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Machine learning-guided synthesis of advanced inorganic materials
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
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