Open-source alternatives to AIRS

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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

DeePKS-kit is a program to generate accurate energy functionals for quantum chemistry systems, for both perturbative scheme (DeePHF) and self-consistent scheme (DeePKS).

deepmodeling-projects

dftio is to assist machine learning communities to transcript DFT output into a format that is easy to read or used by machine learning models.

After cloning this project, install lam-crystal-philately with common dependencies (including requirements for workflows) by ` pip install . To install additional dependencies for DP pip install ".dp" or mace pip install ".mace" `

](https://pepy.tech/project/mlatom)

A collection of Neural Network Models for chemistry - Quantum Chemistry Method - Force Field Method - Kernel Methods - Not based on Graph Models - Graph Domain Models - Transformer Domain Models - Universal models - Empirical force field - Semi-Empirical Method - Coarse-Grained Method - Enhanced…

ocp is the Open Catalyst Project's library of state-of-the-art machine learning algorithms for catalysis.

Easy Model Publishing: Publish pre-trained AI/ML models from a notebook with just a few commands - Reproducible Environments: Use containers to ensure consistent execution across different systems - Remote Execution: Run your models (or others) remotely on HPC resources seamlessly - Discoverable…

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

The GT4SD (Generative Toolkit for Scientific Discovery) is an open-source platform to accelerate hypothesis generation in the scientific discovery process. It provides a library for making state-of-the-art generative AI models easier to use.

Matbench Discovery

load-atoms is a Python package for Loading Open Access Datasets for Atomistic Materials Science (LOAD-AtoMS). See the documentation for more information.

This is the GitHub repository for the Julia programming language project's main website, julialang.org. The repository for the source code of the language itself can be found at github.com/JuliaLang/julia.

scdp is a codebase for training charge density prediction models described in the paper:

experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental) Q-stack

The QUIP package is a collection of software tools to carry out molecular dynamics simulations. It implements a variety of interatomic potentials and tight binding quantum mechanics, and is also able to call external packages, and serve as plugins to other software such as LAMMPS, CP2K and also…

This project serves as a comprehensive, community-driven directory of high-quality open-source Python libraries and tools for machine learning, data science, and artificial intelligence. It functions as a centralized resource for developers to discover, evaluate, and track the maintenance status of software packages across the entire machine learning ecosystem. The platform distinguishes itself through automated popularity tracking and data-driven content curation, which programmatically validate and rank projects based on community activity and development velocity. By organizing these tools

MALA (Materials Learning Algorithms) is a data-driven framework to generate surrogate models of density functional theory calculations based on machine learning. Its purpose is to enable multiscale modeling by bypassing computationally expensive steps in state-of-the-art density functional…

The OPTIMADE Specification

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Platform for materials scientists to contribute and disseminate their materials data through Materials Project

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.