{"repo":"deepmodeling/dpti","free":true,"listed":false,"github":"https://github.com/deepmodeling/dpti","clone":"git clone https://github.com/deepmodeling/dpti.git","description":"A Python Package to Automate Thermodynamic Integration Calculations for Free Energy","language":"Python","stars":41,"topics":["atomistic-simulations","free-energy","molecular-dynamics","workflow","thermodynamic-integration"],"license":"LGPL-3.0","category":"workflow-automation","readme_excerpt":"dpti dpti is a Python package for automating thermodynamic integration (TI) workflows used to compute free energies and phase diagrams from molecular dynamics simulations. The package generates LAMMPS input files, submits or runs the corresponding MD tasks through dpdispatcher , post-processes simulation outputs, evaluates Helmholtz and Gibbs free energies, estimates statistical and numerical integration errors, and propagates coexistence points into phase boundaries. Features - Equilibration tasks in NPT and NVT ensembles. - Hamiltonian thermodynamic integration (HTI) for atomic solids, atomic liquids, ice, and liquid water. - Temperature and pressure thermodynamic integration (TTI and pTI) for propagating free energies along isobars and isotherms. - Gibbs-Duhem integration (GDI) for propagating phase boundaries from coexistence points. - Adaptive refinement of HTI and TI grids based on integration-error estimates. - Local and HPC execution through dpdispatcher . Installation Install the released package with pip : For development, install from a local checkout: Check the installation with: dpti prepares and post-processes MD tasks. To run the generated simulations, you also need a working LAMMPS executable and the force-field backend required by your input files, such as DeePMD-kit for Deep Potential models. Basic Workflow A typical phase-diagram calculation follows five stages. 1. Run an NPT equilibration to determine the average cell at a chosen pressure and temperature. ","default_branch":null,"files":null,"tree":[],"storefront":"/r/deepmodeling","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/deepmodeling/dpti/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}