5 citations · 7 across the 5 of their papers we have counts for
5 papers
Enhancing NMR Shielding Predictions of Atoms-in-Molecules Machine Learning Models with Neighborhood-Informed Representations
Surajit Das, Raghunathan Ramakrishnan
Accurate prediction of nuclear magnetic resonance (NMR) shielding with machine learning (ML) models remains a central challenge for data-driven spectroscopy. We present atomic vari…
Machine-Learned Potentials for Solvation Modeling
Roopshree Banchode, Surajit Das, Shampa Raghunathan +1
Solvent environments play a central role in determining molecular structure, energetics, reactivity, and interfacial phenomena. However, modeling solvation from first principles re…
Unlocking Inverted Singlet-Triplet Gap in Alternant Hydrocarbons with Heteroatoms
Atreyee Majumdar, Surajit Das, Raghunathan Ramakrishnan
Fifth-generation organic light-emitting diodes exhibit delayed fluorescence even at low temperatures, enabled by exothermic reverse intersystem crossing from a negative singlet-tri…
Influence of Pseudo-Jahn-Teller Activity on the Singlet-Triplet Gap of Azaphenalenes
Atreyee Majumdar, Komal Jindal, Surajit Das +1
We analyze the possibility of symmetry-lowering induced by pseudo-Jahn--Teller interactions in six previously studied azaphenalenes that are known to have their first excited singl…
Chemical Space-Informed Machine Learning Models for Rapid Predictions of X-ray Photoelectron Spectra of Organic Molecules
Susmita Tripathy, Surajit Das, Shweta Jindal +1
We present machine learning models based on kernel-ridge regression for predicting X-ray photoelectron spectra of organic molecules originating from the -shell ionization energi…