Research Tools: code, datasets, & models

MatterSim is a deep learning model for accurate and efficient materials simulation and property prediction over a broad range of elements, temperatures and pressures to enable in silico materials design.

This repository contains all scripts for re-producing the results of our paper “Lost in Transmission: When and Why LLMs Fail to Reason Globally”.

ExACT is an approach for teaching AI agents to explore more effectively, enabling them to intelligently navigate their environments, gather valuable information, evaluate options, and identify optimal decision-making and planning strategies.

Biomolecular Emulator (BioEmu-1 for short) is a deep learning model that can generate thousands of protein structures per hour on a single graphics processing unit. It provides orders of magnitude greater computational efficiency compared to…

The Skala functional will enable more accurate, scalable predictions in computational chemistry. It starts with the largest high-accuracy dataset ever built for training deep-learning-based density functional theory (DFT) models. This dataset underpins Skala—coming soon to…

We develop the Science Foundation Model to empower natural scientists and accelerate breakthroughs in scientific discovery. As part of this effort, we introduce the sequence-based model, Nature Language Model (NatureLM). NatureLM is designed to span…

This codebase is the official implementation of “EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning.”

Phi-4-multimodal and Phi-4-mini, the newest models in Microsoft’s Phi family of small language models (SLMs) are now available. These models are designed to empower developers with advanced AI capabilities. Phi-4-multimodal, with its ability to process…

ReMe is a web-based framework that helps researchers create AI chatbots for personalized training and interventions aimed at strengthening memory and cognitive functions. Early evaluations show its potential to contribute to digital health innovation and…

EvoDiff is a general-purpose diffusion framework that combines evolutionary-scale data with the distinct conditioning capabilities of diffusion models for controllable protein generation in sequence space. EvoDiff generates high-fidelity, diverse, and structurally-plausible proteins that cover natural…