697 citations · 1.6k across the 11 of their papers we have counts for
11 papers
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…
Impact of non-normal error distributions on the benchmarking and ranking of Quantum Machine Learning models
Pascal Pernot, Bing Huang, Andreas Savin
Quantum machine learning models have been gaining significant traction within atomistic simulation communities. Conventionally, relative model performances are being assessed and c…
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…
Alchemical and structural distribution based representation for improved QML
Felix A. Faber, Anders S. Christensen, Bing Huang +1
We introduce a representation of any atom in any chemical environment for the generation of efficient quantum machine learning (QML) models of common electronic ground-state proper…
Quantum machine learning using atom-in-molecule-based fragments selected on-the-fly
Bing Huang, O. Anatole von Lilienfeld
First principles based exploration of chemical space deepens our understanding of chemistry, and might help with the design of new materials or experiments. Due to the computationa…
Machine learning prediction errors better than DFT accuracy
Felix A. Faber, Luke Hutchison, Bing Huang +7
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state propert…
Understanding molecular representations in machine learning: The role of uniqueness and target similarity
Bing Huang, O. Anatole von Lilienfeld
The predictive accuracy of Machine Learning (ML) models of molecular properties depends on the choice of the molecular representation. Based on the postulates of quantum mechanics,…