Senior RSDE positions open, join our amazing team :)

Proud to present Denoising Diffusion Models, where we connect the learned score of a diffusion model with force fields to do sampling and simulations🎉 Work done during a wonderful collaborative internship Microsoft Research JCIM & JCTC Journals pubs.acs.org/doi/10.1021/ac…, arxiv.org/abs/2302.00600

Internship opportunity in quantum chemistry 🤩👇

[1/N] Generative AI has revolutionized how we create text and images. How about designing novel materials? We at Microsoft Research #AI4Science are thrilled to announce MatterGen: our generative model that enables broad property-guided materials design. 👇 arxiv.org/abs/2312.03687

I'm not at NeurIPS this year but I strongly echo this sentiment! 🥳 My very first NeurIPS (2017, Long Beach) was also the 1st anniversary of the Deep Learning book! And I got this awesome chance to candidly chat with Ian Goodfellow Aaron Courville and Yoshua Bengio.

[1/7] 🎉Our paper Time-lagged Information Bottleneck (T-IB) is in #ICLR2024! Kudos to Bas Veeling Ryota Tomioka Patrick Forré & MSR AI4Science! 📄arxiv.org/abs/2309.07200 T-IB maps complex dynamics to simple latent spaces for super fast, highly accurate simulations!

Internship opportunity at Microsoft Research #AI4Science on generative protein models. Location Berlin, Amsterdam or Cambridge. Apply here asap: jobs.careers.microsoft.com/global/en/job/…

We have now released the denoising diffusion model codebase! It includes: 🏋️ Pretrained models ✅ Easy sampling and evaluation 💻 Full model and training scripts Check out our repository here: github.com/microsoft/two-…

If you’re interested in RL and applications in drug discovery 👇🔥

If you trained on the true equilibrium distribution structures extracted from a simulation you get the true forces (if your noise is sufficiently low) This paper first showed this and we have validated for a simple system that it is precise. arxiv.org/abs/2302.00600

Exciting work on atmospheric foundation model from my colleagues in Microsoft Research AI for Science 🔥🔥❤️❤️

We’re looking for a senior data engineer for large scale data generation, working at the intersection of ML and comp chem. Come work with us!

New preprint on performing thermodynamic integration along the trajectories of a denoising diffusion model: arxiv.org/abs/2406.02313. With François Fleuret and Tristan Bereau (1/n)

Announcing our new model for materials! FlowMM... - Generates stable & novel materials efficiently - Predicts crystal structure accurately - Generalizes Riemannian Flow Matching to point clouds w/ periodic boundaries arxiv.org/abs/2406.04713 Ricky T. Q. Chen Anuroop Sriram Brandon Wood

The afternoon session on the Deep Generative Models days continues with Diffusion Models with Chin-Wei Huang & Víctor Garcia Satorras from Microsoft Research. Let’s learn what is new around Diffusion Models and how they are used today #ProbAI

Introducing Neural Electron Real-space Density (NERD) models! 🧠 You’ve solved the electronic Schrödinger equation using PauliNet or Psiformer - what next? Important properties come from the 1-electron density (the marginal #MachineLearning) arxiv.org/abs/2409.01306

🥳Happy to share our work on extracting extremely accurate electron densities (and many related properties) from wavefunctions. Feel honored to work with all these talented colleagues in Microsoft Research AI for Science Lab

Curious what I've been working on since joining AI for science? With an incredible, multidisciplinary team we've studied the 'wave function -> election density' marginalisation using score matching & NCE. This has some big gains over older Gaussian-based methods.