2 citations · 4 across the 7 of their papers we have counts for
7 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…
Open-Source Fermionic Neural Networks with Ionic Charge Initialization
Shai Pranesh, Shang Zhu, Venkat Viswanathan +1
Finding accurate solutions to the electronic Schrödinger equation plays an important role in discovering important molecular and material energies and characteristics. Consequently…
Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning
Shang Zhu, Bharath Ramsundar, Emil Annevelink +5
Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyt…
Open Source Infrastructure for Differentiable Density Functional Theory
Advika Vidhyadhiraja, Arun Pa Thiagarajan, Shang Zhu +2
Learning exchange correlation functionals, used in quantum chemistry calculations, from data has become increasingly important in recent years, but training such a functional requi…