most citedLean CNNs for mapping electron charge density fields to material properties

5 citations · 5 across the 1 of their papers we have counts for

collaborators

5 papers

physics.chem-ph2026

Unraveling the PFAS helix: A statistical approach

Pranoy Ray, Haden Cavalli, Gashaw Bizana +5

The extreme persistence of per- and polyfluoroalkyl substances (PFAS) in the environment is rooted in their three-dimensional molecular conformation. The helical twist adopted by p…

cond-mat.mtrl-sci2026

Spatial statistics for screening molecular structures

Pranoy Ray, Surya R. Kalidindi

The dominant paradigm in computational materials discovery relies on heavily parameterized deep architectures, including message-passing graph networks and equivariant models, that…

cond-mat.mtrl-sci2026

Electronic manifolds for extrapolative alloy discovery

Pranoy Ray, Sayan Bhowmik, Phanish Suryanarayana +2

This study presents a computationally efficient framework for accelerated alloy discovery that uses the non-interacting electron density to capture intrinsic structure-property rel…

cond-mat.mtrl-sci20255 cited

Lean CNNs for mapping electron charge density fields to material properties

Pranoy Ray, Kamal Choudhury, Surya R. Kalidindi

This work introduces a lean CNN (convolutional neural network) framework, with a drastically reduced number of fittable parameters (<81K) compared to the benchmarks in current lite…

physics.chem-ph2025

Refining Coarse-Grained Molecular Topologies: A Bayesian Optimization Approach

Pranoy Ray, Adam P. Generale, Nikhith Vankireddy +6

Molecular Dynamics (MD) simulations are essential for accurately predicting the physical and chemical properties of large molecular systems across various pressure and temperature…