| 1. |
- Borysov, Stanislav S., et al.
(författare)
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Online search tool for graphical patterns in electronic band structures
- 2018
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Ingår i: npj Computational Materials. - : Springer Science and Business Media LLC. - 2057-3960. ; 4
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Tidskriftsartikel (refereegranskat)abstract
- Many functional materials can be characterized by a specific pattern in their electronic band structure, for example, Dirac materials, characterized by a linear crossing of bands; topological insulators, characterized by a Mexican hat pattern or an effectively free electron gas, characterized by a parabolic dispersion. To find material realizations of these features, manual inspection of electronic band structures represents a relatively easy task for a small number of materials. However, the growing amount of data contained within modern electronic band structure databases makes this approach impracticable. To address this problem, we present an automatic graphical pattern search tool implemented for the electronic band structures contained within the Organic Materials Database. The tool is capable of finding user-specified graphical patterns in the collection of thousands of band structures from high-throughput calculations in the online regime. Using this tool, it only takes a few seconds to find an arbitrary graphical pattern within the ten electronic bands near the Fermi level for 26,739 organic crystals. The source code of the developed tool is freely available and can be adapted to any other electronic band structure database.
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| 2. |
- Finizio, Simone, et al.
(författare)
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Periodogram-Based Detection of Unknown Frequencies in Time-Resolved Scanning Transmission X-ray Microscopy
- 2022
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Ingår i: ACS Nano. - : American Chemical Society (ACS). - 1936-0851 .- 1936-086X. ; 16:12, s. 21071-21078
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Tidskriftsartikel (refereegranskat)abstract
- Pump–probe time-resolved imaging is a powerful technique that enables the investigation of dynamical processes. Signal-to-noise and sampling rate restrictions normally require that cycles of an excitation are repeated many times with the final signal reconstructed using a reference. However, this approach imposes restrictions on the types of dynamical processes that can be measured, namely, that they are phase locked to a known external signal (e.g., a driven oscillation or impulse). This rules out many interesting processes such as auto-oscillations and spontaneously forming populations, e.g., condensates. In this work we present a method for time-resolved imaging, based on the Schuster periodogram, that allows for the reconstruction of dynamical processes where the intrinsic frequency is not known. In our case we use time of arrival detection of X-ray photons to reconstruct magnetic dynamics without using a priori information on the dynamical frequency. This proof-of-principle demonstration will allow for the extension of pump–probe time-resolved imaging to the important class of processes where the dynamics are not locked to a known external signal and in its presented formulation can be readily adopted for X-ray imaging and also adapted for wider use.
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| 3. |
- Geilhufe, R. Matthias, et al.
(författare)
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Identification of strongly interacting organic semimetals
- 2020
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Ingår i: Physical Review B. - : AMER PHYSICAL SOC. - 2469-9950 .- 2469-9969. ; 102:20
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Tidskriftsartikel (refereegranskat)abstract
- 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 materials can undergo an interaction instability, e.g., into an excitonic insulator phase. Due to generically flatbands, organic crystals represent a promising materials class in this regard. We combine machine learning, density functional theory, and effective models to identify specific example materials. Without taking into account the effect of many-body interactions, we found the organic charge transfer salts [bis(3,4-diiodo-3',4'-ethyleneditio-tetrathiafulvalene), 2,3-dichloro-5,6-dicyanobenzoquinone, acetenitrile] [(EDT-TTF-I-2)(2)](DDQ)center dot(CH3CN) and 2, 2', 5, 5'-tetraselenafulvalene-7, 7, 8, 8-tetracyano-p-quinodimethane (TSeF-TCNQ) and a bis-1,2,3-dithiazolyl radical conductor to exhibit a semimetallic phase in our ab initio calculations. Adding the effect of strong particle-hole interactions for (EDT-TTF-I-2)(2)(DDQ)center dot(CH3CN) and TSeF-TCNQ opens an excitonic gap on the order of 60 and 100 meV, which is in good agreement with previous experiments on these materials.
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| 4. |
- Geilhufe, R. Matthias, et al.
(författare)
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Materials Informatics for Dark Matter Detection
- 2018
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Ingår i: Physica Status Solidi. Rapid Research Letters. - : Wiley. - 1862-6254 .- 1862-6270. ; 12:11
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Tidskriftsartikel (refereegranskat)abstract
- 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 toward lighter WIMPs, which are more difficult to probe experimentally. A detection of sub-GeV WIMPs will require the use of small gap materials in sensors. Using recent estimates of the WIMP mass, we identify the relevant target space toward small gap materials (100 to 10 meV). Dirac Materials, a class of small- or zero-gap materials, emerge as natural candidates for sensors for Dark Matter detection. We propose the use of informatics tools to rapidly assay materials band structures to search for small gap semiconductors and semimetals, rather than focusing on a few preselected compounds. As a specific example of the proposed strategy, we use the organic materials database () to identify organic candidates for sensors: the narrow band gap semiconductors BNQ-TTF and DEBTTT with gaps of 40 and 38 meV, and the Dirac-line semimetal (BEDT-TTF)center dot Br which exhibits a tiny gap of approximate to 50 meV when spin-orbit coupling is included. We outline a novel and powerful approach to search for dark matter detection sensor materials by means of a rapid assay of materials using informatics tools.
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| 5. |
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| 6. |
- Olsthoorn, Bart, et al.
