697 citations · 1.6k across the 9 of their papers we have counts for
10 papers
Identifying Crystal Structures Beyond Known Prototypes from X-ray Powder Diffraction Spectra
Abhijith S. Parackal, Rhys E. A. Goodall, Felix A. Faber +1
The large amount of powder diffraction data for which the corresponding crystal structures have not yet been identified suggests the existence of numerous undiscovered, physically…
GPU-Accelerated Approximate Kernel Method for Quantum Machine Learning
Nicholas J. Browning, Felix A. Faber, O. Anatole von Lilienfeld
Conventional kernel-based machine learning models for ab initio potential energy surfaces, while accurate and convenient in small data regimes, suffer immense computational cost as…
BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale
Carl Poelking, Felix A. Faber, Bingqing Cheng
We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guid…
Rapid Discovery of Stable Materials by Coordinate-free Coarse Graining
Rhys E. A. Goodall, Abhijith S. Parackal, Felix A. Faber +2
A fundamental challenge in materials science pertains to elucidating the relationship between stoichiometry, stability, structure, and property. Recent advances have shown that mac…
An assessment of the structural resolution of various fingerprints commonly used in machine learning
Behnam Parsaeifard, Deb Sankar De, Anders S. Christensen +6
Atomic environment fingerprints are widely used in computational materials science, from machine learning potentials to the quantification of similarities between atomic configurat…
Neural networks and kernel ridge regression for excited states dynamics of CHNH: From single-state to multi-state representations and multi-property machine learning models
Julia Westermayr, Felix A. Faber, Anders S. Christensen +2
Excited-state dynamics simulations are a powerful tool to investigate photo-induced reactions of molecules and materials and provide complementary information to experiments. Since…
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…
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…
Machine Learning Energies of 2 M Elpasolite (ABCD) Crystals
Felix Faber, Alexander Lindmaa, O. Anatole von Lilienfeld +1
Elpasolite is the predominant quaternary crystal structure (AlNaKF prototype) reported in the Inorganic Crystal Structure Database. We have developed a machine learning mod…
Crystal Structure Representations for Machine Learning Models of Formation Energies
Felix Faber, Alexander Lindmaa, O. Anatole von Lilienfeld +1
We introduce and evaluate a set of feature vector representations of crystal structures for machine learning (ML) models of formation energies of solids. ML models of atomization e…