activity
20182022
most citedBand gap prediction for large organic crystal structures with machine learning

76 citations · 229 across the 8 of their papers we have counts for

collaborators

8 papers

astro-ph.IM2022★ 58 cited

LIDA - The Leiden Ice Database for Astrochemistry

W. R. M. Rocha, M. G. Rachid, B. Olsthoorn +3

High quality vibrational spectra of solid-phase molecules in ice mixtures and for temperatures of astrophysical relevance are needed to interpret infrared observations toward proto…

cond-mat.stat-mech2021★ 12 cited

Persistent homology of quantum entanglement

Bart Olsthoorn

Structure in quantum entanglement entropy is often leveraged to focus on a small corner of the exponentially large Hilbert space and efficiently parameterize the problem of finding…

cond-mat.mtrl-sci2021★ 7 cited

Shifting computational boundaries for complex organic materials

R. Matthias Geilhufe, Bart Olsthoorn, Alexander V. Balatsky

Methodology adapted from data science sparked the field of materials informatics, and materials databases are at the heart of it. Applying artificial intelligence to these database…

cond-mat.mtrl-sci2020★ 10 cited

Identification of strongly interacting organic semimetals

R. Matthias Geilhufe, Bart Olsthoorn

Dirac- and Weyl point- and line-node semimetals are characterized by a zero band gap with simultaneously vanishing density of states. Given a sufficient interaction strength, such…

cond-mat.stat-mech2020★ 30 cited

Finding hidden order in spin models with persistent homology

Bart Olsthoorn, Johan Hellsvik, Alexander V. Balatsky

Persistent homology (PH) is a relatively new field in applied mathematics that studies the components and shapes of discrete data. In this work, we demonstrate that PH can be used…

cond-mat.mtrl-sci2019★ 1 cited

Mass fluctuations and absorption rates in Dirac materials sensors

Bart Olsthoorn, Alexander V. Balatsky

We study the mass fluctuations in gapped Dirac materials by treating the mass-term as both a continuous and discrete random variable. Gapped Dirac materials were proposed to be use…

cond-mat.mtrl-sci2018★ 76 cited

Band gap prediction for large organic crystal structures with machine learning

Bart Olsthoorn, R. Matthias Geilhufe, Stanislav S. Borysov +1

Machine-learning models are capable of capturing the structure-property relationship from a dataset of computationally demanding ab initio calculations. Over the past two years, th…

cond-mat.mtrl-sci2018★ 35 cited

Materials Informatics for Dark Matter Detection

R. Matthias Geilhufe, Bart Olsthoorn, Alfredo Ferella +4

Dark Matter particles are commonly assumed to be weakly interacting massive particles (WIMPs) with a mass in the GeV to TeV range. However, recent interest has shifted towards ligh…