Mood Mohan
Oak Ridge National Laboratory, Sandia National Laboratories California, Indian Institute of Technology Guwahati, Osmania University University College of Technology
Research areas: Chemistry · Physical and Theoretical Chemistry · Ionic liquids properties and applications · Biofuel production and bioconversion · Catalysis for Biomass Conversion
0000-0001-5937-9746
About
Mood Mohan is currently working as a postdoctoral research associate in the Biosciences Division, Center for Molecular Biophysics research group at Oak Ridge National Laboratory (ORNL). Previously, Dr. Mohan worked as a postdoctoral research associate in the Department of Bioresource and Environmental Security at Sandia National Laboratories, California. Dr. Mohan received his Ph.D. in 2018 from the Department of Chemical Engineering at the Indian Institute of Technology Guwahati, India. Dr. Mohan's research area is primarily focused on the development of novel green solvents for lignocellulosic biomass, plastic upcycling, and carbon capture using theoretical and computational chemistry calculations (molecular dynamics, quantum chemical, and COSMO-RS model simulations). Dr. Mohan's research interest also covers leveraging both machine learning and first-principles physics-based methods to make improved predictions for biomass conversion, plastic depolymerization, carbon capture (CO2), and thermodynamic property predictions using various ML algorithms.
Employment
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Office of Science Oak Ridge National Laboratory Staff R&D Associate2026 - Present
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Oak Ridge National Laboratory Postdoctoral research associate2022 - 2026
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Sandia National Laboratories California Postdoctoral research associate2018 - 2022
Education
- 2014 - 2018
- 2012 - 2014
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Osmania University University College of Technology B.Tech2008 - 2012
Projects & Funding
Projects & funding information is unavailable.
Publications (47)
- Advances in organosolv delignification: a literature review and community guidelines for machine learning framework Save
- Deep eutectic solvents in lignocellulosic biorefineries: a comprehensive review of mechanistic insights, molecular modeling, and artificial intelligence Save
- Multiscale molecular insights into carbon dioxide chemisorption in amine-functionalized deep eutectic solvents Save
- Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation Save
- Molecular simulation and artificial intelligence for the circular economy of bioenergy and bioproducts Save
- Deep Learning Approaches for Predicting the Surface Tension of Ionic Liquids Save
- Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution Save
- Molecular property prediction for very large databases with natural language processing: a case study in ionic liquid design Save
- Macroscale properties and atomic-scale mechanisms of ash removal in low-temperature hydrothermal carbonization Save
- Molecular-level design of alternative media for energy-saving pilot-scale fibrillation of nanocellulose Save
- Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents Save
- High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning Save
- Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents Save
- Biomass pretreatment with distillable ionic liquids for an effective recycling and recovery approach Save
- Characterization of the acoustic cavitation in ionic liquids in a horn-type ultrasound reactor Save
- Multiscale investigation of the mechanism of biomass deconstruction in the dimethyl isosorbide/water Co-solvent pretreatment system Save
- Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning Save
- Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids Save
- Multiscale molecular simulations for the solvation of lignin in ionic liquids Save
- Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network Save