SANE: strategic autonomous non-smooth exploration for multiple optima discovery in multi-modal and non-differentiable black-box functions

Journal Article · 18 February 2025 · Digital Discovery

DOI: https://doi.org/10.1039/D4DD00299G · OSTI ID:2522847

Biswas, Arpan  ^[1]; Vasudevan, Rama  ^[2]; Pant, Rohit  ^[3]; Takeuchi, Ichiro ^[3]; Funakubo, Hiroshi ^[4]; Liu, Yongtao  ^[2]

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1. University of Tennessee, Knoxville, TN (United States)
2. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
3. University of Maryland, College Park, MD (United States)
4. Tokyo Institute of Technology, Yokohama (Japan)

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-boxes and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into pre-acquired piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiments, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.

• Research Organization: The Pennsylvania State University, University Park, PA (United States)
• Sponsoring Organization: Japan Science and Technology Agency (JST); USDOE; USDOE Office of Science (SC), Basic Energy Sciences (BES)
• Grant/Contract Number: AC05-00OR22725; SC0021118
• OSTI ID: 2522847
• Journal Information: Digital Discovery, Journal Name: Digital Discovery Journal Issue: 3 Vol. 4; ISSN 2635-098X
• Publisher: Royal Society of Chemistry (RSC)Copyright Statement
• Country of Publication: United States
• Language: English