421 citations · 1.2k across the 38 of their papers we have counts for
39 papers
Deepmechanics
Abhay Shinde, Aryan Amit Barsainyan, Jose Siguenza +3
Physics-informed deep learning models have emerged as powerful tools for learning dynamical systems. These models directly encode physical principles into network architectures. Ho…
AgriVariant: Variant Effect Prediction using DeepChem-Variant for Precision Breeding in Rice
Ankita Vaishnobi Bisoi, Bharath Ramsundar
Predicting functional consequences of genetic variants in crop genes remains a critical bottleneck for precision breeding programs. We present AgriVariant, an end-to-end pipeline f…
A fully differentiable framework for training proxy Exchange Correlation Functionals for periodic systems
Rakshit Kumar Singh, Aryan Amit Barsainyan, Bharath Ramsundar
Density Functional Theory (DFT) is widely used for first-principles simulations in chemistry and materials science, but its computational cost remains a key limitation for large sy…
Protect: Steerable Retrosynthesis through Neuro-Symbolic State Encoding
Shreyas Vinaya Sathyanarayana, Shah Rahil Kirankumar, Sharanabasava D. Hiremath +1
Large Language Models (LLMs) have shown remarkable potential in scientific domains like retrosynthesis; yet, they often lack the fine-grained control necessary to navigate complex…
Inferring Dynamic Hidden Graph Structure in Heterogeneous Correlated Time Series
Jeshwanth Mohan, Bharath Ramsundar, Sandya Subramanian
Modeling heterogeneous correlated time series requires the ability to learn hidden dynamic relationships between component time series with possibly varying periodicities and gener…
Density-based Neural Temporal Point Processes for Heartbeat Dynamics
Sandya Subramanian, Bharath Ramsundar
Temporal point processes (TPPs) provide a natural mathematical framework for modeling heartbeats due to capturing underlying physiological inductive biases. In this work, we apply…