2 citations · 2 across the 2 of their papers we have counts for
4 papers
Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
Zhenning Yang, Yuhan Chen, Patrick Tser Jern Kon +5
To unleash the full potential of AI for Science, we must untether the agents from a purely digital environment. The agent's ability to control and explore in real-world labs is ess…
Foundation Models for Discovery and Exploration in Chemical Space
Alexius Wadell, Anoushka Bhutani, Victor Azumah +26
Accurate prediction of atomistic, thermodynamic, and kinetic properties from molecular structures underpins materials innovation. Existing computational and experimental approaches…
CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning
Changwen Xu, Shang Zhu, Venkatasubramanian Viswanathan
The prediction of crystal properties is essential for understanding structure-property relationships and accelerating the discovery of functional materials. However, conventional a…
Self-supervised Pretraining for Partial Differential Equations
Varun Madhavan, Amal S Sebastian, Bharath Ramsundar +1
In this work, we describe a novel approach to building a neural PDE solver leveraging recent advances in transformer based neural network architectures. Our model can provide solut…