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sciai-lab/
structures25-nuclear-gradients

Name: structures25-nuclear-gradients
Availability: InStock

pytorch-lightning

chemistry

density-functional-theory

graph-neural-network

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Analytic nuclear gradients for machine-learned orbital-free DFT

This repository provides four SCIAI-DFT models for density optimization and geometry optimization with machine-learned orbital-free density functional theory (ML-OFDFT).

Directory	Model	Description
`Graphormer/`	Graphormer	Graphormer base model
`Graphormer-NGL/`	Graphormer NGL	Graphormer fine-tuned with the nuclear gradient loss
`eSEN/`	eSEN	eSEN base model
`eSEN-NGL/`	eSEN NGL	eSEN fine-tuned with the nuclear gradient loss

Each directory contains safetensors weights, a self-contained model.yaml inference configuration, and the original hparams.yaml and hparams_resolved.yaml files. SHA256SUMS records the checksums of all four weight files.

Usage

Install the code from sciai-lab/structures25-nuclear-gradients, then load a checkpoint with the project helper:

python

from pathlib import Path

from huggingface_hub import snapshot_download

from mldft.utils.instantiators import instantiate_model

repo = Path(snapshot_download("sciai-lab/structures25-nuclear-gradients"))
model = instantiate_model(repo / "eSEN-NGL" / "eSEN-NGL.safetensors", device="cpu")

The resolved hyperparameter files provide the model-specific data transforms and density-optimization settings needed by the project evaluation scripts.

Scope and limitations

These models predict the learned kinetic-plus-exchange-correlation contribution from atom-centered density coefficients and molecular geometry. They are intended for ML-OFDFT calculations on neutral organic molecules in the chemical regime represented by the training data. They are not general-purpose interatomic potentials. Strongly distorted, reactive, dissociative, or connectivity-changing structures remain challenging.