Dr. Milan Kumar Jena

About

Dr. Milan Kumar Jena is an Assistant Professor in the Department of Materials Science and Metallurgical Engineering (MSME) at the Indian Institute of Technology (IIT) Bhilai, Durg, Chhattisgarh, India. His research interests focus on computational materials science, quantum transport theory, DNA sequencing, and AI/ML driven design and the discovery of advanced materials for energy applications.

Employment

• Indian Institute of Technology Bhilai Assistant Professor
  2025 - Present
• University of Warwick Postdoctoral Associate
  2025 - 2025
• Indian Institute of Technology Indore Translational Research Fellow (TRF)
  2024 - 2025

Education

• Indian Institute of Technology Indore Doctor of Philosophy (PhD)
  2020 - 2024
• Indian Institute of Technology Guwahati M.Sc.
• Ravenshaw University B.Sc.

Projects & Funding

Projects & funding information is unavailable.

Publications (39)

• Electronic Control of Emission Behavior in Atomically Precise Copper Nanoclusters
  JACS Au 2026 DOI: 10.1021/jacsau.6c00121
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• AI-Driven Two-Dimensional Molecular Electronics Spectroscopy for Chiral Discrimination of Natural Products
  ACS Materials Letters 2026 DOI: 10.1021/acsmaterialslett.6c00229
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• Decoding d- and l-Amino Acids: Data-Driven Recognition of Enantiomers and Post-Translational Modifications via Quantum Tunneling
  The Journal of Physical Chemistry Letters 2026 DOI: 10.1021/acs.jpclett.6c00203
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• Quantum Transport Informed Machine Learning Mapping of Current–Voltage Characteristics for Precision Deoxyribonucleic Acid Sequencing
  The Journal of Physical Chemistry A 2025 DOI: 10.1021/acs.jpca.5c03838
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• Insights into Thermal Conductivity of Pnictogen Chalcogenides: Machine Learning Stereochemically Active Lone Pairs and Hybridization
  Chemistry of Materials 2025 DOI: 10.1021/acs.chemmater.4c02294
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• Machine Learning Recognition of Artificial DNA Sequence with Quantum Tunneling Nanogap Junction
  The Journal of Physical Chemistry B 2025 DOI: 10.1021/acs.jpcb.4c06270
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• Automated-Screening Oriented Electric Sensing of Vitamin B1 Using a Machine Learning Aided Solid-State Nanopore
  The Journal of Physical Chemistry B 2025 DOI: 10.1021/acs.jpcb.4c05619
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• Deciphering Electrocatalytic Activity in Cu Nanoclusters: Interplay Between Structural Confinement and Ligands Environment
  Small 2025 DOI: 10.1002/smll.202500302
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• A hybrid supervised and unsupervised machine learning approach for identifying nucleoside drugs using nanopore readouts
  Nanoscale 2025 DOI: 10.1039/d5nr01379h
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• Machine Learning-Driven Quantum Sequencing of Natural and Chemically Modified DNA
  ACS Applied Materials & Interfaces 2025 DOI: 10.1021/acsami.4c22809
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