Aidan Thompson - LAMMPS simulation: physics models, machine-learning potentials, exascale computing Published 2023-03-27 Download video MP4 360p Download video MP4 720p Recommendations 58:26 Lin Lin - Interacting models for twisted bilayer graphene: quantum chemistry approach - IPAM at UCLA 58:38 Gabor Csányi - Machine learning potentials: from polynomials to message passing networks 1:12:03 Steve Brunton: "Introduction to Fluid Mechanics" 1:27:06 Daniel Schwalbe Koda: Machine learning for interatomic potentials 58:02 Steve Brunton - Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics 2:03:33 LAMMPS Workshop 2019 - Week 1 1:30:15 Natasha Jaques PhD Thesis Defense 50:26 Tom Goldstein: "What do neural loss surfaces look like?" 38:16 Lecture: Mathematics of Big Data and Machine Learning 46:32 Input script for LAMMPS 1 59:50 Leslie Lamport: Thinking Above the Code 1:16:34 Steve Brunton: "Dynamical Systems (Part 2/2)" 1:14:48 The Future of Mathematics? 1:09:03 The Einstein Lecture: The Quantum Computing Revolution 1:15:20 Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018) 21:20 Does -1/12 Protect Us From Infinity? - Numberphile 58:12 MIT Introduction to Deep Learning | 6.S191 49:28 Marc Niethammer: "Deep Learning for Medical Image Registration" 1:16:17 Terence Tao: The Cosmic Distance Ladder, UCLA 1:06:11 1. Introduction to Computational and Systems Biology Similar videos 56:40 Distinguished Seminar Series in Computational Science and Engineering: Aidan Thompson, Feb 25, 2021 1:22:55 MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields 50:39 Michele Ceriotti - Machine learning for atomic-scale modeling - potentials and beyond - IPAM at UCLA 57:56 Boris Kozinsky - Uncertainty-aware machine learning models of many-body atomic interactions 44:09 Stan Moore - Optimizing GPU Performance: Case Study Using Chain Benchmark in LAMMPS 1:09:17 Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics | Albert Musaelian 59:46 Ivan Novikov - The MLIP package: Moment tensor potentials with active learning 54:49 Materials Project Seminars – Ju Li, "A Universal Empirical Interatomic Potential" 6:33:27 LAMMPS Workshop 2023 Day 3 2:06:09 ARCHER2 Introduction to LAMMPS Session 1 43:50 Vasily Bulatov - What are we going to do with data generated in exascale simulations? - IPAM at UCLA 56:23 Ralf Drautz - From electrons to the simulation of materials - IPAM at UCLA 24:58 Science | Molecular Dynamics Simulation | The new class of Moment Tensor Potentials 00:18 Two Fullerene C60 molecules Ver.1 (Molecular Dynamics) 47:55 Christoph Ortner - Modelling Atomic Properties with the Atomic Cluster Expansion - IPAM at UCLA 39:30 ES21 Addressing Errors in AIMD Predictions through Machine Learning 59:20 Nataliya Lopanitsyna - Machine learning potential recipes on the example of metal alloys More results