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 eciency 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 TensorMol 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].

Publicly accessible learning resources and tools related to machine learning

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