AB
Arghya Bhowmik
Technical University of Denmark
Research areas: Materials Science · Materials Chemistry · Machine Learning in Materials Science · Advanced Chemical Physics Studies · Advancements in Battery Materials
0000-0003-3198-5116
Employment
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Technical University of Denmark Associate professor
Education
Education history is unavailable.
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Projects & funding information is unavailable.
Publications (106)
- Selective Nitrate-to-Nitrite Electroreduction on Ag/Au Core-Shell Nanocubes Save
- HIP: Hessian Interatomic Potentials without derivatives Save
- Domain-specific dataset enable accurate MLIPs for disordered halide-based solid electrolytes Save
- Limitations of Foundation Models in Energy Materials Simulations: A Case Study in Polyanion Sodium Cathode Materials Save
- Reinforcement Learning for Chemical Ordering in Alloy Nanoparticles Save
- First-principles study of intrinsic and Fe3+ -related luminescence mechanisms in feldspar Save
- Mining Chemical Space with Generative Models for Battery Materials Save
- Machine learning potentials for electrified interfaces Save
- Adsorption Dynamics and Electric Double Layer Properties at Pt(100) Electrodes Save
- Mining Chemical Space with Generative Modelsfor Battery Materials Save
- Dataset exploring the atomic scale structure and ionic dynamics of polyanion sodium cathode materials Save
- Graph2Mat: universal graph to matrix conversion for electron density prediction Save
- Improving generative inverse design of molecular catalysts in small data regime Save
- Discovering Chemical Space from First Principles with Reinforcement Learning Save
- First-Principles Molecular Dynamics with Potential and Charge Fluctuations Applied to Au(111) in Alkaline Solutions Save
- Mining Chemical Space with Generative Modelsfor Battery Materials Save
- Graph2Mat: Universal graph to matrix conversion for electron density prediction Save
- Setting the standard for machine learning in phase field prediction: a benchmark dataset and baseline metrics Save
- ArtiSAN: navigating the complexity of material structures with deep reinforcement learning Save
- CALiSol-23: Experimental electrolyte conductivity data for various Li-salts and solvent combinations Save