","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

Opportunities for retrieval and tool augmented large language models in scientific facilities 科学设施中的检索和工具增强大型语言模型的机遇

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-11-05 DOI: 10.1038/s41524-024-01423-2

Michael H. Prince, Henry Chan, Aikaterini Vriza, Tao Zhou, Varuni K. Sastry, Yanqi Luo, Matthew T. Dearing, Ross J. Harder, Rama K. Vasudevan, Mathew J. Cherukara

{"title":"Opportunities for retrieval and tool augmented large language models in scientific facilities","authors":"Michael H. Prince, Henry Chan, Aikaterini Vriza, Tao Zhou, Varuni K. Sastry, Yanqi Luo, Matthew T. Dearing, Ross J. Harder, Rama K. Vasudevan, Mathew J. Cherukara","doi":"10.1038/s41524-024-01423-2","DOIUrl":"https://doi.org/10.1038/s41524-024-01423-2","url":null,"abstract":"Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"139 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580299","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

Multiscale modeling of metal-hydride interphases—quantification of decoupled chemo-mechanical energies 金属氢化物相间的多尺度建模--解耦化学机械能的量化

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-10-24 DOI: 10.1038/s41524-024-01424-1

Ebert Alvares, Kai Sellschopp, Bo Wang, ShinYoung Kang, Thomas Klassen, Brandon C. Wood, Tae Wook Heo, Paul Jerabek, Claudio Pistidda

{"title":"Multiscale modeling of metal-hydride interphases—quantification of decoupled chemo-mechanical energies","authors":"Ebert Alvares, Kai Sellschopp, Bo Wang, ShinYoung Kang, Thomas Klassen, Brandon C. Wood, Tae Wook Heo, Paul Jerabek, Claudio Pistidda","doi":"10.1038/s41524-024-01424-1","DOIUrl":"https://doi.org/10.1038/s41524-024-01424-1","url":null,"abstract":"The quantification of interphase properties between metals and their corresponding hydrides is crucial for modeling the thermodynamics and kinetics of the hydrogenation processes in solid-state hydrogen storage materials. In particular, interphase boundary energies assume a pivotal role in determining the kinetics of nucleation, growth, and coarsening of hydrides, alongside accompanying morphological evolution during hydrogenation. The total interphase energy arises from both chemical bonding and mechanical strains in these solid-state systems. Since these contributions are usually coupled, it is challenging to distinguish via conventional computational approaches. Here, a comprehensive atomistic modeling methodology is developed to decouple chemical and mechanical energy contributions using first-principles calculations, of which feasibility is demonstrated by quantifying chemical and elastic strain energies of key interfaces within the FeTi metal-hydride system. Derived materials parameters are then employed for mesoscopic micromechanical analysis, predicting crystallographic orientations in line with experimental observations. The multiscale approach outlined verifies the importance of the chemo-mechanical interplay in the morphological evolution of growing hydride phases, and can be generalized to investigate other systems. In addition, it can streamline the design of atomistic models for the quantitative evaluation of interphase properties between dissimilar phases and allow for efficient predictions of their preferred phase boundary orientations.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"212 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142489692","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

Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints 通过网格投影原子指纹的卷积网络学习自洽电子密度

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-10-24 DOI: 10.1038/s41524-024-01433-0

Ryong-Gyu Lee, Yong-Hoon Kim

{"title":"Convolutional network learning of self-consistent electron density via grid-projected atomic fingerprints","authors":"Ryong-Gyu Lee, Yong-Hoon Kim","doi":"10.1038/s41524-024-01433-0","DOIUrl":"https://doi.org/10.1038/s41524-024-01433-0","url":null,"abstract":"The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution (ρ) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can shorten or bypass the SCF loop represents a critical question in electronic structure theory from both practical and fundamental standpoints. Herein, a machine learning strategy, DeepSCF, is presented in which the map between the SCF ρ and the initial guess density (ρ0) constructed by the summation of neutral atomic densities is learned using 3D convolutional neural networks (CNNs). High accuracy and transferability of DeepSCF are achieved by first encoding ρ0 on a 3D grid and then expanding the input features to include atomic fingerprints beyond ρ0. The prediction of the residual density (δρ) rather than ρ itself is targeted, and given that δρ is indicative of chemical bonding information, a dataset of small-sized organic molecules featuring diverse bonding characters is adopted. The fidelity of DeepSCF is finally enhanced by subjecting the atomic geometries of the dataset to random rotations and strains. The effectiveness of DeepSCF is demonstrated using a complex carbon nanotube-based DNA sequencer model. This work evidences that the nearsightedness in electronic structure can be optimally represented via the spatial locality in CNNs, offering insight into the success of various machine learning-based atomistic materials simulations.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"30 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142488773","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

