Explore our open-source tools, databases, and AI frameworks for materials discovery
Physics Informed AI & Machine Learning
AtomGPT.org
An AI-powered chatbot serving 10,000+ users across 50+ institutions for materials science queries, simulations, and analysis.
ALIGNN
Atomistic Line Graph Neural Network using both bond distances and bond angles for superior materials property prediction.
ALIGNN-FF
Universal machine learning force field enabling accurate molecular dynamics at DFT-level accuracy with massive speedup.
ChatGPT Material Explorer
Custom GPT eliminating AI hallucinations by connecting to verified materials science databases.
SlakoNet
Physics-informed neural network combining machine learning with tight-binding theory for electronic structure predictions.
MicroscopyGPT
Vision-language transformer generating atomic structure descriptions from microscopy images of 2D materials.
DiffractGPT
Generative transformer for atomic structure determination from X-ray diffraction patterns.
AtomVision
Computer vision tools for STM and STEM microscopy with automated defect detection.
ChemNLP
Natural language processing toolkit for materials science text mining and information extraction.
Databases & Infrastructure
JARVIS Infrastructure
1M+ materials calculations spanning DFT, force-fields, ML models, and experimental data.
SuperconDB
Comprehensive database of superconducting materials with critical temperatures and computed properties.
Benchmarking Tools
JARVIS-Leaderboard
Large-scale benchmark with 1281 contributions across 274 benchmarks using 152 methods.
BenchQC
Quantum computing benchmark evaluating variational quantum eigensolvers for materials science.