CFN Virtual Colloquium

"Machine Learning for Characterizing and Synthesizing Materials"

Presented by Gerbrand Ceder, Department of Materials Science and Engineering, University of California at Berkeley. Division of Materials Science, Lawrence Berkeley National Laboratory

Thursday, June 3, 2021, 4:00 pm — ZoomGov Meeting - see below for link

While machine learning holds promise in materials research it is currently limited to problems for which large datasets exist. I will discuss two applications where we have successfully used machine learning (ML). Phase identification in X-ray diffraction is particularly well suited for ML as simulated diffraction patterns can be used to generate training data, providing essentially infinite training data. I will show that convolutional neural networks, combined with statistical probability trees, can outperform traditional commercial software in identifying phases in single-phase and multi-phase diffraction patterns.
In a second application, I will show how ML can be used to extract data and its associated meaning from millions of research papers in an automated way. Using this approach, we have generated the largest dataset on the synthesis of inorganic materials. Such datasets are critically needed to start using ML on difficult problems in materials science.

Hosted by: Deyu Lu

Videoconference Instructions

Please click the link below to join the webinar: https://bnl.zoomgov.com/j/1610998573?pwd=b0RpVGZZbkR6TGpaSitaSHRqYjlIQT09 Passcode: 349211 Or iPhone one-tap : US: +16692545252,,1610998573#,,,,,,0#,,349211# or +16468287666,,1610998573#,,,,,,0#,,349211# Or Telephone: Dial(for higher quality, dial a number based on your current location): US: +1 669 254 5252 or +1 646 828 7666 Webinar ID: 161 099 8573 Passcode: 349211 International numbers available: https://bnl.zoomgov.com/u/a9HGRFnQt Or an H.323/SIP room system: H.323: 161.199.138.10 (US West) or 161.199.136.10 (US East) Meeting ID: 161 099 8573 Passcode: 349211 SIP: 1610998573@sip.zoomgov.com Passcode: 349211

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