Data-driven optimization: efficient adaptive learning for self-driving laboratories

Abstract

Self-driving laboratories promise to compress materials-discovery timelines from years to weeks by replacing trial-and-error experimentation with closed-loop, algorithm-guided campaigns. Yet, despite the rapid proliferation of robotic and automation hardware, today's autonomous labs rely almost exclusively on Bayesian optimization (BO) to decide what experiment to run next. BO is a sensible approach to low-dimensional optimization problems with smooth response surfaces, but it struggles in precisely the regimes that matter most for real materials campaigns: tight experimental budgets, dozens of process parameters, mixed-integer choices, hard physical constraints, and noisy expensive measurements. In this talk, I will show how moving from BO to partitioning-based algorithms can substantially improve data efficiency, scale gracefully to dozens of process variables, and handle the constraints and noise that characterize realistic experimental campaigns. I will summarize a recently completed large-scale black-box optimization (BBO) benchmark in which we compared 42 solvers across 502 problems ranging from one to 300 dimensions and from smooth and convex to nonsmooth and nonconvex. The results overturn several community assumptions: BO solves only about 9% of problems within a 2,500-evaluationbudget, while a new branch-and-model (BAM) algorithm reaches an 81% success rate, with GLCCLUSTER, MULTIMIN, MCS, and SNOBFIT also performing strongly. A minimal, irreducible set of eight complementary solvers attains 88% solvability on the full suite. I will then move from in-silico benchmarks to the wet lab, presenting a recent algorithmguided experimental campaign on high-performance perovskite solar cells in which a non- BO solver was used to co-optimize six process variables spanning the perovskite, electrontransport, and hole-transport layers. Time permitting, I will also share early results from applying ensembles of BBO algorithms to digital twins of self-driving labs across additional materials systems. I will close with a forward-looking research vision: accelerating autonomous labs by developing, benchmarking, and experimentally validating data-efficient adaptive algorithms across batteries, semiconductors, catalysts, polymeric membranes, and biomolecules. The benchmarking software will be released as open source, with BAM and most BBO software available free to academic users, so that experimental groups can deploy these tools on their own self-driving platforms. Bio: Nick Sahinidis is the Butler Family Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering and the School of Chemical and Biomolecular Engineering at Georgia Tech. His current research activities are at the interface between computer science and operations research, with applications in various engineering and scientific areas, including: global optimization of mixed-integer nonlinear programs: theory, algorithms, and software; informatics problems in chemistry and biology; process and energy systems engineering. Professor Sahinidis teaches mathematical optimization, process systems engineering, and scientific computing. He has developed a bioinformatics M.S. program and has taught courses ranging from thermodynamics and metabolic engineering to approximation algorithms and GPU computing. Sahinidis has served on the editorial boards of many leading journals and in various positions within AIChE (American Institute of Chemical Engineers). He received an NSF CAREER award, the INFORMS Computing Society Prize, the MOS Beale-Orchard-Hays Prize, the Computing in Chemical Engineering Award, the Constantin Carathéodory Prize, and the National Award and Gold Medal from the Hellenic Operational Research Society. Sahinidis is a member of the U.S. National Academy of Engineering and a fellow of AIChE and INFORMS.