Data CI Pilot: CI-Based Collaborative Development of Data-Driven Interatomic Potentials for Predictive Molecular Simulations

数据 CI 试点：基于 CI 的数据驱动原子间势的协作开发，用于预测分子模拟

• 批准号: 2039575
• 负责人: Ellad Tadmor
• 金额: $ 112.8万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039575&HistoricalAwards=false
• 关键词: Data; CI; Pilot; Based; Collaborative

The project addresses a pressing need of the molecular simulation community by providing materials researchers with a powerful new ability to efficiently synthesize all available data and knowledge related to their particular problem of study. It convenes a consortium of materials researchers around the common goal of increasing interoperability of existing and emerging materials cyberinfrastructures supported by NSF and others. The PIs will develop a new computational framework that enables researchers to rapidly develop and deploy data-driven interatomic potentials for complex material systems, by connecting existing cyberinfrastructure resources of first-principles calculations and experimental data. This will provide qualitative insights into material behavior, as well as predictive capability necessary to design new materials and nanostructures.This project aims to accelerate the adoption of data-driven interatomic potential (DDIP) technology by removing existing barriers faced by materials researchers. Few groups in academia, government and industry have the capacity to develop DDIPs as this requires simultaneous expertise in the physics of the material being modeled, in “first principles” (FP) calculations, and in machine learning techniques, as well as access to extensive computational resources needed to construct the extremely large and diverse training sets required to fit high-quality DDIPs. To address this, a computational infrastructure called “ColabFit” is being developed that will enable researchers to collaborate on DDIP development by pooling their knowledge and data. Researchers will be able to train state-of-the-art DDIPs using a supported fitting code of choice, seamlessly access training data from existing cyberinfrastructure (CI) resources of FP data, and exchange DDIPs in a standard format through the Open Knowledgebase of Interatomic Models (OpenKIM) project so that they can build on each other’s work. This effort fills a pressing need in the materials simulation community, as evidenced by the large consortium of leading DDIP developers, CI projects, and materials standards organization that has been assembled to support it. To keep development of the ColabFit framework focused on real-world materials research needs, it will be organized around a target application of DDIP development for phase transformations in 2D transition metal dichalcogenides.This project is jointly supported by the Office of Advanced Cyberinfrastructure in the Computer and Information Sciences Directorate, and the Division of Materials Research in the Mathematical and Physical Sciences 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.

该项目通过为材料研究人员提供强大的新能力来有效地综合与其特定研究问题相关的所有可用数据和知识，解决了分子模拟界的迫切需求。它围绕提高互操作性的共同目标召集了一个材料研究人员联盟。 NSF 和其他机构支持的现有和新兴材料网络基础设施将开发一个新的计算框架，使研究人员能够通过连接现有的网络基础设施资源，快速开发和部署复杂材料系统的数据驱动的原子间潜力。第一性原理计算和实验数据。这将为材料行为提供定性见解，以及设计新材料和纳米结构所需的预测能力。该项目旨在通过消除现有的数据驱动原子间势（DDIP）技术来加速采用。学术界、政府和工业界很少有团体有能力开发 DDIP，因为这需要同时具备所建模材料的物理、“第一原理”(FP) 计算和机器学习技术方面的专业知识，也为了解决这个问题，正在开发一种名为“ColabFit”的计算基础设施，该基础设施将使研究人员能够通过汇集知识来合作开发 DDIP。研究人员将能够使用受支持的选择代码来训练最先进的 DDIP，从现有的 FP 数据网络基础设施 (CI) 资源中无缝访问训练数据，并通过以下方式以标准格式交换 DDIP：原子间模型开放知识库 (OpenKIM) 项目，以便它们能够在彼此的工作基础上进行构建，这一努力满足了材料模拟社区的迫切需求，领先的 DDIP 开发人员、CI 项目和材料标准组织组成的大型联盟证明了这一点。为了保持 ColabFit 框架的开发专注于现实世界的材料研究需求，它将围绕 DDIP 开发的目标应用进行组织，用于二维过渡金属的相变。该项目由计算机与信息科学局高级网络基础设施办公室和数学与物理科学局材料研究部共同支持。该奖项体现了NSF的法定使命，经评估认为值得支持利用基金会的智力优势和更广泛的影响审查标准。

项目成果

ColabFit exchange: Open-access datasets for data-driven interatomic potentials

ColabFit 交换：用于数据驱动的原子间势的开放访问数据集

• DOI: 10.1063/5.0163882
• 发表时间: 2023-10
• 期刊: The Journal of Chemical Physics
• 作者: Vita, Joshua A.;Fuemmeler, Eric G.;Gupta, Amit;Wolfe, Gregory P.;Tao, Alexander Quanming;Elliott, Ryan S.;Martiniani, Stefano;Tadmor, Ellad B.
• 通讯作者: Tadmor, Ellad B.

KLIFF: A framework to develop physics-based and machine learning interatomic potentials

KLIFF：开发基于物理和机器学习原子间势的框架

• DOI: 10.1016/j.cpc.2021.108218
• 发表时间: 2022-03
• 期刊: Computer Physics Communications
• 影响因子: 6.3
• 作者: Wen, Mingjian;Afshar, Yaser;Elliott, Ryan S.;Tadmor, Ellad B.
• 通讯作者: Tadmor, Ellad B.