arXiv subjects

Patrick Rinke

Publications and source records attributed to Patrick Rinke.

At least 37 records · Page 2Linked to original sources

arXiv2024

Validation of the GreenX library time-frequency component for efficient GW and RPA calculations

Electronic structure calculations based on many-body perturbation theory (e.g. GW or the random-phase approximation (RPA)) require function evaluations in the complex time and frequency domain, for example inhomogeneous Fourier transforms or analytic continuation from the imaginary axis to the real axis. For inhomogeneous Fourier transforms, the time-frequency component of the GreenX library provides time-frequency grids that can be utilized in low-scaling RPA and GW implementations. In addition, the adoption of the compact frequency grids provided by our library also reduces the computational overhead in RPA implementations with conventional scaling. In this work, we present low-scaling GW and conventional RPA benchmark calculations using the GreenX grids with different codes (FHI-aims, CP2K and ABINIT) for molecules, two-dimensional materials and solids. Very small integration errors are observed when using 30 time-frequency points for our test cases, namely $<10^{-8}$ eV/electron for the RPA correlation energies, and 10 meV for the GW quasiparticle energies.

physics.comp-ph↗

arXiv2024

Benchmarking the accuracy of the separable resolution of the identity approach for correlated methods in the numeric atom-centered orbitals framework

Four-center two-electron Coulomb integrals routinely appear in electronic structure algorithms. The resolution-of-the-identity (RI) is a popular technique to reduce the computational cost for the numerical evaluation of these integrals in localized basis-sets codes. Recently, Duchemin and Blase proposed a separable RI scheme [J. Chem. Phys. 150, 174120 (2019)], which preserves the accuracy of the standard global RI method with the Coulomb metric (RI-V) and permits the formulation of cubic-scaling random phase approximation (RPA) and $GW$ approaches. Here, we present the implementation of a separable RI scheme within an all-electron numeric atom-centered orbital framework. We present comprehensive benchmark results using the Thiel and the GW100 test set. Our benchmarks include atomization energies from Hartree-Fock, second-order Møller-Plesset (MP2), coupled-cluster singles and doubles, RPA and renormalized second-order perturbation theory as well as quasiparticle energies from $GW$. We found that the separable RI approach reproduces RI-free HF calculations within 9 meV and MP2 calculations within 1 meV. We have confirmed that the separable RI error is independent of the system size by including disordered carbon clusters up to 116 atoms in our benchmarks

physics.chem-ph↗

arXiv2023

Towards data-driven mass spectrometry in atmospheric science

Aerosols found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass spectrometry is the single most important analytical technique in atmospheric chemistry and is used to track and identify compounds and processes. Vast amounts of data are collected in each measurement of current time-of-flight and orbitrap mass spectrometers using modern rapid data acquisition practices. However, compound identification remains as a major bottleneck during data analysis due to lacking reference libraries and analysis tools. Data-driven compound identification approaches could alleviate the problem, yet remain rare to non-existent in atmospheric science. In this perspective, we review the current state of data-driven compound identification with mass spectrometry in atmospheric science, and discuss current challenges and possible future steps towards a digital mass spectrometry era in atmospheric science.

physics.ao-ph↗

arXiv2023

Screening Mixed-Metal Sn$_2$M(III)Ch$_2$X$_3$ Chalcohalides for Photovoltaic Applications

Quaternary mixed-metal chalcohalides (Sn$_2$BCh$_2$X$_3$) are emerging as promising lead-free perovskite-inspired photovoltaic absorbers. Motivated by recent developments of a first Sn$_2$BCh$_2$X$_3$-based device, we used density functional theory to identify lead-free Sn$_2$BCh$_2$X$_3$ materials that are structurally and energetically stable within Cmcm, Cmc2$_1$ and P2$_1$/c space groups and have a band gap in the range of 0.7 to 2.0 eV to cover out- and indoor photovoltaic applications. A total of 27 Sn$_2$BCh$_2$X$_3$ materials were studied, including Sb, Bi, In for B-site, S, Se, Te for Ch-site and Cl, Br, I for X-site. We identified 12 materials with a direct band gap that meet our requirements, namely: Sn$_2$InS$_2$Br$_3$, Sn$_2$InS$_2$I$_3$, Sn$_2$InSe$_2$Cl$_3$, Sn$_2$InSe$_2$Br$_3$, Sn$_2$InTe$_2$Br$_3$, Sn$_2$InTe$_2$Cl$_3$, Sn$_2$SbS$_2$I$_3$, Sn$_2$SbSe$_2$Cl$_3$, Sn$_2$SbSe$_2$I$_3$, Sn$_2$SbTe$_2$Cl$_3$, Sn$_2$BiS$_2$I$_3$ and Sn$_2$BiTe$_2$Cl$_3$. A database scan reveals that 9 out of 12 are new compositions. For all 27 materials, P2$_1$/c is the thermodynamically preferred structure, followed by Cmc2$_1$. In Cmcm and Cmc2$_1$ mainly direct gaps occur, whereas mostly indirects in P2$_1$/c. To open up the possibility of band gap tuning in the future, we identified 12 promising Sn$_2$B$_{1-{a}}$B$'_{a}$Ch$_{2-{b}}$Ch$'_{b}$X$_{3-{c}}$X$_{c}$ alloys which fulfill our requirements and additional 69 materials by combining direct and indirect band gap compounds.

