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NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering.

Yifan Wang1,2, Tai-Ying Chen1,2, Dionisios G Vlachos1,2

  • 1Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy St., Newark, Delaware 19716, United States.

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This summary is machine-generated.

NEXTorch is a new Python library that uses Bayesian optimization to accelerate chemical research. This data-driven approach optimizes experiments, saving time and resources in chemical and engineering applications.

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Area of Science:

  • Chemical Engineering
  • Computational Chemistry
  • Data Science

Background:

  • Chemical system automation and optimization demand efficient experimental design to minimize resources.
  • Data-driven active learning algorithms, particularly Bayesian optimization, are effective for these tasks.
  • Bayesian optimization is a sequential global optimization method proven in chemistry and engineering.

Purpose of the Study:

  • Introduce NEXTorch, a Python/PyTorch library for Bayesian optimization in laboratory and computational design.
  • Facilitate efficient experimental design and accelerate scientific discovery through automated optimization.

Main Methods:

  • Developed NEXTorch with features for fast predictive modeling and flexible optimization loops.
  • Integrated GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms.
  • Enabled easy interfacing with legacy software and supported automated/human-in-the-loop optimization.

Main Results:

  • NEXTorch offers visualization capabilities and handles diverse parameter and data types.
  • The library supports various applications including catalyst synthesis and reaction optimization.
  • Demonstrated efficiency in accelerating chemical and engineering design processes.

Conclusions:

  • NEXTorch provides a powerful, open-source tool for Bayesian optimization in scientific research.
  • Facilitates faster, more efficient experimental design and process optimization.
  • Comprehensive documentation and examples aid user adoption and application.