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Information hub for our project training the largest possible historical LLMs.
Secure and fast microVMs for serverless computing.
Streamlit app to analyze PowerPoint files and identify large images by slide
Software for writing protocols and running them on the Opentrons Flex and Opentrons OT-2
Main repository for the Modular Autonomous Discovery for Science (MADSci) Framework
An open source implementation of CLIP.
Comprehensive benchmark of uncertainty quantification methods for regression tasks with interactive dashboard
Benchmarking different LLM approaches for Bayesian optimization
An MCP server implementation enabling AI applications to interact with Globus services
DARA: Data-driven automated Rietveld analysis for powder XRD phase search and refinement
DFTB+ general package for performing fast atomistic simulations
Access large language models from the command-line
An open-source AI agent that brings the power of Gemini directly into your terminal.
Packmol - Initial configurations for molecular dynamics simulations
Render any git repo into a single static HTML page for humans or LLMs
The simplest, fastest repository for training/finetuning small-sized VLMs.
Official website for Hackathon on LLM Applications for Materials and Chemistry
Interactive browser visualizations for materials science: periodic tables, 3d crystal structures, MD trajectories, heatmaps, scatter plots, histograms.
Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models
A scientific reasoning model, dataset, and reward functions for chemistry.
Connecting experimental materials science with computational modeling and literature analysis via LLM-powered agents
A GPU accelerated Python multislice slice code
Chemical intuition for surface science in a package.
Torch-native, batchable, atomistic simulations.
[NeurIPS 2024] Official implementation of the Efficiently Scaled Attention Interatomic Potential
Collecting awesome papers of RAG for AIGC. We propose a taxonomy of RAG foundations, enhancements, and applications in paper "Retrieval-Augmented Generation for AI-Generated Content: A Survey".