Enhanced spin Hall ratio in two-dimensional semiconductors 二维半导体中的增强自旋霍尔比

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-10-23 DOI: 10.1038/s41524-024-01434-z

Jiaqi Zhou, Samuel Poncé, Jean-Christophe Charlier

引用次数: 0

Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis 主动学习加速探索氧电催化多金属体系中的单原子局部环境

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-10-19 DOI: 10.1038/s41524-024-01432-1

Hoje Chun, Jaclyn R. Lunger, Jeung Ku Kang, Rafael Gómez-Bombarelli, Byungchan Han

{"title":"Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis","authors":"Hoje Chun, Jaclyn R. Lunger, Jeung Ku Kang, Rafael Gómez-Bombarelli, Byungchan Han","doi":"10.1038/s41524-024-01432-1","DOIUrl":"https://doi.org/10.1038/s41524-024-01432-1","url":null,"abstract":"Single-atom catalysts (SACs) with multiple active sites exhibit high activity for a wide range of sluggish reactions, but identifying optimal multimetallic SAC is challenging due to the vast design space. Here, we present a self-driving computational strategy that combines first-principles calculations and equivariant graph neural network (GNN) to explore over 30,000 binary metallic sites with varying combinations of 3d transition metals and different ligand environments for oxygen reduction and evolution reactions (ORR/OER). Active learning facilitates the investigation of the search space by balancing the exploration of unseen atomic structures with the exploitation of the active ones. The GNN learns the chemical environments to capture composition-structure-property relationships for ORR/OER activity and selectivity. The computational predictions of promising Co-Fe, Co-Co, and Co-Zn metal pairs are consistent with the state-of-the-art results of experimental measurements reported in the literature. This approach can be extended to a broader class of multi-element high entropic materials systems.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"8 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451432","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

MD-HIT: Machine learning for material property prediction with dataset redundancy control MD-HIT：通过数据集冗余控制进行材料特性预测的机器学习

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-10-18 DOI: 10.1038/s41524-024-01426-z

Qin Li, Nihang Fu, Sadman Sadeed Omee, Jianjun Hu

引用次数: 0

Accurate formation enthalpies of solids using reaction networks 利用反应网络精确计算固体形成焓

IF 9.7 1区材料科学

npj Computational Materials Pub Date : 2024-10-14 DOI: 10.1038/s41524-024-01404-5

Rasmus Fromsejer, Bjørn Maribo-Mogensen, Georgios M. Kontogeorgis, Xiaodong Liang

{"title":"Accurate formation enthalpies of solids using reaction networks","authors":"Rasmus Fromsejer, Bjørn Maribo-Mogensen, Georgios M. Kontogeorgis, Xiaodong Liang","doi":"10.1038/s41524-024-01404-5","DOIUrl":"https://doi.org/10.1038/s41524-024-01404-5","url":null,"abstract":"Crystalline solids play a fundamental role in a host of materials and technologies, ranging from pharmaceuticals to renewable energy. The thermodynamic properties of these solids are crucial determinants of their stability and therefore their behavior. The advent of large density functional theory databases with properties of solids has stimulated research on predictive methods for their thermodynamic properties, especially for the enthalpy of formation ΔfH. Increasingly sophisticated artificial intelligence and machine learning (ML) models have primarily driven development in this field in recent years. However, these models can suffer from lack of generalizability and poor interpretability. In this work, we explore a different route and develop and evaluate a framework for the application of reaction network (RN) theory to the prediction of ΔfH of crystalline solids. For an experimental dataset of 1550 compounds we are able to obtain a mean absolute error w.r.t ΔfH of 29.6 meV atom−1 using the RN approach. This performance is better than existing ML-based predictive methods and close to the experimental uncertainty. Moreover, we show that the RN framework allows for straightforward estimation of the uncertainty of the predictions.","PeriodicalId":19342,"journal":{"name":"npj Computational Materials","volume":"9 1","pages":""},"PeriodicalIF":9.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431461","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