Explore the cutting-edge field of stochastic sampling in dense matter through this 58-minute seminar by Chris Pickard. Delve into structure prediction techniques, smooth interactions, and landscapes in ab initio random structure searching. Examine the intricacies of gamma boron and the debate between data-driven and first principles approaches. Investigate superconducting hydrides and binary hydrides, and learn about manifold learning through stochastic hyperspatial embedding and projection. Discover new directions in binary chain research, covalent bonds, and composition space. Gain valuable insights into the latest advancements in condensed matter physics and materials science.
Overview
Syllabus
Intro
Stochastic sampling of dense matter
Structure Prediction
Smooth interactions
Smooth Landscapes
Ab Initio Random Structure Searching
Gamma Boron
Data Driven or First Principles?
Being Sensible
Condensed matter!
Potentials
Superconducting Hydrides
Superconducting Binary Hydrides
Manifold learning Stochastic hyperspatial embedding and projection
New directions
Binary chain
Covalent bonds
Composition Space
Conclusion
Taught by
Inside Livermore Lab
