12 papers
Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives
Ibrahim Elsharkawy, Joschka Birk, Vinicius Mikuni +3
Foundation models (FMs) trained on large datasets and fine-tuned on downstream tasks have emerged as a powerful paradigm in AI for science. Industrial FMs are typically trained usi…
Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials
Alex Morehead, Miruna Cretu, Antonia Panescu +14
General-purpose 3D modeling in chemistry encompasses molecules and materials, requiring both generative and predictive capabilities. However, most existing AI approaches are optimi…
OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers
Ibrahim Elsharkawy, Vinicius Mikuni, Wahid Bhimji +1
We present OmniMol, a state-of-the-art all-to-all transformer-based small molecule machine-learned interatomic potential (MLIP). OmniMol is built by adapting Omnilearned, a foundat…
FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology
Biwei Dai, Po-Wen Chang, Wahid Bhimji +15
Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allow…
OmniLearned: A Foundation Model Framework for All Tasks Involving Jet Physics
Wahid Bhimji, Chris Harris, Vinicius Mikuni +1
Foundation models use large datasets to build an effective representation of data that can be deployed on diverse downstream tasks. Previous research developed the OmniLearn founda…
Competing with AI Scientists: Agent-Driven Approach to Astrophysics Research
Thomas Borrett, Licong Xu, Andy Nilipour +7
We present an agent-driven approach to the construction of parameter inference pipelines for scientific data analysis. Our method leverages a multi-agent system, Cmbagent (the anal…