From the 1 of 6 linked papers with an AI index.
6 papers
Quantum Multiscale Modeling: A Hierarchy of Algorithms for Complex Chemical Systems
Seenivasan Hariharan, Kareljan Schoutens, Sachin Kinge +1
The paper proposes a systematic framework for linking fault‑tolerant quantum algorithms across electronic, atomistic, mesoscopic, and continuum scales in complex chemical systems,…
Quantum Walks for Chemical Reaction Networks
Seenivasan Hariharan, Sebastian Zur, Sachin Kinge +3
Near a detailed-balance equilibrium, the perturbed mass-action dynamics of a chemical reaction network (CRN) map exactly onto an electrical-flow problem on the bipartite species-re…
Transfer learning of GW-Bethe-Salpeter Equation excitation energies
Dario Baum, Arno Förster, Lucas Visscher
A persistent challenge in machine learning for electronic-structure calculations is the sharp imbalance between abundant low-fidelity data like DFT or TDDFT results and the scarcit…
qs quasiparticle and -BSE excitation energies of 133,885 molecules
Dario Baum, Arno Förster, Lucas Visscher
Machine learning applications in the chemical sciences, especially when based on neural networks, critically depend on the availability of large quantities of high quality data. As…
Predicting complete basis set limit quasiparticle energies from triple- calculations
Dario Baum, Lucas Visscher, Arno Förster
We present a simple linear model to estimate the basis set incompleteness errors (BSIE) of (vertex-corrected) QP energies based on the kinetic energy of the corresponding orbi…
Calculation and analysis of exciton couplings via a subsystem formulation of the -Bethe-Salpeter Equation
Sarathchandra Khandavilli, Arno Förster, Lucas Visscher
We present a fragment-based framework for analyzing exciton couplings within the -Bethe-Salpeter Equation formalism using localized molecular orbitals, and assess how excitonic…