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Materials Discovery using Computational and Data-Driven Approaches

Novel materials discovery is a key to addressing many challenges in energy, climate change, and future sustainability. Usual procedure of finding innovative materials based mainly on experiments, however, can take far too long due to a vast and discrete search space, and thus accelerating this process by orders of magnitude using scalable computations would significantly reduce the time and cost of new discovery. In achieving this grand goal, density, functional first principles simulation offers a sweet spot between the prediction accuracy and feasibility. I will demonstrate some of the examples to discover new materials in energy storage and conversion applications using them, and also briefly describe some of our recent efforts to make density functional calculations more accurate and also scale favorably with system size. I will also talk about some of our initial efforts to use machine learning for chemical science that can contribute greatly to creating potential solutions to some materials problems.

Speaker: Yousung Jung, Graduate School of EEWS, KAIST

Monday, 07/30/18

Contact:

Website: Click to Visit

Cost:

Free

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Hildebrand Hall

UC Berkeley
Library
Berkeley, CA 94720