Discover an index of datasets, SDKs, APIs and open-source tools developed by Microsoft researchers and shared with the global academic community below. These experimental technologies—available through Azure AI Foundry Labs (opens in new tab)—offer a glimpse into the future of AI innovation.
RetroChimera is a model that takes as input a target molecule that one wants to synthesize, encoded as a sequence of characters (using the SMILES notation), and produces several potential chemical reactions which could be…
Skala is a neural network-based exchange-correlation functional for density functional theory (DFT), developed by Microsoft Research AI for Science. It leverages deep learning to predict exchange-correlation energies from electron density features, achieving chemical accuracy for…
This repository hosts the official code and data artifact for the “Affective Air Quality” dataset. Details about the user study and data collection can be found in our paper. The dataset released it’s the first to…
Dion is a scalable optimizer that accelerates neural network training by applying orthonormal weight updates using amortized power iteration, which works efficiently on sharded matrices. It reduces communication overhead through low-rank compression and error feedback,…
The absolute trainer to light up AI agents. We present Agent Lightning, a flexible and extensible framework that enables seamless agent optimization for any existing agent framework.
AI Behavioral Science for Anthropomorphic Agents: toward human‑centric, symbiotic, and autonomous AI. This project centers on evaluating and promoting anthropomorphic intelligence—AI agents with a human-like mindset and a degree of awareness, capable of acting proactively…
TRELLIS is a large 3D asset generation model that creates high-quality 3D assets from simple text or image inputs. Using a unified latent space (SLAT), it delivers detailed, textured 3D models in formats like meshes,…
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.
