Uncertainty Quantification of Quantum Chemical Methods - IPAM at UCLA
Institute for Pure & Applied Mathematics (IPAM) via YouTube
Overview
Syllabus
Introduction
Outline
Context
Automation
Camoton
Example
Network
Data
Kinetic modeling
Uncertainty quantification
Transferability
General Remarks
The Most Fundamental Problem
Discretization Errors
Individual Absolute Errors
Error Compensation
Benchmark Results
Continuous Benchmarking
Knowledge Based Error Estimation
Examples
Gaussian Processes
Reaction Network Exploration
Soap Kernel
Standard Database
Multiconfiguration
DMRG
Entanglement Measures
Selection Algorithm
User Interface
Reference Data
D3 Correction
Training Data
Technical Details
Data Machine Learning
Automated Workflow
Related Work
Conclusion
References
Taught by
Institute for Pure & Applied Mathematics (IPAM)