Post #841

GraphML News (May 18th) - MatterSim, new workshops 🔮 Continuing the success of MatterGen, a diffusion model for material generation, MSR announced MatterSim (blog), an ML force field for atomistic simulations. A single MatterSim model supports a wide range of temperatures (0-5000 K) and pressures (up to 1000 GPa) and thus could be seen as a competitor to a recent MACE MP-0 - in fact, the authors compare against MACE MP-0 and observe significant improvements in certain tasks. Practically, MatterSim exists with M3GNet or Graphormer backbones (equivariance lives!) so you can select one depending on the available compute. MatterSim could be especially useful in active learning scenarios as a quick proxy when filtering generated candidates. 👷 A few upcoming summer schools and workshops: - Machine Learning for Chemistry 2024 CZS Summer School (Sept 9-13th in Karlsruhe) with invited speakers from Google, MSR, Mila, TU Munich, KIT, and EPFL. Early bird registration lasts until June 13th. - 21st Machine Learning on Graphs (MLG) workshop (Sept 9th or 13th, co-located with ECML PKDD 2024 in Vilnius) accepts submissions until June 15th. Invited speakers include Yllka Velaj (Uni Vienna) and Haggai Maron (NVIDIA & Technion). Weekend reading: Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings by Nurudin Alvareg-Gonzalez et al AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion by Adeesh Kolluru and John R Kitching - perhaps the first diffusion model for this task (uses Equiformer V2 and GemNet OC) MiniMol: A Parameter-Efficient Foundation Model for Molecular Learning by Kerstin Kläser, Błazej Banaszewski, and Valence labs - a 10M param for encompassing most tasks on 2D molecules (where you have smiles and graphs)