(författare)
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Band Gap Prediction for Large Organic Crystal Structures with Machine Learning
- 2019
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Ingår i: Advanced Quantum Technologies. - : Wiley. - 2511-9044. ; 2:7-8
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Tidskriftsartikel (refereegranskat)abstract
- 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, the Organic Materials Database (OMDB) has hosted a growing number of calculated electronic properties of previously synthesized organic crystal structures. The complexity of the organic crystals contained within the OMDB, which have on average 82 atoms per unit cell, makes this database a challenging platform for machine learning applications. In this paper, the focus is on predicting the band gap which represents one of the basic properties of a crystalline material. With this aim, a consistent dataset of 12 500 crystal structures and their corresponding DFT band gap are released, freely available for download at https://omdb.mathub.io/dataset. An ensemble of two state‐of‐the‐art models reach a mean absolute error (MAE) of 0.388 eV, which corresponds to a percentage error of 13% for an average band gap of 3.05 eV. Finally, the trained models are employed to predict the band gap for 260 092 materials contained within the Crystallography Open Database (COD) and made available online so that the predictions can be obtained for any arbitrary crystal structure uploaded by a user.
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| 7. |
- Olsthoorn, Bart, et al.
(författare)
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Finding hidden order in spin models with persistent homology
- 2020
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Ingår i: Physical Review Research. - : American Physical Society (APS). - 2643-1564. ; 2:4
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Tidskriftsartikel (refereegranskat)abstract
- Persistent homology (PH) is a relatively new field in applied mathematics that studies the components and shapes of discrete data. In this paper, we demonstrate that PH can be used as a universal framework to identify phases of classical spins on a lattice. This demonstration includes hidden order such as spin-nematic ordering and spin liquids. By converting a small number of spin configurations to barcodes we obtain a descriptive picture of configuration space. Using dimensionality reduction to reduce the barcode space to color space leads to a visualization of the phase diagram.
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| 8. |
- Olsthoorn, Bart
(författare)
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Homology and machine learning for materials informatics
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Materials informatics is the field of study where materials science is combined with modern data science. This data-driven approach is powered by the growing availability of computational power and storage capability. The development and application of these methods accelerates materials science and represents an effective way to study and model material properties. This thesis is a compilation of theoretical and computational works that can be divided into three key areas: materials databases, machine learning for materials, and homology for materials.Machine learning and data mining rely on the availability of materials databases to test methods and models. The Organic Materials Database (OMDB), for example, contains a large number of organic crystals and their corresponding electronic structures. The electronic properties of the organic crystals are computed using atomic scale materials modelling, which is computationally expensive because organic crystals typically contain many atoms in the unit cell. However, the resulting data can be used in a variety of materials informatics applications. We demonstrate data mining for dark matter sensors as an example application.Accurate machine learning models can capture the structure-property relationship of materials and accelerate the discovery of new materials with desired properties. This is explored by investigating the properties of the organic crystals in the OMDB. For example, we employ supervised learning on the electronic band gap, an important material property for technological applications. Unsupervised learning is used to construct a dimensionality-reduced chemical space that reveals interesting clusters of materials.Finally, persistent homology is a relatively new method from the field of algebraic topology that studies the shapes that are present in data at different length scales. In this thesis, the method is used to study magnetic materials and their phase transitions. More specifically, in the case of classical models, we use persistent homology to detect the phase transition directly from sampled spin configurations. For quantum spin models, the shapes in the entanglement structure are captured and a sudden change reveals a quantum phase transition.In summary, these three topics provide an overview on how to study material properties with modern data science methods. The tools can be used in combination with the traditional methods in materials science and accelerate materials design.
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| 9. |
- Olsthoorn, Bart, et al.
(författare)
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Indoor radon exposure and its correlation with the radiometric map of uranium in Sweden
- 2022
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Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 811
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Tidskriftsartikel (refereegranskat)abstract
- Indoor radon concentrations are controlled by both human factors and geological factors. It is important to separate the anthropogenic and geogenic contributions. We show that there is a positive correlation between the radiometric map of uranium in the ground and the measured radon in the household in Sweden. A map of gamma radiation is used to obtain an equivalent uranium concentration (ppm eU) for each postcode area. The aggregated uranium content is compared to the yearly average indoor radon concentration for different types of houses. Interestingly, modern households show reduced radon concentrations even in postcode areas with high average uranium concentrations. This shows that modern construction is effective at reducing the correlation with background uranium concentrations and minimizing the health risk associated with radon exposure. These correlations and predictive housing parameters could assist in monitoring higher risk areas.
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| 10. |
- Olsthoorn, Bart, et al.
(författare)
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Mass fluctuations and absorption rates in dark-matter sensors based on Dirac materials
- 2020
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Ingår i: Physical Review B. - : AMER PHYSICAL SOC. - 2469-9950 .- 2469-9969. ; 101:4
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Tidskriftsartikel (refereegranskat)abstract
- 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 used as materials for dark-matter sensors. One thus would need to estimate the role of disorder and fluctuations on the interband absorption of dark matter. We find that both continuous and discrete fluctuations across the sample introduce tails (e.g., Dirac-Lifshitz tails) in the density of states and the interband absorption rate. We estimate the strength of the gap filling and discuss implications of these fluctuations on the performance as sensors for dark matter detection. The approach used in this work provides a basic framework to model the disorder by any arbitrary mechanism on the interband absorption of Dirac material sensors.
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