1. The Materials Computation Center, University of Illinois Duane Johnson and Richard Martin (PIs), NSF DMR-03-25939 www.mcc.uiuc.edu OBJECTIVE: To accelerate Quantum Chemistry (QC) simulations of excited-state reactions by +1000 times by creating semi-empirical potentials with accuracy approaching that of high-level methods. APPROACH: Use machine-learning methods based upon efficient, Competent Genetic Algorithms (eCGA) and multi-objective optimization (MO). WHY IT MATTERS: With fast but accurate semi- empirical potentials, we can search for new drugs or critical biological reactions 100 – 1000 times faster! Accurate Semi-Empirical Quantum Chemistry via Evolutionary Algorithms STRATEGY: Using well-known semiempirical (MP3) QC potential* we optimize two objectives (error in energy and energy-gradient) for ethylene (C 2 H 4 ) using a few excited-state structures calculated from high-level QC (ab initio CASSCF learning set) to predict excited-states not in the learning set. *MP3 potential has 11 parameters just for carbon. We get agreement with high-level QC methods: 160±40 fs (vs.180±50 fs) for excitation decay. D 2d excitation energy 2.5 eV (vs 2.5 eV). Pyramidalization energy 0.9 eV (vs 0.9 eV). Potentials 1000x faster and “transferable” to other C-H based molecules. Duane Johnson, Todd Martinez, and David Goldberg and Students: Kumara Sastry and Alexis Thompson Physically most optimal o Stable Solutions + Sensitive/Unstable RESULT: (upper) An excitation of ethylene and example excited-state configurations. (lower) Multi-objective Genetic Algorithms found the “best MP3 solutions” (circles) with “on-the-fly” sensitivity analysis of solution sets.  All optimal solution sets agree with our high- level QC calculations. E Pyramidalized D 2d twisted

2. The Materials Computation Center, University of Illinois Duane Johnson and Richard Martin (PIs), NSF DMR-03-25939 www.mcc.uiuc.edu OUTLOOK: We are finalizing analysis for ethylene and benzene and details of why “non-dominate” Pareto front and eCGA are necessary to do well, as opposed to standard GA’s being used in chemistry. We have: provided MO-GA code on Software Archive©. proven the utility of MO-GA using eCGA. shown transferability of potentials for other C-H molecules that were not used in learning set. verified the cusp surfaces of the excited molecules are described well by semi-empirical potentials. revealed the importance of “non-domininant Pareto fronts”, “crowding distances”, and “tournament selection” to obtain good MO-GA solutions. PUBLICATIONS 2006-2007: Kumara Sastry, D.D. Johnson, Alexis L. Thompson, D.E. Goldberg, T.J. Martinez, "Optimization of Semiempirical Quantum Chemistry Methods via Multiobjective Genetic Algorithms: Accurate Photochemistry for Larger Molecules and Longer Time Scales" (invited) Materials and Manufacturing Processes 22 (2007) 553 - 561. Kumara. Sastry, D.D. Johnson, and D.E. Goldberg, "Scalability of a Hybrid Extended Compact Genetic Algorithm for Ground State Optimization of Clusters,” (invited) Materials and Manufacturing Processes 22 (2007) 570 - 576. Kumara Sastry, D.D. Johnson, Alexis L. Thompson, D.E. Goldberg, T.J. Martinez, J. Leiding, and Jane Owens, "Multiobjective Genetic Algorithms for Multiscaling Excited-State Dynamics in Photochemistry," GECCO 1745-1752 (2006) *Silver Medal, Best Paper in real-world track. Recognition and Industry: At Genetic and Evolutionary Computation Conf. 2006. Awarded Silver “Hummie” Medal. Awarded “Best Paper” in Real-World Applications. Student Kumara Sastry was finalist for the Lemelson- MIT innovation prize. US provisional patent application made.  K. Sastry joining INTEL in fall 2007 to develop future- generation chip via optimization.