Machine learning-guided synthesis of advanced inorganic materials
This work addresses the challenge of accelerating inorganic materials development for researchers and industries by reducing experimental trials and costs, though it is incremental as it builds on existing ML applications in materials science.
The authors tackled the problem of synthesizing advanced inorganic materials with high uncertainty and cost by applying machine learning to optimize synthesis conditions in two multi-variable systems, achieving higher success rates and enhanced properties like photoluminescence quantum yield with minimized trials.
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.