Collaborative Research: CDS&E: Systematic Multiscale Modeling using the Knowledgebase of Interatomic Models (KIM)

非技术总结该奖项支持OPENKIM，支持研究人员使用基于牛顿定律的原子计算机模拟来攻击材料科学，化学，工程和物理问题，从而发现新材料，设计新设备，了解生化过程等等。 原子模拟在现实科学、工程和工业应用中起着关键作用。这些模拟越来越多地使用拟合的原子间模型（IM），描述原子相互作用时作用在原子上的力的数学处方，预测材料的性质，它们对外部应力的反应方式，并设计创新的纳米结构，原子的微小结构比人类头发小10万倍。在过去，这种原子模拟的潜力受到几个因素的限制：（1）缺乏标准化的应用程序编程接口，使得难以将IM从一个模拟程序转移到另一个模拟程序;（2）缺乏用于存储和交换IM的计算机实现的策划的电子库，使得难以再现已发表的结果;（3）缺乏用于比较IM的准确性的工具，使得难以在新的应用中有信心地使用IM。这些局限性已经通过创建原子间模型开放知识库（OpenKIM）得到了解决，OpenKIM是一个协作在线材料项目，旨在合理化，标准化和表征IM。该奖项支持OpenKIM，因为它将以重要的方式推动从电子电路到飞机制造等领域的科学和工程进步。它将在竞争对手的IM和模拟方法之间进行尖锐的评估和比较，使计算研究人员能够快速探索替代的已发表IM或开发和验证新的IM。它还将促进在科学模拟中复制结果。该项目将扩展OpenKIM，以吸引计算化学和分子生物学社区参与到这一材料奋进中，促进具有共同目标和兴趣但迄今为止因语言，单位和计算惯例而划分的两个社区之间的沟通。该小组的学生和博士后有机会与一个由知名科学家和工程师组成的国际跨学科小组合作，研究具有挑战性的科学问题，例如缺陷在确定材料性能中的作用以及电子设备操作中不满意的化学键的影响。通过降低进入计算材料科学的门槛，OpenKIM正在促进代表性不足的群体和来自发展中国家的人进入这一技术和科学中心领域。技术总结该奖项支持OpenKIM，一个合作的在线材料项目，用于合理化，标准化和表征用于表示材料模拟中原子之间的能量和力的原子间模型（IM）。该项目旨在支持、扩展和利用OpenKIM进行科学研究。主要研究人员将把材料界的智慧和经验与机器学习、数据挖掘和信息几何学的先进方法相结合，从根本上简化材料的原子模拟领域，并使其更加严格。OpenKIM代表了一个不寻常的机会来回答基本的科学问题。有了完全开放的访问，PI预计许多研究人员将使用丰富的OpenKIM知识库来解决该领域的科学和方法问题。PI将通过整合新的IM，参考数据和测试来支持这些活动，通过扩展KIM标准以支持远程静电场，Monte Carlo和生物分子结合力场，并继续提供有关KIM的文档，讲座，研讨会和教程。为了进一步推进KIM的使命，PI将解决两个广泛而迷人的问题，成功的顺序多尺度建模至关重要：（1）什么样的关键功能IM需要重现，以准确地模拟现象X在连续尺度？该项目将提供回答这一问题的工具，方法是：（a）开发各向异性材料特性的函数形式，以封装已知缺陷和界面的行为，这些缺陷和界面是已经被确定为对微观结构演化的连续模拟至关重要的特性，以及（B）使用从信息几何理论中收集的流形学习方法，该理论将微分几何技术应用于概率论领域，找到经验主义或规则，提供对一类IM的更高尺度行为的洞察，以及对真实的世界的洞察。(2)对于给定的应用程序X，给定的IM有多可靠？PI将通过以下方式解决不确定性量化的这一组成部分，也称为IM可转移性：（a）使用机器学习技术来识别与重要的连续统尺度材料属性密切相关的关键原子间配置，并使用统计方法来估计这些配置的IM不确定性，以及（B）使用IM拟合参数中的大的不确定性来提供IM预测中的系统误差的贝叶斯信息几何估计。

NONTECHNICAL SUMMARYThis award supports OPENKIM which supports the community of researchers using computer simulations of atoms based on Newton's Laws to attack materials science, chemistry, engineering, and physics problems enabling the discovery of new materials, the design of new devices, the understanding of biochemical processes and much more. Atomistic simulations play a key role in realistic scientific, engineering, and industrial applications. These simulations increasingly use fitted interatomic models (IMs), mathematical prescriptions that describe the forces acting on atoms when they interact, to predict the properties of materials, the way they respond to external stresses, and to design innovative nanostructures, tiny structures of atoms some 100,000 times smaller than a human hair. In the past the potential of atomistic simulations of this kind has been limited by several factors: (1) the lack of a standardized application programing interface has made it difficult to transfer IMs from one simulation program to another; (2) the lack of a curated electronic repository for storing and exchanging computer implementations of IMs has made it difficult to reproduce published results; (3) the lack of tools for comparing the accuracy of IMs made it difficult to use IMs with confidence in new applications. These limitations have been addressed by the creation of the Open Knowledgebase of Interatomic Models (OpenKIM), a collaborative online materials project to rationalize, standardize, and characterize IMs. This award supports OpenKIM as it goes forward in important ways that will facilitate scientific and engineering progress in fields from growing electronic circuits to airplane manufacture. It will make sharp evaluations and comparisons between rival IMs and simulation methods, allowing computational researchers to rapidly explore alternative published IMs or develop and validate