Quantum Chemistry and Chemical Compound Space Design

My research combines quantum chemistry, high-throughput computation, and machine learning to understand how molecular structure determines properties and to guide the design of new compounds. The QM9 dataset [1] and our work on Δ-machine learning [2] helped establish data-driven approaches to predicting quantum chemical properties across large molecular spaces. More recently, we have applied these ideas to inverse design in the BODIPY chemical space [3] and to understanding singlet–triplet energy gaps in azaphenalenes [4].

Computational NMR is a particular focus. We developed the QM9-NMR dataset and benchmarks for 13C shielding prediction [5], and have since improved predictions using neighborhood-informed molecular representations [6]. This work connects local chemical environments to NMR shielding and supports the interpretation of molecular structure and conformation. My broader interests include quantum mechanical descriptions of molecular behavior, such as real-time electron dynamics and quantum interference [7]. I also apply mathematical and computational modeling to biological questions, including how neural microcircuits can be distinguished from their patterns of activity [8].

Selected references

  1. R. Ramakrishnan et al., Quantum chemistry structures and properties of 134 kilo molecules, Scientific Data 1, 140022 (2014).
  2. R. Ramakrishnan et al., Big data meets quantum chemistry approximations: The delta-machine learning approach, Journal of Chemical Theory and Computation 11, 2087–2096 (2015).
  3. A. Gupta et al., Data-driven modeling of S0 → S1 excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design, The Journal of Chemical Physics 155, 244102 (2021).
  4. A. Majumdar and R. Ramakrishnan, Insights into symmetry and substitution patterns governing singlet–triplet energy gap in the chemical space of azaphenalenes, Chemistry—A European Journal (2026).
  5. A. Gupta et al., Revving up 13C NMR shielding predictions across chemical space, Machine Learning: Science and Technology 2, 035010 (2021).
  6. S. Das and R. Ramakrishnan, Enhancing NMR shielding predictions of atoms-in-molecules machine learning models with neighborhood-informed representations, The Journal of Chemical Physics (2026).
  7. R. Ramakrishnan, Quantum interference in real-time electron dynamics, The Journal of Chemical Physics 152, 194111 (2020).
  8. A. Naveen Kumar and R. Ramakrishnan, Indistinguishability domains of neural microcircuit motifs mapped through classification scores of postsynaptic spike counts, Proceedings of the 13th ACM IKDD International Conference on Data Science (2026).