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Deadline June 13, 2019, 11:59 pm (ET)
Symposium MT02—Closing the Loop—Using Machine Learning in High-Throughput Discovery of New Materials
Automation of high-throughput experiments and computations has resulted in the creation of materials data at a scale hitherto unknown. Meanwhile, 7 years of efforts within the Materials Genome Initiative have provided tremendous advances in materials data science, empowering researchers to analyze and extract knowledge from data at unprecedented rates. This has created a field at the boundary of materials science and computer science in which artificial intelligence (A.I.) is being used to drive materials research. In fact, with the prevalence of open-source machine learning platforms, a materials researcher can quickly and effectively apply A.I. to their research. Initial efforts have already led to reports of ten to thousand-fold acceleration in the rate of materials discovery, especially when these approaches are augmented with high-throughput approaches. However, most of the general materials community lacks formal computer science training. This leads to concerns about the uniformed application of A.I. and hinders community acceptance of these powerful tools potentially resulting in a slower discovery of important materials. This symposium will provide a platform for machine learning materials science early adopters, materials data platform creators, and cutting-edge computer scientists to present their recent work exploring the interface between computer science and materials science.