T
able 2 | Publicly accessible learning resources and tools related to machine learning Name Description
URL General-purpose machine-learning frameworks Caret
Package for machine learning in R https://topepo.github.io/caret Deeplearning4j
Distributed deep learning for Java https://deeplearning4j.org H2O.ai
Machine-learning platform written in Java that can be imported as a Python or R library https://h2o.ai Keras
High-level neural-network API written in Python https://keras.io Mlpack
Scalable machine-learning library written in C++ https://mlpack.org Scikit-learn
Machine-learning and data-mining member of the scikit family of toolboxes built around the SciPy Python library http://scikit-learn.org Weka
Collection of machine-learning algorithms and tasks written in Java https://cs.waikato.ac.nz/ml/weka Machine-learning tools for molecules and materials Amp
Package to facilitate machine learning for atomistic calculations https://bitbucket.org/andrewpeterson/amp ANI
Neural-network potentials for organic molecules with Python interface https://github.com/isayev/ASE_ANI COMBO
Python library with emphasis on scalability and eciency https://github.com/tsudalab/combo DeepChem
Python library for deep learning of chemical systems https://deepchem.io GAP
Gaussian approximation potentials http://libatoms.org/Home/Software MatMiner
Python library for assisting machine learning in materials science https://hackingmaterials.github.io/matminer NOMAD
Collection of tools to explore correlations in materials datasets https://analytics-toolkit.nomad-coe.eu PROPhet
Code to integrate machine-learning techniques with quantum-chemistry approaches https://github.com/biklooost/PROPhet T
ensorMol Neural-network chemistry package https://github.com/jparkhill/TensorMol
(PDF) Machine learning for molecular and materials science. Available from: https://www.researchgate.net/publication/326608140_Machine_learning_for_molecular_and_materials_science [accessed Dec 06 2018].