Webinar: Machine-Learned Potentials: DFT-Level Accuracy for Real-World R&D
- Katherine Hollingsworth
- 1 day ago
- 2 min read
Recent advancements in machine-learned potentials (MLPs) allow researchers to perform simulations that access nanosecond-scale dynamics with atomistic models that describe domain sizes of the order of several nanometers. Such simulations can now be performed with reasonable computational resources whereby the interatomic interactions are described with the accuracy of first-principles methods such as standard density functional methods or beyond.
While MLPs are the key to modeling real materials, integrating them into existing R&D workflows remains a significant challenge for many researchers. In response to this, the MedeA 3.12 multiscale simulation environment delivers a comprehensive, end-to-end machine learning workflow for materials modeling.
This webinar will share several case studies showcasing how the integrated machine-learning workflow in MedeA 3.12 can accelerate your research, streamline the adoption of MLPs, and move beyond the limitations of standard first-principles methods.
Learn how to:
Train machine learned potentials for accurate atomistic simulations
Deploy MLPs within an integrated multiscale modeling environment
Bridge first-principles accuracy with large-scale, long-timescale simulations
Streamline the adoption of MLPs into existing R&D workflows
Accelerate materials research using an end-to-end machine learning workflow in MedeA 3.12
Who should attend:
Materials scientists and computational chemists in industry and academia
R&D engineers working in molecular modeling, simulation, or design
Researchers interested in machine learning applications in materials science
Users of density functional theory (DFT) seeking scalable alternatives or extensions
Teams looking to accelerate materials discovery and simulation workflows
Webinar Sessions
Tuesday, May19th:
Live Q&A 10:00 AM PST (USA) 1:00 PM EST (USA) 19:00 CET (EUROPE)
Wednesday, May 20th:
Live Q&A 07:00 AM PST (USA) 10:00 AM EST (USA) 16:00 CET (EUROPE) 20:30 IST (INDIA)
Thursday, May 21st:
Live Q&A 08:00 CET (EUROPE) 12:30 IST (INDIA) 15:00 CST (CHINA) 16:00 JST (JAPAN)
Note: Please select a day and time that best fits your schedule. This one-hour webinar is offered multiple times to accommodate participants worldwide. Note: All of our scientific webinars are free to attend.
Dr. Cheng-Wei Lee
Dr. Cheng-Wei Lee, is passionate about enabling scientific and engineering breakthroughs through computational methods. He recently joined the team at Materials Design, Inc. as a Support and Application Scientist, bringing with him deep expertise in multiscale materials simulations, including Density Functional Theory (DFT), Molecular Dynamics (MD), and Machine Learning (ML).
Before joining Materials Design Inc., Dr. Lee was a Postdoctoral Researcher at the Colorado School of Mines. There, he specialized in computational discovery and engineering of wide bandgap materials for applications in power electronics, optoelectronics, ferroelectrics, and batteries. He obtained his Ph.D. in Materials Science and Engineering from the University of Illinois at Urbana-Champaign, where his research focused on non-adiabatic electron-ion dynamics in semiconductors and their responses to ionizing ion beams.
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