Machine Learning for Materials Science (MLMS)

Abstract

Artificial intelligence and machine learning are being increasingly used in scientific domains such as computational fluid dynamics and chemistry. Particularly notable is a recently renewed interest in solving partial differential equations using machine learning models, especially deep neural networks, as partial differential equations arise in many scientific problems of interest. Within materials science literature, there has been a surge in publications on AI-enabled materials discovery, in the last five years. Despite this, the interaction between machine learning researchers and materials scientists (especially, scientists working on structural materials, their microstructures, textures and so on) has been very sparse. On the other hand, AI/ML techniques can clearly be integrated into materials design frameworks (e.g., MGI efforts) to support accelerated materials development, novel simulation methodologies and advanced data analytics. Hence there is an immediate need for exchange of ideas and collaborations between machine learning and materials science communities. We believe a workshop dedicated to this theme would be well- suited to foster such collaborations. The aim of this workshop is to bring together the computer science and materials science communities and foster deeper collaborations between the two to accelerate the adoption of AI/ML in materials science. We hope and envision this workshop to facilitate in building a community of researchers in this interdisciplinar area in the years ahead.