Machine-Learning Potentials: The Accurate, the Fast, and the Applied

  • TH Department Online Seminar
  • Date: Mar 24, 2022
  • Time: 02:00 PM (Local Time Germany)
  • Speaker: Dr. Matthias Rupp
  • Universität Konstanz, Computer Science, Bioinformatics and Information Mining, Konstanz, DE
  • Location:
  • Room: Meeting ID: 947 8192 4316 | Passcode: 015558
  • Host: TH Department
Machine-Learning Potentials: The Accurate, the Fast, and the Applied
Data-driven algorithms ("machine learning") are increasingly used in science and engineering for analysis, prediction, and control, enabling new insights and applications. A promising example are first-principles simulations of the dynamics of atomistic systems.

These are essential for physics, chemistry, and materials science, but severely limited by their computational cost. Machine-learning potentials (MLPs) are data-driven surrogate models that accurately interpolate between a few selected reference calculations at orders of magnitude lower computational cost, enabling accurate dynamics simulations at otherwise inaccessible system sizes and time scales. I will present "ultra-fast potentials" based on splines, prediction of thermal transport in materials via the Green-Kubo formalism and deep network MLPs, and outline first steps towards MLPs based on quantum Monte Carlo calculations. In doing so, I will discuss the role of domain knowledge and trade-offs involved in the design of MLPs.

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