Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial

• URL: http://arxiv.org/abs/2604.01328v3
• Date: Tue, 07 Apr 2026 15:06:34 GMT
• Title: Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
• Abstract summary: This tutorial presents Bayesian optimisation (BO), a principled probability-driven framework that formalises and automates scientific discovery.<n>We first frame scientific discovery as an intuitive problem, then unpack BO's core components, end-to-end, and real-world efficacy.<n>We also cover critical technical extensions for scientific applications, including batched experimentation, heteroscedasticity, contextual optimisation, and human-in-the-loop integration.
• Score: 43.112626387235586
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional scientific discovery relies on an iterative hypothesise-experiment-refine cycle that has driven progress for centuries, but its intuitive, ad-hoc implementation often wastes resources, yields inefficient designs, and misses critical insights. This tutorial presents Bayesian Optimisation (BO), a principled probability-driven framework that formalises and automates this core scientific cycle. BO uses surrogate models (e.g., Gaussian processes) to model empirical observations as evolving hypotheses, and acquisition functions to guide experiment selection, balancing exploitation of known knowledge and exploration of uncharted domains to eliminate guesswork and manual trial-and-error. We first frame scientific discovery as an optimisation problem, then unpack BO's core components, end-to-end workflows, and real-world efficacy via case studies in catalysis, materials science, organic synthesis, and molecule discovery. We also cover critical technical extensions for scientific applications, including batched experimentation, heteroscedasticity, contextual optimisation, and human-in-the-loop integration. Tailored for a broad audience, this tutorial bridges AI advances in BO with practical natural science applications, offering tiered content to empower cross-disciplinary researchers to design more efficient experiments and accelerate principled scientific discovery.

Related papers

• Accelerating Scientific Research with Gemini: Case Studies and Common Techniques [105.15622072347811]
  Large language models (LLMs) have opened new avenues for accelerating scientific research.<n>We present a collection of case studies demonstrating how researchers have successfully collaborated with advanced AI models.
  arXiv  Detail & Related papers  (2026-02-03T18:56:17Z)
• Operationalizing Serendipity: Multi-Agent AI Workflows for Enhanced Materials Characterization with Theory-in-the-Loop [0.0]
  SciLink is an open-source, multi-agent artificial intelligence framework designed to operationalize serendipity in materials research.<n>It creates a direct, automated link between experimental observation, novelty assessment, and theoretical simulations.<n>We show its application to atomic-resolution and hyperspectral data, its capacity to integrate real-time human expert guidance, and its ability to close the research loop.
  arXiv  Detail & Related papers  (2025-08-07T04:59:17Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning [1.1057473962658189]
  Integration of theory into automated and autonomous experimental setups is emerging as a crucial objective for accelerating scientific research. Here, we introduce a method for integrating theory into the loop through Bayesian co-navigation of theoretical model space and experimentation. While demonstrated here within the context of functional responses in ferroelectric materials, our approach holds promise for broader applications.
  arXiv  Detail & Related papers  (2024-04-19T14:11:32Z)
• Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties [49.351577714596544]
  We propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into surrogate modeling. We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments.
  arXiv  Detail & Related papers  (2024-02-27T09:23:13Z)
• HypBO: Accelerating Black-Box Scientific Experiments Using Experts' Hypotheses [0.0]
  We exploit expert human knowledge in the form of hypotheses to direct Bayesian searches more quickly to promising regions of chemical space. Our proposed method, which we call HypBO, uses expert human hypotheses to generate improved seed samples.
  arXiv  Detail & Related papers  (2023-08-22T20:59:21Z)
• GFlowNets for AI-Driven Scientific Discovery [74.27219800878304]
  We present a new probabilistic machine learning framework called GFlowNets. GFlowNets can be applied in the modeling, hypotheses generation and experimental design stages of the experimental science loop. We argue that GFlowNets can become a valuable tool for AI-driven scientific discovery.
  arXiv  Detail & Related papers  (2023-02-01T17:29:43Z)
• Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling [58.56638141701966]
  Experimenters can often acquire the knowledge about the location of the global optimum. It is unknown how to incorporate the expert prior knowledge about the global optimum into Bayesian optimization. An efficient Bayesian optimization approach has been proposed via posterior sampling on the posterior distribution of the global optimum.
  arXiv  Detail & Related papers  (2020-02-26T01:57:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.