Institute for Pure & Applied Mathematics (IPAM) Courses

Machine Learning to Improve the Exchange and Correlation Functional in DFT

Explore machine learning approaches to improve exchange and correlation functionals in density functional theory, discussing physically informed methods and their advantages in quantum mechanics.

• YouTube
• 54 minutes
• On-Demand
• Free Video

Compact Representations of Excited States from QMC, as Data for Low-Energy Models

Explore compact wave function representations for excited states in quantum mechanics, focusing on variational Monte Carlo methods and their applications in effective model development.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Quantum Time Correlation Functions in an Open-Chain Path Integral Distribution

Explore quantum time correlation functions using open-chain path integrals, offering a new perspective on semiclassical approximations in computational chemistry with potential applications in rate theory and spectroscopy.

• YouTube
• 53 minutes
• On-Demand
• Free Video

Unified Understanding of E(3)-Equivariant Interatomic Potentials - Theory and Applications

Explore E(3)-equivariant interatomic potentials, covering theory, applications, and advancements in machine learning approaches for quantum mechanics and molecular dynamics simulations.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Approximate Matrix Eigenvalues, Subspace Iteration With Repeated Random Sparsification

Explore efficient matrix eigenvalue approximation using subspace iteration and random sparsification, with applications in quantum chemistry and high-dimensional problems.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Physically Inspired Machine Learning for Excited States - IPAM at UCLA

Explore machine learning applications in photochemistry, focusing on excited state predictions and light-matter interactions. Discover innovative techniques for overcoming data limitations and enabling accurate photodynamics simulations.

• YouTube
• 44 minutes
• On-Demand
• Free Video

High Accuracy Wavefunctions Using Deep-Learning-Based Variational Monte Carlo

Explore deep learning-based variational Monte Carlo for high-accuracy wavefunctions in computational chemistry, featuring novel architecture and improved methods for ground state energy calculations.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Conditional Probability Density Functional Theory - IPAM at UCLA

Explore conditional probability density functional theory for electronic structure calculations, addressing challenges in DFT and offering insights into ground-state correlations and warm dense matter simulations.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Neural-Network Wave Functions for Quantum Chemistry - IPAM at UCLA

Explore neural-network wave functions in quantum chemistry, covering variational quantum Monte Carlo, antisymmetric ansatzes, and applications to electronic and exciton-phonon Hamiltonians for improved accuracy in molecular modeling.

• YouTube
• 51 minutes
• On-Demand
• Free Video

Auxiliary Field Quantum Monte Carlo - AFQMC with Multi Slater Wavefunctions

Explore advanced quantum Monte Carlo techniques, focusing on AFQMC with multi-Slater wavefunctions and efficient exchange matrix evaluation for large-scale quantum systems.

• YouTube
• 46 minutes
• On-Demand
• Free Video

Machine Learning in Equation of State and Transport Modeling at Extreme Conditions

Explore machine learning applications in equation of state and transport modeling for extreme conditions, enhancing accuracy and efficiency in material science and astrophysics research.

• YouTube
• 49 minutes
• On-Demand
• Free Video

Stochastic Density Functional Theory - IPAM at UCLA

Explore stochastic density functional theory for efficient ground-state calculations in extended materials, including noise reduction techniques and applications in warm dense matter at elevated temperatures.

• YouTube
• 42 minutes
• On-Demand
• Free Video

Molecular Electron Densities via Machine Learning - IPAM at UCLA

Explore machine learning techniques for predicting molecular electron densities and energies, with applications in quantum mechanics and molecular dynamics simulations.

• YouTube
• 38 minutes
• On-Demand
• Free Video

Fermionic Neural-Network Quantum States - IPAM at UCLA

Explore fermionic neural-network quantum states, their efficient representations, and applications in quantum mechanics with Giuseppe Carleo's insightful presentation at IPAM's workshop.

• YouTube
• 43 minutes
• On-Demand
• Free Video

Auxiliary-Field Methods for Quantum Materials - IPAM at UCLA

Overview of auxiliary-field quantum Monte Carlo methods for simulating quantum materials, discussing recent advances in ab initio calculations, including correlated sampling and structural optimization.

• YouTube
• 49 minutes
• On-Demand
• Free Video