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active_motif_orr

active motif based screening to discover active, selective and stable ORR catalysts for H2O2 production

aflow

Python API wrapping the AFLUX API language for AFLOW library.

amptorch

AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch

argoflow

Deploying Kubeflow with Argo CD

asap

ASAP is a package that can quickly analyze and visualize datasets of crystal or molecular structures.

ase-espresso

ase interface for Quantum Espresso

camd

Agent-based sequential learning software for materials discovery

cas

catalyst-acquisitions

Acquisition functions for density function theory (DFT) simulations for catalyst screening

catalystsre

catapalt

CATalyst Analysis Package At Large Throughput: Dev package for high-throughput analysis tools

catgym

Surface segregation using Deep Reinforcement Learning

catkit

General purpose tools for high-throughput catalysis

catlas

files for dask parallelization of OCP energy predictions.

catlas-paper-manuscript

cgcnn

Crystal graph convolutional neural networks for predicting material properties.

charge-density-models

cleavage_energy_manuscript

cluster_mlp

A Genetic algorithm + active learning framework to identify the optimal metallic nanoclusters for a given number of atoms

doe_her

doeionomers

dogss

espresso_tools

The Quantum Simulation Group (QSG) at Lawrence Livermore National Lab (LLNL) has developed various tools to manage their [Quatum Espresso](https://www.quantum-espresso.org/) calculations. The Ulissi group has ported, open-sourced, and managed these tools to Github on their behalf.

f22-06-325

finetuna

Active Learning for Machine Learning Potentials

finetuna-2023-manuscript

finetuna_manuscript

flamyngo

Flask frontend for MongoDB

gaspy

gaspy_feedback

This is a GASpy submodule that is meant to work with GASpy_regressions to determine what GASpy simulations to run next.