Collaborative Research: Reliable Materials Simulation based on the Knowledgebase of Interatomic Models (KIM)

协作研究：基于原子间模型知识库（KIM）的可靠材料模拟

• 批准号: 1834332
• 负责人: Mark Transtrum
• 金额: $ 40.84万
• 依托单位: Brigham Young University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1834332&HistoricalAwards=false
• 关键词: Collaborative; Research; Reliable; Materials; Simulation

NONTECHNICAL SUMMARYThis award supports OpenKIM, a cyberinfrastructure component of the research community that uses computer simulations of atoms based on Newton's Laws and models for the interaction between atoms, to attack problems in materials science, engineering, and physics, and to enable the discovery of new materials, design new devices, to advance the understanding of materials-related phenomena, and much more. Recent years have seen significant advancement in the areas of materials knowledge, discovery, and manufacturing methodologies. This includes, for example, the development of graphene (a single atomic layer of carbon atoms, which has exceptional mechanical, thermal, and electrical properties) and the related class of two-dimensional materials with unprecedented material properties now being extensively studied by scientists and engineers. Another example is the advent of three-dimensional printing techniques that allow engineers to design new materials from the ground up that can be tailor-made for their specific application. Computer simulation of materials at the atomic-scale is one of the key enabling technologies driving the current materials revolution. Although the most accurate atomic-scale simulations employ the equations of quantum mechanics, such computations take so long to complete, even on today's powerful computers, that practically they are limited to a few thousands of atoms. This is simply not enough for the study of materials properties, which requires the simulation of interactions between millions and even billions of atoms. Thus, materials researchers rely on faster more approximate equations, known as interatomic models (IMs), to describe atomic interactions. These models are fast, but typically they are only accurate for a restricted range of material properties. This limited range of applicability necessitates the creation of many IMs, even for a single material such as silicon. Organizing, sharing, and evaluating the range of applicability of these IMs has been a long-standing challenge for the materials research community. In most cases researchers have no way of knowing which IM is suitable for their particular application. Further, the proliferation of IMs, often designed to work only with specific simulation programs, makes it difficult to share and exchange IMs, and to reproduce other researchers' work, which is how science evolves and self corrects.The Knowledgebase of Interatomic Models (KIM) is a project that is working to solve these challenges. To date, the KIM project has developed an online framework at https://openkim.org to address the issues of IM provenance, selection, and portability. IMs archived on this website are exhaustively tested and can be used in plug-and-play fashion in a variety of major simulation codes that conform to a standard developed as part of the KIM project. The development activity of the current project will extend the KIM framework by broadening the number and types of supported IMs, and will add new capabilities and educational resources that will make it easy for researchers to integrate the IMs and materials data available on openkim.org into their daily research workflow. Further, emerging techniques in information topology and machine learning will be applied to study and quantify the inherent uncertainty in predictions made by IMs, and to assist materials researchers to select the best IM for their application. Together the development, educational, and research activities of this project are expected to significantly increase the userbase and broader impact of the KIM project. TECHNICAL SUMMARYThis award supports OpenKIM, a community Knowledgebase of Interatomic Models (KIM) for simulation. KIM is a project for normalizing the use of IMs in molecular simulations of materials. An IM, often referred to as a "potential" or "force field," is an approximate method for computing the energy and its derivatives for an atomic configuration. This project addresses both traditional "physics-based" IMs and the new class of "data-driven" IMs introduced in recent years. In a sustained effort, the KIM project has developed a systematic framework to address the IM provenance, selection, and portability problems faced by materials researchers. Before KIM, these challenges were the cause of significant inefficiencies and inaccuracies in the research pipeline. Today, an IM available on openkim.org is subjected to a rigorous set of "Verification Checks" that aim to ensure that its implementation conforms to a high software-engineering standard, and to an extensive set of "Tests," each of which computes a well-defined material property for assessing the IM's accuracy. A researcher can come to openkim.org and explore the predictions of KIM Models in comparison with experimental