DMSE’s Killian Sheriff and Yifan Cao, with Profs. Rodrigo Freitas and Prof. Tess Smidt, use computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more. MIT EECS buff.ly/4bS1AMc

MIT researchers, using NSF ACCESS-funded resources at SDSC, have developed a cutting-edge #machinelearning method to analyze chemical patterns in metallic alloys. This breakthrough in computational models offers unprecedented insights. ow.ly/6mKx50SxwAA #deeplearning

An Massachusetts Institute of Technology (MIT) team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more. news.mit.edu/2024/machine-l…

Watch how machine learning views the complex patterns created by different arrangements of atoms in high-entropy alloys. Machine learning helps scientists map out over a million of these arrangements, known as short-range order. Read more here: buff.ly/4bS1AMc

Researchers from Massachusetts Institute of Technology (MIT) used ACCESS allocations on Expanse to develop a machine-learning method that efficiently decodes short-range order patterns in metallic alloys. Read the full story: access-ci.org/simulated-allo… @nsf

Graduate students Killian Sheriff and Yifan Cao from DMSE at MIT are using machine learning to quantify the complex chemical arrangements that make up short-range order (SRO) — the arrangement of atoms over small distances. news.mit.edu/2024/machine-l…

😍 Our work on the “Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks” has been published in Nature - npj Computational Materials npj Journals 😍 Check out the findings in our paper here: nature.com/articles/s4152…

Check out our latest work on “Nonequilibrium chemical short-range order in metallic alloys”!

The Nobel Prize in Physics 2024 has been awarded to John Hopfield and Geoffrey Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.” We present this collection to recognize the award. go.nature.com/47Xs1Qm

Thrilled to see our work featured alongside so many great scientists!

Graduate students Killian Sheriff, Yifan Cao, and Professor Rodrigo Freitas’ work on using machine learning to characterize short-range order has been featured in Nature Portfolio’s Nobel Prize in Physics 2024 collection. Read more here: buff.ly/3YBj9g4

Super engaging, two-day visit to Massachusetts Institute of Technology (MIT) for a pair of talks and a packed agenda of meetings with amazing faculty, PhD students and postdocs. On the first day I presented in the Program in Polymers and Soft Matter – a talk, titled ‘Soft Materials in Wireless, Skin-Integrated Medical

Excited to attend the #NeurIPS2024 #AI4Mat workshop next week! I’ll be presenting my internship work conducted Toyota Research Institute (TRI) under the supervision of Steven Torrisi, and Amalie Trewartha! Feel free to reach out if you'd like to hang out! openreview.net/forum?id=NX2RO…

Our paper has been selected as a spotlight at #NeurIPS2024 AI for Materials. We uncover why generative models are well-suited for materials synthesis prediction and propose a diffusion-based approach, When/Where: Sat 14 Dec 8:15a, West 211-214 openreview.net/forum?id=hy39q…

🚨 Our "Roadmap for the development of machine learning-based interatomic potentials" is now out! 🚨 iopscience.iop.org/article/10.108…

You can set `UV_TORCH_BACKEND=auto` and uv will automatically install the right CUDA-enabled PyTorch for your machine, zero configuration

You can run `uv add --script /path/to/script.py` to add inline dependencies to a Python file. If the script header doesn't exist already, uv will generate it for you.

🚨 Our work on “Machine learning potentials for modeling alloys across compositions” is out on arXiv! 🚨 arxiv.org/abs/2506.12592

Want to join our efforts Microsoft Research AI for Science to push the frontier of AI for materials? We are the team behind MatterGen & MatterSim and we have 2 job openings! Each can be in Amsterdam, NL, Berlin, DE, or Cambridge, UK. It is a rare opportunity to join a highly talented,

🚨Job opening🚨 Two positions in Cambridge/Amsterdam/Berlin as Senior Researcher and Senior Research Engineer to work with our materials discovery team at Microsoft Research AI for science. We are the team behind MatterGen and MatterSim Links in thread #materialsscience #AIforScience