BASIL: Bayesian Application for Scientific Iteration and Learning

Kelvin P. Idanwekhai; Valeriia Kaneva; Stefano Menegatti; Alexander Tropsha

cs.LGJun 19, 2026

BASIL: Bayesian Application for Scientific Iteration and Learning

Authors: Kelvin P. Idanwekhai, Valeriia Kaneva, Stefano Menegatti, Alexander Tropsha

Organizations: Department of Chemistry, UNC Chapel Hill, Chapel Hill, NC, USA · Laboratory for Molecular Modeling (MML), UNC Chapel Hill, Chapel Hill, NC, USA · Department of Chemical and Biomolecular Engineering, NC State University, Raleigh, NC, USA · ChromaGenix, Raleigh, NC, USA

Abstract

We introduce BASIL, a user-friendly desktop application for process optimization. BASIL employs a Bayesian approach, incorporating special acquisition functions that can be used to solve both single and multi-objective optimization problems. It provides a graphical interface that enables users to input their experimental parameters, optimization objectives, and legacy data. This is then used to build surrogate models, which are coupled with acquisition functions to guide and optimize a process towards a desired objective. To facilitate model building, BASIL provides a variety of predefined surrogate model templates. BASIL can be used to optimize any arbitrary experiment or process with known, user-defined input variables, optimization objectives, and defined output.

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DASH: Decoupled Adaptive Surrogate - Acquisition Harness for Automated Bayesian Optimization

Changquan Zhao, Yuxiang Sun, Ruihao Zhu, Cheng Hua+1

Global College, Shanghai Jiao Tong University, Shanghai, China · Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China · SC Johnson College of Business, Cornell University, Ithaca, NY, USA

Bayesian optimization (BO) relies on a surrogate model and an acquisition function, yet the most suitable choices vary across tasks and optimization stages. Automated Bayesian optimization (AutoBO) addresses this variability by adapting BO components online. However, existing AutoBO methods either adapt one component, leaving the other mismatched and creating a bottleneck, or jointly select surrogate--acquisition pairs under a shared criterion, overlooking their distinct roles: surrogate selection depends on predictive reliability, whereas acquisition adaptation should respond to campaign context.In this paper, we propose DASH, a Decoupled Adaptive Surrogate--Acquisition Harness for large-language- model (LLM)-enhanced AutoBO. DASH selects surrogates by predictive reliability, uncertainty calibration, and ranking consistency; its two-stage acquisition controller periodically reallocates quotas across acquisition functions, builds a BO shortlist accordingly, and delegates final selection to an LLM. DASH also incorporates an integrated harness, consisting of knowledge-guided warm start and structured memory, to ground optimization in domain knowledge and accumulated feedback. Across four chemical optimization tasks, DASH outperforms the best AutoBO baseline by 12.51% in trajectory-level Acceleration Factor and 5.00% in endpoint Enhancement Factor. Results remain strong across LLM backbones, and ablations verify the complementary contributions of all components. Full-table and behavioral contamination checks find no detectable evidence that direct benchmark memorization or source-cell leakage explains these gains.

Bayesian Optimization Accurate Surrogate Models Memorization

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Abstract

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