Machine learning-guided synthesis of advanced inorganic materials

TL;DR

This paper demonstrates how machine learning can effectively optimize inorganic material synthesis processes, reducing trials and costs while improving success rates and material properties, thus accelerating materials development.

Contribution

The study introduces ML models for optimizing synthesis conditions in inorganic materials, including classification, regression, and adaptive models, showcasing their potential in experimental design.

Findings

ML models improve synthesis success rates

Adaptive models reduce experimental trials

ML accelerates inorganic material development

Abstract

Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods leads to high uncertainty, numerous trials and exorbitant cost. Recently, machine learning (ML) has demonstrated tremendous potential for material research. Here, we report the application of ML to optimize and accelerate material synthesis process in two representative multi-variable systems. A classification ML model on chemical vapor deposition-grown MoS2 is established, capable of optimizing the synthesis conditions to achieve higher success rate. While a regression model is constructed on the hydrothermal-synthesized carbon quantum dots, to enhance the process-related properties such as the photoluminescence quantum yield. Progressive adaptive model is further developed, aiming to involve ML at the beginning stage of new material synthesis. Optimization of the experimental outcome with minimized number of trials can be achieved with the effective feedback loops. This work serves as proof of concept revealing the feasibility and remarkable capability of ML to facilitate the synthesis of inorganic materials, and opens up a new window for accelerating material development.