activity
20222024
most cited-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol

24 citations · 51 across the 6 of their papers we have counts for

collaborators

6 papers

physics.chem-ph2024★ 16 cited

Analyzing Atomic Interactions in Molecules as Learned by Neural Networks

Malte Esders, Thomas Schnake, Jonas Lederer +4

While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a te…

cond-mat.mtrl-sci2024★ 1 cited

A Journey with THeSeuSS: Automated Python Tool for Modeling IR and Raman Vibrational Spectra of Molecules and Solids

Ariadni Boziki, Frédéric Ngono Mebenga, Philippe Fernandes +1

Vibrational spectroscopy is an indispensable analytical tool that provides structural fingerprints for molecules, solids, and interfaces thereof. This study introduces THeSeuSS (TH…

physics.chem-ph2024★ 24 cited

-Machine Learning to Elevate DFT-based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol

Apurba Nandi, Priyanka Pandey, Paul L. Houston +5

Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation…

quant-ph2024★ 1 cited

Gaussian Entanglement Measure: Applications to Multipartite Entanglement of Graph States and Bosonic Field Theory

Matteo Gori, Matthieu Sarkis, Alexandre Tkatchenko

Computationally feasible multipartite entanglement measures are needed to advance our understanding of complex quantum systems. An entanglement measure based on the Fubini-Study me…

physics.chem-ph2023★ 3 cited

Enabling Inverse Design in Chemical Compound Space: Mapping Quantum Properties to Structures for Small Organic Molecules

Alessio Fallani, Leonardo Medrano Sandonas, Alexandre Tkatchenko

Computer-driven molecular design combines the principles of chemistry, physics, and artificial intelligence to identify novel chemical compounds and materials with desired properti…

econ.EM2022★ 6 cited

Human Wellbeing and Machine Learning

Ekaterina Oparina, Caspar Kaiser, Niccolò Gentile +4

There is a vast literature on the determinants of subjective wellbeing. International organisations and statistical offices are now collecting such survey data at scale. However, s…