cond-mat.mtrl-sci↗

arXiv2023

Updates to the DScribe Library: New Descriptors and Derivatives

We present an update of the DScribe package, a Python library for atomistic descriptors. The update extends DScribe's descriptor selection with the Valle-Oganov materials fingerprint and provides descriptor derivatives to enable more advanced machine learning tasks, such as force prediction and structure optimization. For all descriptors, numeric derivatives are now available in DSribe. For the many-body tensor representation (MBTR) and the Smooth Overlap of Atomic Positions (SOAP), we have also implemented analytic derivatives. We demonstrate the effectiveness of the descriptor derivatives for machine learning models of Cu clusters and perovskite alloys.

cond-mat.mtrl-sci↗

arXiv2023

Structural disorder by octahedral tilting in inorganic halide perovskites: New insight with Bayesian optimization

Structural disorder is common in metal-halide perovskites and important for understanding the functional properties of these materials. First-principles methods can address structure variation on the atomistic scale, but they are often limited by the lack of structure-sampling schemes required to characterize the disorder. In this work, structural disorder in the benchmark inorganic halide perovskites CsPbI$_3^{}$ and CsPbBr$_3^{}$ is computationally studied in terms of the three octahedral-tilting angles. The consequent variation in energetics and properties are described by three-dimensional potential-energy surfaces (PESs) and property landscapes, delivered by Bayesian Optimization Structure Search method with integrated density-functional-theory (DFT) calculations. The rapid convergence of the PES with about 200 DFT data points in three-dimensional searches demonstrates the power of active learning and strategic sampling with Bayesian optimization. Further analysis indicates that disorder grows with increasing temperature, and reveals that the materials band gap at finite temperatures is a statistical mean over disordered structures.

cond-mat.mtrl-sci↗

arXiv2023

Single-atom dopants in plasmonic nanocatalysts

Bimetallic nanostructures combining plasmonic and catalytic metals are promising for tailoring and enhancing plasmonic hot-carrier generation utilized in plasmonic catalysis. In this work, we study the plasmonic hot-carrier generation in noble metal nanoparticles (Ag, Au, Cu) with single-atom dopants (Ag, Au, Cu, Pd, Pt) with first-principles time-dependent density-functional theory calculations. Our results show that the local hot-carrier generation at the dopant atom is significantly altered by the dopant element while the plasmonic response of the nanoparticle as a whole is not significantly affected. In particular, hot holes at the dopant atom originate from the discrete d-electron states of the dopant. The energies of these d-electron states, and hence those of the hot holes, depend on the dopant element, which opens up the possibility to tune hot-carrier generation with suitable dopants.

cond-mat.mes-hall↗

arXiv2022

Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining density-functional theory and $\boldsymbol{GW}$

We present a quantitatively accurate machine-learning (ML) model for the computational prediction of core-electron binding energies, from which x-ray photoelectron spectroscopy (XPS) spectra can be readily obtained. Our model combines density functional theory (DFT) with $GW$ and uses kernel ridge regression for the ML predictions. We apply the new approach to materials and molecules containing carbon, hydrogen and oxygen, and obtain qualitative and quantitative agreement with experiment, resolving spectral features within 0.1 eV of reference experimental spectra. The method only requires the user to provide a structural model for the material under study to obtain an XPS prediction within seconds. Our new tool is freely available online through the XPS Prediction Server.

cond-mat.mtrl-sci↗

arXiv2022

Benchmark of $\boldsymbol{GW}$ Methods for Core-Level Binding Energies

The $GW$ approximation has recently gained increasing attention as a viable method for the computation of deep core-level binding energies as measured by X-ray photoelectron spectroscopy (XPS). We present a comprehensive benchmark study of different $GW$ methodologies (starting-point optimized, partial and full eigenvalue-self-consistent, Hedin shift and renormalized singles) for molecular inner-shell excitations. We demonstrate that all methods yield a unique solution and apply them to the CORE65 benchmark set and ethyl trifluoroacetate. Three $GW$ schemes clearly outperform the other methods for absolute core-level energies with a mean absolute error of 0.3 eV with respect to experiment. These are partial eigenvalue self-consistency, in which the eigenvalues are only updated in the Green's function, single-shot $GW$ calculations based on an optimized hybrid functional starting point and a Hedin shift in the Green's function. While all methods reproduce the experimental relative binding energies well, the eigenvalue self-consistent schemes and the Hedin shift yield with mean errors $<0.2$ eV the best results.

physics.chem-ph↗

arXiv2022

Protective Coating Interfaces for perovskite Solar Cell Materials: A first Principles Study

