activity
20152023
most citedAn assessment of the structural resolution of various fingerprints commonly used in machine learning

49 citations · 93 across the 5 of their papers we have counts for

collaborators

9 papers

cond-mat.mtrl-sci2023★ 7 cited

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…

physics.chem-ph2022★ 16 cited

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…

cs.LG2021★ 1 cited

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…

cond-mat.mtrl-sci2021

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…

physics.comp-ph2020★ 49 cited

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…

physics.chem-ph2019

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…