Arghya Bhowmik

According to our database, Arghya Bhowmik authored at least 19 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

AtomComposer: Discovering Chemical Space from First Principles with Reinforcement Learning.

Bjarke Hastrup, François R. J. Cornet, Tejs Vegge, Arghya Bhowmik

CoRR, May, 2026

Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink.

Jonas Elsborg, Felix Ærtebjerg, Luca A. Thiede, Alán Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik

CoRR, January, 2026

2025

Reinforcement Learning for Chemical Ordering in Alloy Nanoparticles.

Jonas Elsborg, Arghya Bhowmik

CoRR, November, 2025

GO-Diff: Data-free and amortized global structure optimization.

Nikolaj Rønne, Tejs Vegge, Arghya Bhowmik

CoRR, October, 2025

Shoot from the HIP: Hessian Interatomic Potentials without derivatives.

Andreas Burger, Luca A. Thiede, Nikolaj Rønne, Varinia Bernales, Nandita Vijaykumar, Tejs Vegge, Arghya Bhowmik, Alán Aspuru-Guzik

CoRR, September, 2025

Acoustic Classification of Maritime Vessels using Learnable Filterbanks.

Jonas Elsborg, Tejs Vegge, Arghya Bhowmik

CoRR, May, 2025

ELECTRA: A Symmetry-breaking Cartesian Network for Charge Density Prediction with Floating Orbitals.

Jonas Elsborg, Luca A. Thiede, Alán Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik

CoRR, March, 2025

Graph2Mat: universal graph to matrix conversion for electron density prediction.

Pol Febrer, Peter Bjørn Jørgensen, Miguel Pruneda, Alberto García, Pablo Ordejón, Arghya Bhowmik

Mach. Learn. Sci. Technol., 2025

Improving generative inverse design of molecular catalysts in small data regime.

François R. J. Cornet, Pratham Deshmukh, Bardi Benediktsson, Mikkel N. Schmidt, Arghya Bhowmik

Mach. Learn. Sci. Technol., 2025

Kinetic Langevin Diffusion for Crystalline Materials Generation.

François R. J. Cornet, Federico Bergamin, Arghya Bhowmik, Juan Maria Garcia Lastra, Jes Frellsen, Mikkel N. Schmidt

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Reducing training data needs with minimal multilevel machine learning (M3L).

Stefan N. Heinen, Danish Khan, Guido Falk von Rudorff, Konstantin Karandashev, Daniel J. Arismendi-Arrieta, Alastair J. A. Price, Surajit Nandi, Arghya Bhowmik, Kersti Hermansson, O. Anatole von Lilienfeld

Mach. Learn. Sci. Technol., 2024

ArtiSAN: navigating the complexity of material structures with deep reinforcement learning.

Jonas Elsborg, Arghya Bhowmik

Mach. Learn. Sci. Technol., 2024

2023

Equivariant Graph-Representation-Based Actor-Critic Reinforcement Learning for Nanoparticle Design.

Jonas Elsborg, Arghya Bhowmik

J. Chem. Inf. Model., June, 2023

2022

NeuralNEB - neural networks can find reaction paths fast.

Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Peter Bjørn Jørgensen, Ole Winther

Mach. Learn. Sci. Technol., December, 2022

Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks.

Jonas Busk, Peter Bjørn Jørgensen, Arghya Bhowmik, Mikkel N. Schmidt, Ole Winther, Tejs Vegge

Mach. Learn. Sci. Technol., 2022

Cheap Turns Superior: A Linear Regression-Based Correction Method to Reaction Energy from the DFT.

Surajit Nandi, Jonas Busk, Peter Bjørn Jørgensen, Tejs Vegge, Arghya Bhowmik

J. Chem. Inf. Model., 2022

Transition1x - a Dataset for Building Generalizable Reactive Machine Learning Potentials.

Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Jonas Busk, Ole Winther

CoRR, 2022

NeuralNEB - Neural Networks can find Reaction Paths Fast.

Mathias Schreiner, Arghya Bhowmik, Tejs Vegge, Ole Winther

CoRR, 2022

2020

DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction.

Peter Bjørn Jørgensen, Arghya Bhowmik

CoRR, 2020