You can also find my articles on Google Scholar

Published

1. R. Modee, S. Mehta, S. Laghuvarapu, and U. D. Priyakumar, “MolOpt: Autonomous Molecular Geometry Optimization Using Multiagent Reinforcement Learning”, J. Phys. Chem. B, 2023, 127, 10295–10303.
2. R. Modee, A. Verma, K. Joshi, and U. D. Priyakumar, “MeGen - Generation of gallium metal clusters using reinforcement learning”, Machine Learning: Science and Technology (2023).
3. D. B. Korlepara, C. S. Vasavi, S. Jeurkar, P. K. Pal, S. Roy, S. Mehta, S. Sharma, V. Kumar, C. Muvva, B. Sridharan, A. Garg, R. Modee, A. P. Bhati, D. Nayar, and U. D. Priyakumar, “PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications”, Sci. Data 9, 1–10 (2022).
4. R. Modee, S. Laghuvarapu, and U. D. Priyakumar, “Benchmark study on deep neural network potentials for small organic molecules”, J. Comput. Chem. 43, 308–318 (2022).
5. R. Modee, S. Agarwal, A. Verma, K. Joshi, and U. D. Priyakumar, “DART: Deep learning enabled topological interaction model for energy prediction of metal clusters and its application in identifying unique low energy isomers”, Phys. Chem. Chem. Phys. 23, 21995–22003 (2021).

Not Published

1. R. Modee, and U. D. Priyakumar, MolOpt2: Understanding and correcting pathologies in developing learned molecular geometry optimizer, (2023).
2. B. Sridharan, A. Sinha, J. Bardhan, R. Modee, and U. D. Priyakumar, Review of Reinforcement Learning in Chemistry, (2022).(In press)