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Best practices for multi-fidelity Bayesian optimization in materials and molecular research.

Víctor Sabanza-Gil1,2,3,4, Riccardo Barbano4, Daniel Pacheco Gutiérrez4

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Multi-fidelity Bayesian optimization (MFBO) accelerates materials and molecular discovery by using data of varying accuracy and cost. This study provides guidelines for applying MFBO in experimental settings, enhancing chemical science research.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Drug discovery

Background:

  • Multi-fidelity Bayesian optimization (MFBO) leverages diverse data sources for efficient discovery.
  • Systematic evaluation of MFBO parameters is lacking for chemical applications.

Purpose of the Study:

  • To provide guidelines for employing MFBO in experimental settings.
  • To evaluate MFBO performance in molecular and materials discovery.
  • To benchmark MFBO against single-fidelity methods.

Main Methods:

  • Investigated two acquisition function families on synthetic problems.
  • Analyzed the impact of approximate function informativeness and cost.
  • Benchmarked MFBO on three real-world discovery problems using a custom implementation.

Main Results:

  • MFBO demonstrates potential for accelerating discovery processes.
  • Performance is sensitive to acquisition function choice and data fidelity.
  • Guidelines for MFBO implementation were developed.

Conclusions:

  • MFBO can be a valuable tool for chemical sciences when implemented with careful parameter selection.
  • Further research can refine MFBO strategies for broader adoption.
  • This work offers practical recommendations for experimental design.