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Machine learning for understanding dynamics
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Classroom Contents
Uncertainty-Aware Machine Learning Models of Many-Body Atomic Interactions
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- 1 Intro
- 2 Machine learning for understanding dynamics
- 3 Atomistic modeling methods evolution
- 4 Limitations of Current ML Functionals
- 5 Nonlocal Features for Exchange Energy
- 6 Model and Training Details
- 7 What is the derivative discontinuity?
- 8 Dynamic problems require dynamic solutions
- 9 Computing forces for molecular dynamics
- 10 Symmetry-Aware Machine Learning Force Fields
- 11 E(3) equivariance allows to capture 3D geometry
- 12 NequIP: E(3)-equivariant Neural Interatomic Potentials
- 13 Long MD simulation stability
- 14 Allegro's two-track architecture
- 15 Allegro accuracy and scalability
- 16 Allegro: Large-scale dynamics
- 17 Selecting optimal training sets
- 18 FLARE Bayesian Force Fields
- 19 FLARE on the fly active learning
- 20 Phase transitions in 2D stanene
- 21 ML force fields for transition metals
- 22 Micron-scale heterogeneous reaction dynamics
- 23 Evolution of Li anode-electrolyte interface