new ones for their use. It will also facilitate replication of results in scientific simulations. This project will extend OpenKIM in order to draw the computational chemistry and molecular biology communities into this materials endeavor, facilitating communication between two communities with common goals and interests but hitherto divided by language, units, and computational conventions. Students and post-docs in the group have the opportunity of collaborating with an international, interdisciplinary group of well-known scientists and engineers on a cross-section of challenging scientific problems, such as the role of defects in determining properties of materials and the effect of unsatisfied chemical bonds in electronic device operation. By lowering the barriers to entry into computational materials science, OpenKIM is facilitating the entry of underrepresented groups and those from developing nations into this technologically and scientifically central field.TECHNICAL SUMMARYThis award supports OpenKIM, a collaborative online materials project to rationalize, standardize, and characterize interatomic models (IMs) used to represent energies and forces between atoms in materials simulations. This project is aimed to support, extend, and leverage OpenKIM to do science. The Principal Investigators will blend the wisdom and experience of the materials community with advanced methods from machine learning, data mining, and information geometry to radically simplify and make more rigorous the field of atomistic simulations of materials. OpenKIM represents an unusual opportunity to answer fundamental scientific questions. With full and open access, the PIs anticipate many researchers will use the rich OpenKIM Repository to address scientific and methodological questions of the field. The PIs will support these activities by incorporating new IMs, reference data, and tests, by extending the KIM standard to support long-range electrostatic fields, Monte Carlo, and biomolecular bonded force fields, and by continuing to provide documentation, talks, workshops, and tutorials on KIM. To further the KIM mission, the PIs will address two broad and fascinating issues of critical importance to successful sequential multiscale modeling: (1) What key features does an IM need to reproduce in order to accurately model phenomenon X at a continuum scale? The project will provide tools to answer this question, by (a) developing functional forms for anisotropic materials properties to encapsulate the behavior of known defects and interfaces which are properties already identified as vital for continuum simulation of microstructure evolution, and (b) using manifold-learning methods gleaned from information geometry theory, which applies the techniques of differential geometry to the field of probability theory, to find empirical heuristics or rules that provide insight into the higher scale behavior of a class of IMs, and insight on the real world. (2) How reliable will a given IM be for a given application X? The PIs will address this component of uncertainty quantification, also called IM transferability, by (a) using machine-learning techniques to identify key interatomic configurations which strongly correlate with important continuum scale materials properties and using statistical methods to estimate IM uncertainties for these configurations, and (b) using the large uncertainties in IM fitted parameters to provide Bayesian information geometry estimates for the systematic errors in IM predictions.