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maml (MAterials Machine Learning) is a Python package that aims to provide useful high-level interfaces that make ML for materials science as easy as possible.
The main features of materialyzeai/maml are: General Tools.
Open-source alternatives to materialyzeai/maml include: datamol-io/molfeat — molfeat - the hub for all your molecular featurizers Docs | Homepage. deepchem/deepchem — DeepChem is an open-source Python framework for applying deep learning to molecular, chemical, and biological data,… divelab/airs — [license-image]:https://img.shields.io/badge/license-GPL3.0-green.svg [license-url]:https://github.com/divelab/AIRS/blo… dralgroup/mlatom — ](https://pepy.tech/project/mlatom). libatoms/quip — The QUIP package is a collection of software tools to carry out molecular dynamics simulations. It implements a… pycroscopy/atomai — AtomAI is a Pytorch-based package for deep and machine learning analysis of microscopy data that doesn't require any…
DeepChem is an open-source Python framework for applying deep learning to molecular, chemical, and biological data, serving as a comprehensive toolkit for drug discovery and materials science. At its core, it provides a featurizer-pipeline abstraction that converts raw molecular data into numerical representations, including graph-based molecular structures, SMILES tokenization vocabularies, and disk-sharded dataset persistence for handling large-scale data that exceeds RAM capacity. The framework distinguishes itself through integrated molecular docking workflows that automate pocket detecti
license-image:https://img.shields.io/badge/license-GPL3.0-green.svg license-url:https://github.com/divelab/AIRS/blob/main/LICENSE contributing-image:https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat
molfeat - the hub for all your molecular featurizers Docs | Homepage
