This 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.
This 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.
