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GASpy
amptorch
AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorchvasp-interactive
finetuna
Active Learning for Machine Learning Potentialsuncertainty_benchmarking
Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.wherewulff
WhereWulff: A semi-autonomous workflow for systematic catalyst surface reactivity under reaction conditionsmath-methods-chemical-engineering
Course notes for the CMU course 06-262 Math Methods of Chemical Engineering (ODE's, linear algebra, PDEs, stats) in the form of jupyter notebooksGASpy_manuscript
catgym
Surface segregation using Deep Reinforcement Learningcharge-density-models
Cleavage_Energy_Manuscript
ml_catalysis_tutorials
cluster_mlp
A Genetic algorithm + active learning framework to identify the optimal metallic nanoclusters for a given number of atomsF22-06-325
tmQM_wB97MV
Code for "Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Dataset"catlas
files for dask parallelization of OCP energy predictions.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.OfflineAL-for-MLPs-manuscript
Physics Enabled Convergence of Offline Active Learning with Machine Learning PotentialsGASpy_regressions
Regressions performed on databases created by GASpy. It is intended to be used as a submodule of GASpy.s19-06262
numerical notes for 06-262 at CMU (spring 19)active_motif_ORR
active motif based screening to discover active, selective and stable ORR catalysts for H2O2 productioncatalyst-acquisitions
Acquisition functions for density function theory (DFT) simulations for catalyst screeningnitrate
Repository for tools and code for water purification (NO3 RR)Love Open Source and this site? Check out how you can help us