or quantum "Reference Data" to select a suitable IM for their application. The current project is aimed at extending KIM to become an integral component of the workflow of researchers engaged in molecular simulation to make their work more efficient and their results more reliable and reproducible. To achieve this vision, the Principal Investigators (PIs) will pursue the following program of cyberinfrastructure R&D and basic research related to IM usage and science. The cyberinfrastructure R&D will include extensions to KIM standards to support additional common IM features (such as long-range fields) and added support for IMs having cutting-edge features that cannot yet be standardized. Further, KIM will be integrated into existing simulation tools so that researchers may query and retrieve data archived on openkim.org as part of their daily workflow. This approach reduces errors, ensures reproducibility, uses a standard tested method (embodied in a KIM Test) to obtain the desired property, and firmly integrates the KIM framework into the workflow of computational materials researchers. The basic research component of the project includes three research thrusts requiring advances to enhance the reliability of molecular simulations: (1) IM Uncertainty: The PIs will use ideas from information topology and differential geometry to automatically generate IM ensembles for obtaining estimates of the inherent uncertainty of the IM. (2) IM Transferability: The PIs plan to adapt a multi-task machine learning approach to predict an IM's accuracy for different applications. This will lead to a rigorous, objective criterion to assist researchers with IM selection. (3) IM Heuristics: By mining IM predictions and Reference Data archived on openkim.org, it is possible to identify correlations similar to empirical heuristics such as Vegard's rule and connections between microscopic properties and macroscopic features. Detection of such heuristics will provide insights into the limitations of IMs, help design optimal training sets, and lead to better understanding of the properties of IMs generally. In terms of broader impacts, the scope of the KIM project is unusually large - far beyond materials science - due to the prevalence of molecular simulations across the physical sciences from microbiology to geology. The project aims to maximize its impact by (1) expanding the KIM user base, (2) engaging the materials research community directly and through targeted research and educational efforts, and (3) developing new relationships and collaborations with other materials modeling cyberinfrastructures and organizations.This award is jointly supported by the Division of Materials Research in the Directorate for Mathematical and Physical Sciences and the Civil, Mechanical and Manufacturing Innovation Division in the Engineering Directorate.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项支持OpenKIM，一个研究社区的网络基础设施组成部分，它使用基于牛顿定律的原子计算机模拟和原子之间相互作用的模型，来解决材料科学、工程和物理学中的问题，并使新材料的发现、设计新设备、推进对材料相关现象的理解等等。近年来，在材料知识、发现和制造方法方面取得了重大进展。例如，石墨烯（由碳原子组成的单原子层，具有优异的机械、热学和电学性能）和相关的二维材料的开发，这些材料具有前所未有的材料性能，目前正受到科学家和工程师的广泛研究。另一个例子是三维打印技术的出现，它使工程师能够从头开始设计新材料，并为其特定应用量身定制。原子尺度材料的计算机模拟是推动当前材料革命的关键使能技术之一。尽管最精确的原子尺度模拟采用了量子力学方程，但即使在当今功能强大的计算机上，这样的计算也需要很长时间才能完成，实际上它们仅限于几千个原子。这对于材料性质的研究来说是远远不够的，因为材料性质需要模拟数百万甚至数十亿个原子之间的相互作用。因此，材料研究人员依靠更快更近似的方程，称为原子间作用模型（IMs），来描述原子间的相互作用。这些模型速度很快，但通常它们只对有限范围的材料特性准确。这种有限的应用范围需要创建许多IMs，即使是单一的材料，如硅。组织、共享和评估这些IMs的适用性范围一直是材料研究界面临的一个长期挑战。在大多数情况下，研究人员无法知道哪种IM适合他们的特定应用。此外，即时通讯的激增通常被设计为只能与特定的模拟程序一起工作，这使得共享和交换即时通讯以及复制其他研究人员的工作变得困难，而这正是科学发展和自我纠正的方式。原子间模型知识库（KIM）就是一个致力于解决这些挑战的项目。迄今为止，KIM项目已经在https://openkim.org上开发了一个在线框架，以解决IM的来源、选择和可移植性问题。本网站上存档的im经过了详尽的测试，可以在各种主要模拟代码中以即插即用的方式使用，这些代码符合作为KIM项目一部分开发的标准。当前项目的开发活动将通过扩大支持im的数量和类型来扩展KIM框架，并将增加新的功能和教育资源，使研究人员能够轻松地将openkim.org上提供的im和材料数据集成到他们的日常研究工作流程中。此外，信息拓扑和机器学习方面的新兴技术将被应用于研究和量化IM预测中的固有不确定性，并帮助材料研究人员为其应用选择最佳IM。这个项目的开发、教育和研究活动预计将显著增加KIM项目的用户基础和更广泛的影响。该奖项支持OpenKIM，一个用于模拟的原子间模型（KIM）社区知识库。KIM是一个规范在材料分子模拟中使用IMs的项目。IM，通常被称为“势”或“力场”，是计算原子构型的能量及其导数的近似方法。该项目涉及传统的“基于物理的”即时通讯和近年来引入的新型“数据驱动的”即时通讯。在持续的努力下，KIM项目已经开发了一个系统的框架来解决材料研究人员面临的IM来源、选择和可移植性问题。在KIM之前，这些挑战是研究管道中显著低效率和不准确的原因。今天，openkim.org上可用的IM都要经过一套严格的“验证检查”，旨在确保其实现符合高软件工程标准，并经过一套广泛的“测试”，每个测试都计算一个定义良好的材料属性，以评估IM的准确性。研究人员可以访问openkim.org，并将KIM模型的预测与实验或量子“参考数据”进行比较，以选择适合其应用的IM。目前的项目旨在扩展KIM，使其成为从事分子模拟的研究人员工作流程的一个组成部分，使他们的工作更有效率，结果更可靠和可重复。为了实现这一愿景，首席研究人员（pi）将开展以下网络基础设施研发计划，以及与IM使用和科学相关的基础研究。网络基础设施研发将包括对KIM标准的扩展，以支持额外的通用IM功能（如远程字段），并增加对具有尚未标准化的尖端功能的IM的支持。此外，KIM将集成到现有的仿真工具中，以便研究人员可以查询和检索openkim.org上存档的数据，作为他们日常工作的一部分。这种方法减少了误差，确保了再现性，使用标准测试方法（体现在KIM测试中）来获得所需的属性，并将KIM框架牢固地集成到计算材料研究人员的工作流程中。该项目的基础研究部分包括三个研究重点，需要提高分子模拟的可靠性：(1)IM不确定性：pi将使用信息拓扑和微分几何的思想来自动生成IM集成，以获得IM固有不确定性的估计。(2) IM的可移植性：pi计划采用多任务机器学习方法来预测IM在不同应用中的准确性。这将导致一个严格的，客观的标准，以协助研究人员与IM选择。(3) IM启发式：通过挖掘IM预测和openkim.org上的参考数据，可以识别类似于经验启发式（如Vegard规则）的相关性以及微观属性和宏观特征之间的联系。这种启发式的检测将提供对即时消息的局限性的见解，帮助设计最佳训练集，并导致更好地理解即时消息的一般属性。就更广泛的影响而言，由于分子模拟在从微生物学到地质学等物理科学领域的流行，KIM项目的范围异常之大——远远超出了材料科学。该项目旨在通过(1)扩大KIM用户群，(2)通过有针对性的研究和教育工作直接参与材料研究界，以及(3)与其他材料建模网络基础设施和组织建立新的关系和合作，最大限度地发挥其影响。该奖项由数学和物理科学理事会的材料研究部以及工程理事会的土木、机械和制造创新部联合支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Roadmap on multiscale materials modeling

