9 citations · 15 across the 5 of their papers we have counts for
5 papers
Open Materials Generation with Stochastic Interpolants
Philipp Hoellmer, Thomas Egg, Maya M. Martirossyan +11
The discovery of new materials is essential for enabling technological advancements. Computational approaches for predicting novel materials must effectively learn the manifold of…
ColabFit Exchange: open-access datasets for data-driven interatomic potentials
Joshua A. Vita, Eric G. Fuemmeler, Amit Gupta +5
Data-driven (DD) interatomic potentials (IPs) trained on large collections of first principles calculations are rapidly becoming essential tools in the fields of computational mate…
Kondo effect in a non-Hermitian, -symmetric Anderson model with Rashba spin-orbit coupling
Vinayak M Kulkarni, Amit Gupta, N. S. Vidhyadhiraja
The non-interacting and non-Hermitian, parity-time ()-symmetric Anderson model exhibits an exceptional point (EP) at a non-Hermitian coupling , which remains unr…
Data-Driven Modeling of S0 -> S1 Excitation Energy in the BODIPY Chemical Space: High-Throughput Computation, Quantum Machine Learning, and Inverse Design
Amit Gupta, Sabyasachi Chakraborty, Debashree Ghosh +1
Derivatives of BODIPY are popular fluorophores due to their synthetic feasibility, structural rigidity, high quantum yield, and tunable spectroscopic properties. While the characte…
Revving up 13C NMR shielding predictions across chemical space: Benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules
Amit Gupta, Sabyasachi Chakraborty, Raghunathan Ramakrishnan
The requirement for accelerated and quantitatively accurate screening of nuclear magnetic resonance spectra across the small molecules chemical compound space is two-fold: (1) a ro…