3 papers
eess.SY2026
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…
physics.chem-ph2026
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…
cond-mat.mtrl-sci2025
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…