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Intergrating Atomistic Skills into Agentic IDEs (Cursor, Claude Code, Google Antigravity, OpenClaw, etc)
The database of chemical parameters used with Reaction Mechanism Generator
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls.
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
ALCHEMI Toolkit-Ops is a collection of optimized batch kernels to accelerate computational chemistry and material science workflows.
Torch-native, batchable, atomistic simulations.
This repository implements supplementary useful functions for Python that are not part of the standard library. Examples include useful utilities like transparent support for zipped files etc.
Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflo…
A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy-paste templates that bring immediate value.
A simple, robust and flexible just-in-time job management framework in Python.
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
Similar to the Google authenticator just written in Python to automate annoying two factor authentication.
An open-source Python package for creating fast and accurate interatomic potentials.
The Fireworks Workflow Management Repo.
An interactive structure/property explorer for materials and molecules
Nonlinear Equation Solver with Modern Fortran
This repository contains all of the raw data, Q-Chem inputs, and plotting scripts for the paper "Gaussian-Based Periodic Grand Canonical Density Functional Theory with Implicit Solvation for Comput…
Interactive browser visualizations for materials science: crystal structures/molecules, trajectories, convex hulls, phase diagrams, Fermi surfaces, bands+DOS, Brillouin zones, etc.
Artificial Intelligence Research for Science (AIRS)
DScribe is a python package for creating machine learning descriptors for atomistic systems.
Automated creation and manipulation of chemical reaction networks (CRNs) in heterogeneous catalysis, powered by state-of-the-art ML evaluators and Julia-solvers for microkinetic modelling.
A unified framework for machine learning collective variables for enhanced sampling simulations
Code for performing adversarial attacks on atomistic systems using NN potentials