Tool
MatterGen
MatterGen is a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property constraints.
Research Tools: code, datasets, & models
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.
Dataset Source Code
HeurAgenix
HeurAgenix is a novel framework based on LLM, designed to generate, evolve, evaluate, and select heuristic algorithms for solving combinatorial optimization problems. It leverages the power of large language models to autonomously handle various optimization…
Dataset Source Code
MarS
MarS is a cutting-edge financial market simulation engine powered by the Large Market Model (LMM), a generative foundation model.
Dataset Source Code
Reducio Variational Autoencoder (Reducio-VAE)
Reducio-VAE is a model for encoding videos into an extremely small latent space. It is part of the Reducio-DiT, which is a highly efficient video generation method. Reducio-VAE encodes a 16-frame video clip to T/4∗H/32∗W/32…
Tool
TamGen
TamGen is a transformer-based chemical language model for developing target-specific drug compounds. Research shows that TamGen can also optimize existing molecules by designing target-aware molecule fragments, potentially enabling the discovery of novel compounds that build…
Dataset Source Code
TileLang
TileIR (tile-ir) is a concise domain-specific IR designed to streamline the development of high-performance GPU/CPU kernels (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). By employing a Pythonic syntax with an underlying compiler infrastructure on top of…
Dataset Source Code
MMLU-CF
Paper: “MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark”
Tool
RAD-DINO model
RAD-DINO is a vision transformer model trained to encode chest X-rays using the self-supervised learning method DINOv2. RAD-DINO is described in detail in RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision (F. Pérez-García, H. Sharma, S.…