255 citations · 531 across the 4 of their papers we have counts for
10 papers · 1 filter
Towards self-driving laboratories: The central role of density functional theory in the AI age
Bing Huang, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Density functional theory (DFT) plays a pivotal role for the chemical and materials science due to its relatively high predictive power, applicability, versatility and computationa…
Towards DMC accuracy across chemical space with scalable -QML
Bing Huang, O. Anatole von Lilienfeld, Jaron T. Krogel +1
In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numer…
Ab initio machine learning in chemical compound space
Bing Huang, O. Anatole von Lilienfeld
Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first p…
Dictionary of 140k GDB and ZINC derived AMONs
Bing Huang, O. Anatole von Lilienfeld
We present all {\bf A}mons for {\bf G}DB and {\bf Z}inc data-bases using no more than 7 non-hydrogen atoms (AGZ7)---a calculated organic chemistry building-block dictionary based o…
Boosting quantum machine learning models with multi-level combination technique: Pople diagrams revisited
Peter Zaspel, Bing Huang, Helmut Harbrecht +1
Inspired by Pople diagrams popular in quantum chemistry, we introduce a hierarchical scheme, based on the multi-level combination (C) technique, to combine various levels of approx…
The fundamentals of quantum machine learning
Bing Huang, Nadine O. Symonds, O. Anatole von Lilienfeld
Within the past few years, we have witnessed the rising of quantum machine learning (QML) models which infer electronic properties of molecules and materials, rather than solving a…