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.

非技术摘要这一奖项支持OpenKim，该奖项是研究社区的网络基础结构的组成部分，该奖项根据牛顿的定律和模型，使用原子的计算机模拟，以攻击原子之间的相互作用，以攻击材料科学，工程和物理学中的问题，并启用新材料的发现，启用新设备，设计新设备，以提高对材料和材料相关的现象的了解，以及更多。近年来，在材料知识，发现和制造方法的领域取得了重大进步。 例如，这包括石墨烯的开发（单个具有特殊机械，热和电性能的碳原子的原子层）和相关类别的二维材料具有前所未有的材料特性的相关类别。 另一个例子是三维印刷技术的出现，该技术使工程师可以从头开始设计新材料，以便为其特定应用量身定制。 在原子规模上对材料的计算机模拟是推动当前材料革命的关键促成技术之一。 尽管最准确的原子尺度模拟采用了量子力学的方程式，但即使在当今强大的计算机上，这些计算也需要花费很长时间才能完成，实际上它们仅限于数千个原子。 对于研究材料特性的研究根本不足，这需要模拟数百万或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的近似方程（称为原子间模型（IMS））来描述原子相互作用。 这些模型很快，但通常它们仅对于限制材料范围的范围才能准确。 这种有限的适用性范围也需要创建许多IM，即使对于单一材料（例如硅）也是如此。 组织，共享和评估这些IMS的适用性范围一直是材料研究界的长期挑战。 在大多数情况下，研究人员无法知道哪个IM适合其特定应用。此外，IMS的扩散通常是为了与特定的模拟程序一起工作，使得很难共享和交换IMS，并重现其他研究人员的工作，这就是科学发展和自我纠正的方式。 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出处，选择和可移植性的问题。 该网站上存档的IMS经过详尽的测试，可以以各种主要的仿真代码符合KIM项目的一部分而以各种主要的仿真代码以插件方式使用。当前项目的开发活动将通过扩大支持IMS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，从而使研究人员可以轻松地将OpenKim.org上的IMS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用于研究和量化IMS做出的预测的固有不确定性，并协助材料研究人员为其应用选择最佳IM。 预计该项目的开发，教育和研究活动将大大增加KIM项目的用户群和更广泛的影响。 技术摘要这一奖项支持OpenKim，OpenKim是一个社区知识基础模型（KIM）进行模拟。 KIM是一个用于使IMS在材料分子模拟中使用的项目。 IM通常称为“电势”或“力场”，是计算原子构型能量及其衍生物的近似方法。 该项目涉及近年来引入的传统“基于物理”的IMS和新的“数据驱动” IMS。在持续的努力中，KIM项目开发了一个系统的框架，以解决材料研究人员面临的IM出处，选择和可移植性问题。 在KIM之前，这些挑战是研究管道中严重效率低下和不准确性的原因。 如今，OpenKim.org上的IM已进行了一组严格的“验证检查”，旨在确保其实施符合高软件工程标准，并符合大量的“测试”，每种测试都计算出一个确定的材料属性，以评估IM的准确性。 与实验性或量子“参考数据”相比，研究人员可以来到OpenKim.org并探索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成为从事分子模拟的研究人员工作流程的组成部分，以使其工作更有效，结果更加可靠和可重复。 为了实现这一愿景，首席研究人员（PIS）将遵循以下网络基础设施研发计划以及与IM使用和科学有关的基础研究。 网络基础设施研发将包括符合KIM标准的扩展，以支持其他常见的IM功能（例如长距离字段），并为具有尚未标准化的尖端功能的IMS提供了支持。 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，作为其日常工作流程的一部分。 这种方法减少了错误，确保可重复性，使用标准测试方法（体现在KIM测试中）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。 该项目的基础研究组成部分包括三个研究推力，需要提高分子模拟的可靠性：（1）IM不确定性：PIS将使用信息拓扑和差异几何形状中的思想来自动产生IM集合以获得IM固有不确定性的估计。 （2）IM可传输性：PIS计划适应多任务机器学习方法，以预测IM对于不同应用程序的准确性。 这将导致严格，客观的标准，以帮助研究人员进行IM选择。 （3）IM启发式方法：通过挖掘IM预测和参考数据在OpenKim.org上存档，可以识别类似于经验启发式方法的相关性，例如Vegard的规则以及显微镜特性与宏观特征之间的联系。 对这种启发式方法的检测将提供有关IMS局限性，帮助设计最佳训练集的见解，并可以更好地了解IMS的特性。 就更广泛的影响而言，由于从微生物学到地质学的物理科学中，分子模拟的普遍存在，KIM项目的范围异常大 - 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来吸引材料研究社区，以及（3）建立新的关系，以及与其他材料与其他材料建模的材料建模的新关系，并与该材料建模，该奖项是由该材料委员会共同支持了数学和现有机构的材料研究局，并支持了数学和工具，并建立了工具，并建立了工具和现有机构，机构和机构，该奖项。奖项反映了NSF的法定任务，并通过使用基金会的智力优点和更广泛的影响审查标准评估值得支持。

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.