Upcoming Webinar
Machine-Learned Potentials: Surpassing the Limits of the Ab Initio World without Leaving it Behind
Optimizing the performance of engineering materials requires an ability to control their microstructure and predict their fundamental properties on engineering length and time scales. To this end, ever-increasing computational power and the rise of artificial intelligence have given rise to atomistic simulation capabilities for virtual experimentation with unprecedented accuracy, based on machine learned interatomic potentials (MLPs). #MLPs are uniquely enabling for high fidelity #mesoscale #material modeling by virtue of their basis in arbitrarily large training sets of ab initio results. Machine-learned potentials (MLPs) thus provide a unique means to extend the proven fidelity, reliability, and maturity of ab initio methods to the mesoscale where users may study the collective behavior of millions or more of atoms and sample millions of atomic configurations.
This webinar showcases the ease and versatility of the #MedeA software for unlocking the power of MLPs to address three sample problems of engineering importance. We demonstrate how MedeA enables users to expertly and efficiently:
Generate and apply MLPs to predict phase transitions
Simulate an interfacial #diffusion process
Model the kinetics of #nanoparticle impacts on surfaces
Webinar Sessions
Tuesday, August 1st: Live Q&A 10:00 AM PDT (USA) 1:00 PM EDT (USA) 19:00 CEST (EUROPE) Wednesday, August 2nd: Live Q&A 07:00 AM PDT (USA) 10:00 AM EDT (USA) 16:00 CEST (EUROPE) 19:30 IST (INDIA) Thursday, August 3rd: Live Q&A 08:00 CEST (EUROPE) 11:30 IST (INDIA) 14:00 CST (China) 15:00 JST (JAPAN)
*Recording and Slides
Registrations will also include a link to the recording and slides after the sessions end. Please choose a day and time that works for your schedule. The one hour webinar is repeated on various days and times to fit schedules worldwide.
Presenters
Dr. Volker Eyert
Volker Eyert, a Senior Scientist here at Materials Design. Volker received a PhD in theoretical physics from the Technical University of Darmstadt. After postdoc positions at the Max-Planck-Institute for Solid State Research in Stuttgart and the Hahn-Meitner-Institute in Berlin, he held a position as lecturer and assistant professor at Augsburg University. Volker developed an efficient Full-Potential Augmented Spherical Wave (ASW) code, which is based on density functional theory and gives access to the electronic, magnetic, optical, transport, and elastic properties of crystalline materials.
Dr. Xiaoli Liu
Dr. Xiaoli Liu is a support and applications scientist at Materials Design. She earned her PhD degree at Clarkson University, performing DFT calculations for a variety of materials. Hher academic background and practical experience currently are devoted to assisting MDI customers to derive maximum benefit from MedeA’s broad functionalities.
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