Materials chemistry has been one of the fastest growing areas in chemistry in the past decade. This is driven by materials discovery for renewable energy, clean environment, and biomedical applications. Understanding structure-property relationships is fundamental to the chemistry of materials and key to realizing materials functions. Advances in theoretical understanding, algorithms, computational power, big data, artificial intelligence and machine learning are enabling computational tools to play an increasing role in materials discovery, development and optimization. For example, recently developed data mining techniques and machine learning approaches enable the virtual synthesis of novel materials, with their properties being predicted on a computer before ever being synthesized in a laboratory.

This workshop aims to bring together computational scientists working on focused topics of materials chemistry to exchange ideas and to stimulate discussion. The 2021 workshop will concentrate on machine learning in materials chemistry: from data to descriptors, predictions and insights; from materials informatics and high-throughput screening to virtual synthesis; from neural network potentials to exploration of potential-energy surface and structure prediction; from catalysis to energy storage. This workshop will provide a unique opportunity for the participants to broaden their view and deepen their understanding of machine learning and its broader impacts in materials and chemistry.