Skip to content

PESMaker Project Brief

Name

PESMaker: Potential Energy Surface Maker.

Working paper title:

PESMaker: a lightweight foundation-potential-assisted workflow for
application-oriented machine-learning potential dataset generation

Core idea

PESMaker starts from user-provided atomistic structures and turns them into DFT-labeled datasets and trained machine-learned interatomic potentials.

The intended path is:

known material or task
  -> targeted structure generation and sampling
  -> DFT SCF labeling
  -> dataset quality checks
  -> NEP or MACE training
  -> final potential

Target users

  • battery materials researchers;
  • solid electrolyte researchers;
  • thermal transport researchers;
  • alloy and defect researchers;
  • surface and catalysis researchers;
  • users who already have VASP/GPUMD/LAMMPS scripts but need an automated, reproducible workflow.

Differentiation from autoplex

autoplex is a strong reference and should be cited. Its strongest identity is random-structure-search-driven potential-landscape exploration, especially with AIRSS/buildcell and heavy workflow infrastructure.

PESMaker should be different:

  • user-provided structures first;
  • physical application recipes first;
  • foundation potentials for affordable sampling before DFT labeling;
  • NEP/GPUMD and MACE as first-class training targets;
  • lightweight local execution first, with Slurm/PBS support;
  • database services optional rather than mandatory.

First development target

MVP 1 should not try to implement all planned science modules. It should support:

  1. read one or more initial structures;
  2. generate supercells and optional perturbations;
  3. create SCF calculation folders from a user template;
  4. write a manifest of all generated calculation tasks;
  5. provide a CLI that can validate configs and prepare workflow stages.

After this is stable, add job submission, VASP parsing, extxyz export, and NEP training.