martinmejia Goto Github PK

-physics-driven-discovery-and-optimization-of-hybrid-perovskite-films

02-sql

2_day_workweek

batteryml

baybe

A Bayesian Back End for Design of Experiments

bofire

Experimental design and (multi-objective) bayesian optimization.

datawig

Imputation of missing values in tables.

deep_learning

deep_learning_topics

deepchem

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

denn

Unsupervised neural networks for solving differential equations

dsi-workshop-repo

e3nn

A modular framework for neural networks with Euclidean symmetry

estimation_machine_learning_testing

gauche

A Library for Gaussian Processes in Chemistry

geofs-liveryselector

Unified livery handler addon for GeoFS.

gphsp

Code to build a probabilistic predictive model for HSP

gpmol

honegumi

Honegumi (骨組み) is an interactive "skeleton code" generator for API tutorials with a focus on optimization packages.

keras-molecules

Autoencoder network for learning a continuous representation of molecular structures.

machine-learning-meets-pka

materials_discovery

nequip

NequIP is a code for building E(3)-equivariant interatomic potentials

openfold

Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2

peer_benchmark

PEER Benchmark, appear at NeurIPS 2022 Dataset and Benchmark Track (https://arxiv.org/abs/2206.02096)

production

rdeditor

Simple RDKit molecule editor GUI using PySide

seirsplus

Models of SEIRS epidemic dynamics with extensions, including network-structured populations, testing, contact tracing, and social distancing.

transfergpbo

This will be the companion code for the benchmarking study reported in the paper Transfer Learning with Gaussian Processes for Bayesian Optimization accepted for publication at AISTATS 2022

uoft_temp