PyVCHAM documentation

PyVCHAM is an open-source Python package designed to construct vibronic coupling (VC) Hamiltonians for complex molecular systems where the Born-Oppenheimer approximation fails. Leveraging machine learning techniques, specifically automatic differentiation via TensorFlow, PyVCHAM optimizes Hamiltonian parameters efficiently and accurately. It is built to integrate seamlessly with quantum chemistry tools, enabling high-dimensional nonadiabatic dynamics simulations with enhanced flexibility and precision.

Features

• Automatic Differentiation: Uses TensorFlow to compute precise gradients of cost functions, improving optimization efficiency.

• Quantum Chemistry Integration: Interfaces with tools such as OpenMolcas and ADC-connect for ab initio data input.

• Standardized JSON Format: Proposes a structured JSON format for storing VC Hamiltonians, enhancing interoperability with dynamics software like the Heidelberg MCTDH package.

• Modular Design: Supports custom diabatic or coupling functions to adapt to specific research needs.

• Robust Optimization: Employs the Adam algorithm for parameter fitting, with support for linear and higher-order vibronic coupling terms.

• Symmetry Handling: Incorporates symmetry constraints to ensure physically meaningful coupling terms.

Contents:

• Home
• Installation
  • Requirements
  • Installation from source
  • Installation via pip
• Examples
  • \(\text{Preparing } \textit{ab-initio} \text{ data}\)
  • \(\text{C}_2\text{H}_2\text{F}_2 \text{ Model}\)
  • \(\text{FCCH Model}\)
  • \(\text{H}_2\text{CO} \text{ Model}\)
  • \(\text{Li}_3 \text{ Model}\)
  • \(\text{Model 2 molecules}\)
• License