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Multiscaling Research Using Genetic Programming at Materials Computation Center, University of Illinois

This research at the Materials Computation Center, University of Illinois led by Duane Johnson and Richard Martin, funded by NSF DMR-03-25939, aims to advance multiscale simulations through machine learning. By utilizing Genetic Programming, the team regressed fine-scale information with minimal direct calculations, achieving remarkable results in alloy surface diffusion barriers and kinetic simulations. The approach significantly accelerates computations, enabling the study of complex systems with minimal data input. This innovative technique has broader implications in alloy constitutive laws and excited-state chemistry reactions.

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Multiscaling Research Using Genetic Programming at Materials Computation Center, University of Illinois

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  1. Materials Computation Center, University of Illinois Duane Johnson and Richard Martin, NSF DMR-03-25939Multiscaling using Genetic Programming D.D. Johnson, D.E. Goldberg., P. Bellon, and student K. Sastry (MatSE) • ResearchObjectives:Advance multiscale spatial and timesimulations via machine-learning. • Approach: Use Genetic Programming– a Genetic Algorithm that evolves a program – to regress allfine-scale information from only a few direct calculations. • Significant Results: Regressed all 8196vacancy-assisted diffusion barriers at alloy surface (due to local environments)with ~0.1% error using < 3% of the barriers, regardless of type of potential! • Found that less info needed with increasing complexity. • Allows Kinetic MC simulation of real time via in-line function “table”, rather than standard look-up table2,1. • 100x faster than table method during simulation. • 4-8 orders faster than “on-the-fly” type simulations. • Broader Impact: Allows addressing morecomplexity with less information; e.g, find constitutive law in alloys1; obtain accurate excited-state chemistryreactions by regressed semi-empirical potentials that rival ab initio CASSCF, for (on-going with T. Martinez, Chemistry). Kinetic Simulation: Surface of a binary alloy with two vacancies showing first and second nearest neighbor (n.n.) diffusion paths with first (green box) and second (red box) n.n. chemical arrangements. Potentials: Additive and Non-Additive Barrier Prediction: GP-predicted (red) vs. calculated (blue) using 3% of all 8192 barriers. 1. K. Sastry, et al., Int. J. of Multiscale Comput. Eng. (accepted). 2. K. Sastry, et al., Phys. Rev. Lett. (submitted).

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