ITR - (ASE) - (sim+dmc): Parallel Data Mining for Nanoscale Kinetic Monte Carlo Simulation Models

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Intellectual Merit:Advances in computational science and technology have made the theoreticalmodeling of materials processes and properties viable, desirable, and a strong supplement to experimental work. Since the understanding and manipulation of the macroscopic properties of materials relies on information obtained at the microscopic level, one of the challenges in ASE is in developing the framework for a seamless, multiscale study of materials properties and related phenomena. The kinetic Monte Carlo method is one such technique which is suitable for simulations over a large range of length and time scales and which has the potential to connect atomistic details with macroscopic observations.The standard method is, however, handicapped because of the requirement of prior knowledge of theunderlying atomic mechanisms and their energetics. Typically only a few processes involving singleatom motion are provided as input to kinetic Monte Carlo simulations, thereby neglecting the role ofcollective atomic motion, vacancy creation, and complex atomic processes, as well as biasing the timeevolution of the system. In the proposed research in ASE with technical focus in sim and dmc, we planto overcome these limitations through the inclusion of unique and innovative pattern recognition schemesalong with automated procedures for the calculation of system energetics on the fly. This procedure willallow the development of an extensive database of possible atomic events. The database so collected willserve as input for further analysis and processing using machine learning and data mining for thedevelopment of efficient, robust, and accurate mapping functions which will be extensively tested throughsimulations of a variety of phenomena in epitaxial growth and validated through comparison with relevantexperimental data. The resulting mapping functions will serve to vastly increase the accuracy and speedof simulations.Broader Impact:Our goal of creating accurate and efficient computational algorithms for the simulationof phenomena such as thin-film growth will be a significant achievement in the technical focus areas ofsim and dmc, because of the innovative methodologies resulting from cross-disciplinary approaches. Thesuccessful implementation of the algorithms for computer design of materials, however, will be abreakthrough in ASE, as it will enable the development of technologically important materials with muchreduced cost and much greater control.The work will also provide us opportunities for educational and outreach activities with broad national, international and societal impact. Apart from the education and training of our graduate and undergraduate students in ITR, we will propose to work with the K-12 community in this endeavor. We intend to do so through the integration of research and education. Our team will collectively incorporate products of the research into courses on computational methods in physics, on data mining, on machine learning, and on adaptive parallelization techniques. A module for instructional and outreach purposes will also be developed. Two high school teachers will be recruited to spend summer sessions at KSU. Regular outreach activities with K-12 teachers and students will help broaden the pool of individuals in IT and nanoscale science literate individuals. Existing international collaborations of the PI with Prof. Alatalo, Finland, Dr. Trushin, Russia, and Dr. Durukanoglu, Turkey will help extend the outcomes of the proposed work internationally.