Low-Latency Noise Mitigation Techniques in Gravitational-Wave Detector Data Using Auxiliary Sensor Information
Explore low-latency noise mitigation techniques in gravitational-wave detectors, focusing on automated methods to enhance sensitivity and enable rapid multi-messenger follow-up of astrophysical events.
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.
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.
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.
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.
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.
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.
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.
Error Bounds for Properties in Planewave Electronic Structure Calculations
Explore error bounds in planewave electronic structure calculations, focusing on ground state density matrix and interatomic forces estimation. Learn efficient approximation methods and see numerical results for materials systems.
Math-Chimie: Developing Approaches for Predicting New Superconductors
Explore innovative approaches for predicting superconductors, combining mathematics and chemistry to analyze electron density, localization, and high-pressure materials for groundbreaking discoveries in quantum mechanics.
Convergence of the Planewave Approximations for Quantum Incommensurate Systems
Explore quantum incommensurate systems through rigorous analysis of Schrödinger operators' spectrum distribution, focusing on density of states characterization and planewave approximation methods with novel energy cutoffs.
Generating Reference Data and Controlling Accuracy in DFT and Hybrid DFT Simulations
Explore advanced techniques in DFT simulations, including all-electron approaches, pseudopotential validation, and hybrid DFT acceleration methods for improved accuracy and efficiency in quantum mechanics calculations.
Fast Algorithms for FCI Excited States - IPAM at UCLA
Fast algorithms for electronic structure excited-state calculations in configuration interaction framework, focusing on global convergence, linear convergence rates, and acceleration techniques for improved efficiency.
Finite-Size Error and Its Correction in Energy Calculations for Periodic Systems
Explore finite-size errors in periodic systems, focusing on Hartree-Fock and MP2 theories. Learn about convergence rates, Madelung-constant correction, and the staggered mesh method for improved calculations.
Large Scale Hybrid DFT Functionals - Fast Algorithms and Finite-Size Effects - IPAM at UCLA
Explore accelerating DFT calculations with hybrid functionals and finite-size effects in Hartree-Fock calculations for periodic systems in this advanced mathematics lecture.
Low Rank Approximation in Electron Excitation Calculations - IPAM at UCLA
Explore low rank approximation techniques for efficient electron excitation calculations, focusing on the Interpolative Separable Density Fitting method and its applications in quantum mechanics.
Localization Methods - Perspectives on Initialization and Optimization - IPAM at UCLA
Explore Wannier localization in quantum mechanics, its mathematical formulation, challenges, and new approaches for efficient orbital localization in condensed matter systems.
Quantum Dynamics from Classical Trajectories - IPAM at UCLA
Explore semiclassical approximations in quantum mechanics, focusing on a novel approach for complex chemical systems and addressing challenges in path space sampling and prefactor evaluation.
Methods and Software for Electron-Phonon Physics - IPAM at UCLA
Explore electron-phonon physics with advanced methods and software. Learn about spectral density functions, Green's functions, and polarons in materials, with real-world examples and computational challenges.
Large-Coupling Strength Expansion in DFT and Hartree-Fock Adiabatic Connections
Explore large-coupling strength expansions in DFT and Hartree-Fock adiabatic connections, examining their applications in quantum chemistry and electronic structure theory.
Systematically Improvable Models from Alchemical Perturbations - IPAM UCLA
Explore systematic improvements in quantum models using alchemical perturbations, connecting chemical spaces and simplifying material design through novel approaches and fundamental constraints.