FEATURED — CONSTANT-POTENTIAL MLIP PIPELINE (PRIVATE LANL)

Three composable packages.

Together: generate, ground-truth, and learn electrified-interface physics for CP-MLMD.

Stage 1 — Structures

electrode-electrolyte_interface_builder

Private LANL Research Code

Building physically valid electrode-electrolyte structures for MLIP training is months of manual work most groups skip. I wrote a stage-validated pipeline that generates IrO₂/RuO₂ slabs across four facets, populates the full electric double layer with water, Na⁺, and ClO₄⁻, places OER/HER/CER adsorbates at chemically valid sites, and validates each. Input layer of the HIPPIE-NN pipeline.

PythonASEQuantum ESPRESSOVASP

Stage 2 — Ground Truth

qe-tools

Internal LANL Infrastructure — Release Pending IP Review

Daily friction in computational electrochemistry is in the plumbing: months of manual HPC setup, monitoring, and recovery. I built a Quantum ESPRESSO automation suite that compresses full OER campaigns to 2–3 weeks of unattended compute. It also adds operando XAS under ESM-RISM — the first such implementation in QE. Every LANL electrochemistry project ran on this toolkit.

PythonParslQuantum ESPRESSOSlurmESM-RISMXSpectra

Stage 3 — Model

hippynn-softfqeq

Private LANL Research Code — arXiv Preprint Forthcoming

Every charge-aware ML potential predicts a flat electrochemical potential across the electrode-electrolyte interface — an artifact of a single shared chemical potential. HIPPIE-NN doesn't. My HIPPYNN fork adds Soft-FQEq: per-fragment augmented-Lagrangian charge equilibration so the electric double layer emerges from training. Forces by autograd via Uzawa unroll. ~50,000× faster than DFT-MD.

PyTorchHIPPYNNPythonCUDASoft-FQEq