Designs of Molecules, Materials, and Processes Based on Interpretation and Direct Inverse Analysis of Machine Learning Models

Abstract

  Data analysis and machine learning are being used in the research, development and production of functional materials such as crystal materials, enabling more efficient design of molecules, materials and processes and process control. After constructing a nonlinear machine learning model, new molecules, materials and processes can be proposed with direct inverse analysis of the model to perform molecules, materials and processes directly from target values of properties and activities. In addition, the model can be interpreted using genetic algorithm-based partial least squares regression with only the first component, cross-validated permutation feature importance, and local slope of model prediction to discuss important parameters in experiments, manufacturing, and processes, and to advance understanding of phenomena and clarification of mechanisms in molecules and materials.