Research Objectives: Advance multiscale spatial and time simulations via machine-learning.

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).