exa-PD: A scalable high-performance workflow for multi-element phase diagram construction

2026-07-16

AI Summary

Constructing multicomponent phase diagrams is hindered by substantial computational demands and the complexity of free energy sampling. This work proposes a highly parallelized workflow that simultaneously samples multiple phases across a fine temperature–composition grid, integrating molecular dynamics (via LAMMPS) and Monte Carlo methods to compute free energies, and coupling these with PyCalphad for CALPHAD-based thermodynamic modeling. Built upon the Parsl workflow engine, the approach enables strongly scalable scheduling of large-scale MD/MC tasks, significantly enhancing both the efficiency and accuracy of phase diagram construction in high-performance computing environments. The method demonstrates robust performance and excellent scalability when applied to high-dimensional multicomponent systems.

Abstract

Exa-PD is a highly parallelizable workflow designed for the construction of multi-element phase diagrams (PDs). It uses standard sampling techniques, molecular dynamics (MD) and Monte Carlo (MC) as implemented in the LAMMPS package, to simultaneously sample multiple phases over a fine temperature-composition mesh for free-energy calculations. Parsl serves as the global workflow engine, coordinating large ensembles of MD and MC tasks to achieve massive parallelization with strong scalability. The resulting free energies of liquid and solid phases are then fed to CALPHAD modeling via the PyCalphad package to construct multi-element PDs.

Problem

• phase diagram
• multi-element
• free-energy calculation
• high-performance computing
• scalability

Innovation

• exa-PD
• multi-element phase diagrams
• massive parallelization
• free-energy calculation
• CALPHAD