Go to ICML 2026 Workshop AI4Science homepageLithography Solvent Discovery using Neuro-Symbolic Search Agent
Track: Track 1: Original Research/Position/Education/Attention Track
TL;DR: We propose AI4S-SDS, a diversity-aware neuro-symbolic framework combining LLM-based hypothesis generation with differentiable physics-informed optimization for feasibility-constrained solvent discovery.
Abstract: Scientific formulation discovery often unfolds under imperfect evaluators: wet-lab validation is expensive, design spaces are combinatorial, and available proxy scores only partially reflect downstream experimental performance. We study this challenge in lithography solvent design, a mixed discrete--continuous problem that requires selecting solvent components and optimizing their mixing ratios under explicit physicochemical constraints. We propose, a diversity-aware neuro-symbolic search framework for solvent discovery under evaluator uncertainty. LLMs act as chemistry-informed hypothesis generators over discrete formulation topologies, while a differentiable physics-informed module refines continuous mixture ratios and enforces feasibility. To reduce premature collapse toward evaluator-preferred patterns, combines sibling-aware local diversification with memory-driven global planning. Experiments show that maintains full compliance with the explicit physicochemical constraints adopted in our framework and improves exploration diversity over score-centric baselines. Preliminary lithography tests further suggest that representative candidates discovered through diverse search can exhibit favorable qualitative pattern definition under the tested conditions, even when they are not top-ranked by the proxy evaluator. These results highlight the value of diversity-aware search for scientific discovery when available evaluators are informative but incomplete.
Keywords: AI for Science, Neuro-Symbolic Search, Diversity-Aware Search, Solvent Design, Lithography, Large Language Models
Submission Number: 331
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