This summer school will provide an overview of current developments in data-driven materials science involving Machine Learning and modern numerical techniques and will offer a platform for discussions about future perspectives.
Materials Genome Engineering aims at realizing the transition to a new paradigm in materials development from a traditional "trial and error" mode to a "rationally designed experiments" mode. In this highly promising approach, thorough and reliable theoretical prediction and high-throughput screening are followed by experimental verification and technological implementation. Complementary efforts and the seamless integration of theory, computation and experiment, will result in a remarkable acceleration of the pace of new materials discovery, design and deployment. To fully take advantage of its potential, this novel scheme has to rely on cross-innovation and convergence of various scientific fields such as materials science, computer science, physics, chemistry as well as information and data science.
This online summer school targets Master students, Ph.D. students and (early-stage) Postdocs interested in or working on topics related to computational and experimental high-throughput approaches, machine learning and data-driven materials science.
Please refer to the Summer School homepage:
The registration is open until June 30, 2020 and participation is free of charge.
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