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The new Paddy software package, based on a biologically inspired algorithm, efficiently optimizes chemical systems. It demonstrates robust performance across various benchmarks, avoiding local minima for better global solutions in automated experimentation.

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Area of Science:

  • Computational chemistry
  • Chemical process optimization
  • Algorithm development

Background:

  • Chemical systems and processes require efficient optimization methods.
  • Existing algorithms often need numerous experiments and can converge on local minima.
  • Complexity in chemical systems necessitates advanced experimental design algorithms.

Purpose of the Study:

  • To introduce the Paddy software package and its underlying Paddy field algorithm.
  • To benchmark Paddy against diverse optimization approaches for chemical and mathematical tasks.
  • To evaluate Paddy's ability to optimize objectives, sample parameter space, and avoid local optima.

Main Methods:

  • Development of the Paddy software package utilizing the biologically inspired Paddy field algorithm.
  • Benchmarking Paddy against Tree of Parzen Estimator (Hyperopt), Bayesian optimization (Ax framework), and EvoTorch algorithms.
  • Testing performance on mathematical tasks (bimodal distribution, function interpolation) and chemical tasks (ANN hyperparameter optimization, molecule generation, experimental planning).

Main Results:

  • Paddy demonstrated robust and versatile performance across all tested optimization benchmarks.
  • Paddy maintained strong performance compared to other algorithms with varying results.
  • The algorithm effectively avoided early convergence and bypassed local optima, searching for global solutions.

Conclusions:

  • Paddy offers a facile, versatile, robust, and open-source toolkit for chemical problem-solving.
  • The software is well-suited for automated experimentation, prioritizing exploratory sampling.
  • Paddy's resistance to early convergence aids in identifying optimal solutions for complex chemical systems.