ulissigroup/uncertainty_benchmarking

Stars
37
Rank 706,830 (Top 15 %)
Language
Jupyter Notebook
Created almost 5 years ago
Updated about 3 years ago

ulissigroup/uncertainty_benchmarking

ulissigroup

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Various code/notebooks to benchmark different ways we could estimate uncertainty in ML predictions.

GASpy

amptorch

AMPtorch: Atomistic Machine Learning Package (AMP) - PyTorch

vasp-interactive

Jupyter Notebook

finetuna

Active Learning for Machine Learning Potentials

wherewulff

WhereWulff: A semi-autonomous workflow for systematic catalyst surface reactivity under reaction conditions

math-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 notebooks

Jupyter Notebook

GASpy_manuscript

Jupyter Notebook

catgym

Surface segregation using Deep Reinforcement Learning

charge-density-models

Cleavage_Energy_Manuscript

Jupyter Notebook

ml_catalysis_tutorials

Jupyter Notebook

cluster_mlp

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

Jupyter Notebook

F22-06-325

Jupyter Notebook

tmQM_wB97MV

Code for "Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Dataset"

Jupyter Notebook

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 Potentials

Jupyter Notebook

GASpy_regressions

Regressions performed on databases created by GASpy. It is intended to be used as a submodule of GASpy.

Jupyter Notebook

s19-06262

numerical notes for 06-262 at CMU (spring 19)

Jupyter Notebook

active_motif_ORR

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

Jupyter Notebook

catalyst-acquisitions

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

Jupyter Notebook

DOE_HER

Jupyter Notebook

nitrate

Repository for tools and code for water purification (NO3 RR)

Jupyter Notebook

Open-Catalyst-Dataset

Workflow for creating and analyzing the Open Catalyst Dataset