• DOI: 10.1088/1361-651x/ab7150
• 发表时间: 2020-03
• 期刊: Modelling and Simulation in Materials Science and Engineering
• 影响因子: 1.8
• 作者: E. Giessen;P. Schultz;N. Bertin;V. Bulatov;W. Cai;Gábor Csányi;S. Foiles;M. Geers;C. González;M. Hütter;Woo Kyun Kim;D. Kochmann;J. Llorca;A. Mattsson;J. Rottler;A. Shluger;R. Sills;I. Steinbach;A. Strachan;E. Tadmor

Automated determination of grain boundary energy and potential-dependence using the OpenKIM framework

• DOI: 10.1016/j.commatsci.2023.112057
• 发表时间: 2022-12
• 期刊: Computational Materials Science
• 影响因子: 3.3
• 作者: Brendon Waters;Daniel S. Karls;I. Nikiforov;R. Elliott;E. Tadmor;B. Runnels

Hybrid neural network potential for multilayer graphene

• DOI: 10.1103/physrevb.100.195419
• 发表时间: 2019-09
• 期刊: Physical Review B
• 影响因子: 3.7
• 作者: Mingjian Wen;E. Tadmor

Extending OpenKIM with an Uncertainty Quantification Toolkit for Molecular Modeling

使用分子建模的不确定性定量工具包扩展 OpenKIM

• 发表时间: 2022
• 期刊: escience-2022
• 作者: Kurniawan, Yonatan;Petrie, Cody;Transtrum, Mark;Tadmor, Ellad;Elliott, Ryan;Karls, Daniel;Wen, Mingjian
• 通讯作者: Wen, Mingjian

The OpenKIM processing pipeline: A cloud-based automatic material property computation engine

• DOI: 10.1063/5.0014267
• 发表时间: 2020-08-14
• 期刊: JOURNAL OF CHEMICAL PHYSICS
• 影响因子: 4.4
• 作者: Karls, D. S.;Bierbaum, M.;Tadmor, E. B.
• 通讯作者: Tadmor, E. B.