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: 1834251
• 负责人: Ellad Tadmor
• 金额: $ 273.96万
• 依托单位: University of Minnesota-Twin Cities
• 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=1834251&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.

非技术摘要该奖项支持 OpenKIM，这是研究社区的一个网络基础设施组成部分，它使用基于牛顿定律和原子间相互作用模型的原子计算机模拟来解决材料科学、工程和物理学中的问题，并促进新发现材料、设计新设备、增进对材料相关现象的理解等等。近年来，材料知识、发现和制造方法领域取得了重大进展。 例如，这包括石墨烯（碳原子的单原子层，具有优异的机械、热和电性能）以及具有前所未有的材料性能的相关二维材料的开发，目前科学家和科学家正在广泛研究工程师。 另一个例子是三维打印技术的出现，使工程师能够从头开始设计可针对其特定应用量身定制的新材料。 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量子力学方程，但即使在当今功能强大的计算机上，此类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。 这对于材料特性的研究来说根本不够，因为材料特性的研究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（称为原子间模型 (IM)）来描述原子相互作用。 这些模型速度很快，但通常它们仅对有限范围的材料属性准确。 这种有限的适用范围需要创建许多 IM，即使对于硅等单一材料也是如此。 组织、共享和评估这些 IM 的适用范围一直是材料研究界面临的长期挑战。 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，IM 的激增（通常设计为仅适用于特定的模拟程序）使得共享和交换 IM 以及重现其他研究人员的工作变得困难，而这正是科学发展和自我修正的方式。原子间模型知识库（KIM） ）是一个致力于解决这些挑战的项目。 迄今为止，KIM 项目已在 https://openkim.org 上开发了一个在线框架，以解决 IM 来源、选择和可移植性问题。 该网站上存档的 IM 都经过了详尽的测试，并且可以以即插即用的方式用于各种主要模拟代码，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的 IM 的数量和类型来扩展 KIM 框架，并将添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。此外，信息拓扑和机器学习中的新兴技术将用于研究和量化 IM 预测中固有的不确定性，并帮助材料研究人员为其应用选择最佳的 IM。 该项目的开发、教育和研究活动预计将显着增加 KIM 项目的用户群和更广泛的影响。 技术摘要该奖项支持 OpenKIM，这是一个用于模拟的原子间模型 (KIM) 社区知识库。 KIM 是一个规范 IM 在材料分子模拟中使用的项目。 IM，通常称为“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 IM 和近年来推出的新型“数据驱动”IM。经过持续的努力，KIM 项目开发了一个系统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，这些挑战是导致研究流程严重低效和不准确的原因。 如今，openkim.org 上提供的 IM 需要经过一系列严格的“验证检查”，旨在确保其实现符合高软件工程标准，并接受一系列广泛的“测试”，其中每项测试都会计算用于评估 IM 准确性的明确定义的材料属性。 研究人员可以访问 openkim.org 并与实验或量子“参考数据”进行比较来探索 KIM 模型的预测，以选择适合其应用的 IM。当前的项目旨在扩展 KIM，使其成为从事分子模拟的研究人员工作流程中不可或缺的组成部分，从而使他们的工作更加高效，结果更加可靠和可重复。 为了实现这一愿景，首席研究员 (PI) 将开展以下与 IM 使用和科学相关的网络基础设施研发和基础研究计划。 网络基础设施研发将包括对 KIM 标准的扩展，以支持其他常见 IM 功能（例如远程字段），并增加对具有尚未标准化的尖端功能的 IM 的支持。 此外，KIM 将集成到现有的模拟工具中，以便研究人员可以在日常工作流程中查询和检索 openkim.org 上存档的数据。 这种方法减少了错误，确保了可重复性，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，需要取得进展以提高分子模拟的可靠性：（1）IM不确定性：PI将利用信息拓扑和微分几何的思想自动生成IM系综，以获得固有不确定性的估计即时消息的。 (2) IM 可转移性：PI 计划采用多任务机器学习方法来预测 IM 对于不同应用程序的准确性。 这将产生一个严格、客观的标准来帮助研究人员进行 IM 选择。 (3) IM 启发式：通过挖掘 IM 预测和 openkim.org 上存档的参考数据，可以识别类似于经验启发式的相关性，例如 Vegard 规则以及微观属性和宏观特征之间的联系。 对此类启发式的检测将有助于深入了解 IM 的局限性，帮助设计最佳训练集，并更好地理解 IM 的总体属性。 就更广泛的影响而言，KIM 项目的范围异常之大，远远超出了材料科学的范畴，因为分子模拟在从微生物学到地质学的整个物理科学领域都很盛行。 该项目旨在通过以下方式最大限度地发挥其影响力：(1) 扩大 KIM 用户群，(2) 通过有针对性的研究和教育工作直接吸引材料研究界的参与，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该奖项由数学和物理科学理事会材料研究部和工程理事会土木、机械和制造创新部共同支持。该奖项反映了 NSF 的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准。

项目成果

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

使用 OpenKIM 框架自动确定晶界能量和电势依赖性

• DOI: 10.1016/j.commatsci.2023.112057
• 发表时间: 2022-12-22
• 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
• 通讯作者: B. Runnels

Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties

将经验原子间势的领域知识注入神经网络以预测材料特性

• DOI: 10.48550/arxiv.2210.08047
• 发表时间: 2022-10-14
• 期刊: ArXiv
• 作者: Zeren Shui;Daniel S. Karls;Mingjian Wen;Ilia Nikiforov;E. Tadmor;G. Karypis
• 通讯作者: G. Karypis

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: 2022-09
• 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

OpenKIM 处理管道：基于云的自动材料属性计算引擎

• DOI: 10.1063/5.0014267
• 发表时间: 2020-08
• 期刊: The Journal of Chemical Physics
• 作者: Karls, D. S.;Bierbaum, M.;Alemi, A. A.;Elliott, R. S.;Sethna, J. P.;Tadmor, E. B.
• 通讯作者: Tadmor, E. B.

Selecting simple, transferable models with the supremum principle

根据至上原则选择简单、可转移的模型

• DOI: 10.1103/physrevresearch.4.l032044
• 发表时间: 2022-09
• 期刊: Physical Review Research
• 影响因子: 4.2
• 作者: Petrie, Cody;Anderson, Christian;Maekawa, Casie;Maekawa, Travis;Transtrum, Mark K.
• 通讯作者: Transtrum, Mark K.