The protection of halide perovskites is important for the performance and stability of emergent perovskite-based optoelectronic technologies. In this work, we investigate the potential inorganic protective coating materials ZnO, SrZrO3, and ZrO2 for the CsPbI3perovskite. The optimal interface registries are identified with Bayesian optimization. We then use semi-local density-functional theory (DFT) to determine the atomic structure at the interfaces of each coating material with the clean CsI-terminated surface and three reconstructed surface models with added PbI2and CsI complexes. For the final structures, we explore the level alignment at the interface with hybrid DFT calculations. Our analysis of the level alignment at the coating-substrate interfaces reveals no detrimental mid-gap states, but substrate-dependent valence and conduction band offsets. While ZnO and SrZrO3act as insulators on CsPbI3, ZrO2 might be suitable as electron transport layer with the right interface engineering.

cond-mat.mtrl-sci↗

arXiv2021

Surface reconstruction of tetragonal methylammonium lead triiodide

We present a detailed first-principles analysis of the (001) surface of methylammonium lead triiodide (MAPbI3). With density-functional theory we investigate the atomic and electronic structure of the tetragonal (I4cm) phase of MAPbI3. We analysed surfaces models with MAI- (MAI-T) and PbI2-terminations(PbI2-T). For both terminations, we studied the clean-surface and a series of surface reconstructions. We find that the clean MAI-T model is more stable than its PbI2-T counterpart. For the MAI termination,reconstructions with added or removed units of nonpolar MAI and PbI2 are most stable. The corresponding band structures reveal surface states originating from the conduction band. Despite the presence of such additional surface states, our stable reconstructed surface models do not introduce new states within the band gap.

cond-mat.mtrl-sci↗

arXiv2021

Electronic Characterization of a Charge-Transfer Complex Monolayer on Graphene

Organic charge-transfer complexes (CTCs) formed by strong electron acceptor and strong electron donor molecules are known to exhibit exotic effects such as superconductivity and charge density waves. We present a low-temperature scanning tunneling microscopy and spectroscopy (LT-STM/STS) study of a two-dimensional (2D) monolayer CTC of tetrathiafulvalene (TTF) and fluorinated tetracyanoquinodimethane (F4TCNQ), self-assembled on the surface of oxygen-intercalated epitaxial graphene on Ir(111) (G/O/Ir(111)). We confirm the formation of the charge-transfer complex by dI/dV spectroscopy and direct imaging of the singly-occupied molecular orbitals. High-resolution spectroscopy reveals a gap at zero bias, suggesting the formation of a correlated ground state at low temperatures. These results point to the possibility to realize and study correlated ground states in charge-transfer complex monolayers on weakly interacting surfaces.

cond-mat.mes-hall↗

arXiv2021

Assessing the $G_0W_0Γ^{(1)}_0$ approach: Beyond $G_0W_0$ with Hedin's full second-order self-energy contribution

We present and benchmark a self-energy approach for quasiparticle energy calculations that goes beyond Hedin's $GW$ approximation by adding the full second-order self-energy (FSOS-$W$) contribution. The FSOS-$W$ diagram involves two screened Coulomb interaction ($W$) lines and adding the FSOS-$W$ to the $GW$ self-energy can be interpreted as first-order vertex correction to $GW$ ($GWΓ^{(1)}$). Our FSOS-$W$ implementation is based on the resolution-of-identity technique and exhibits better than $O(N^5)$ scaling with system size for small to medium-sized molecules. We then present one-shot $GWΓ^{(1)}$ ($G_0W_0Γ^{(1)}_0$) benchmarks for the $GW$100 test set and a set of 24 acceptor molecules. For semilocal or hybrid density functional theory starting points, $G_0W_0Γ^{(1)}_0$ systematically outperforms $G_0W_0$ for the first vertical ionization potentials (vIPs) and electron affinities (vEAs) of both test sets. Finally, we demonstrate that a static FSOS-$W$ self-energy significantly underestimates the quasiparticle energies.

cond-mat.mtrl-sci↗

arXiv2021

Integrating Bayesian Inference with Scanning Probe Experiments for Robust Identification of Surface Adsorbate Configurations

Controlling the properties of organic/inorganic materials requires detailed knowledge of their molecular adsorption geometries. This is often unattainable, even with current state-of-the-art tools. Visualizing the structure of complex non-planar adsorbates with atomic force microscopy (AFM) is challenging, and identifying it computationally is intractable with conventional structure search. In this fresh approach, cross-disciplinary tools are integrated for a robust and automated identification of 3D adsorbate configurations. Bayesian optimization is employed with first-principles simulations for accurate and unbiased structure inference of multiple adsorbates. The corresponding AFM simulations then allow fingerprinting adsorbate structures that appear in AFM experimental images. In the instance of bulky (1S)-camphor adsorbed on the Cu(111) surface, three matching AFM image contrasts are found, which allow correlating experimental image features to distinct cases of molecular adsorption.

cond-mat.mtrl-sci↗

arXiv2020

Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization

Identifying the atomic structure of organic-inorganic interfaces is challenging with our current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search (BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates. We apply BOSS with density-functional theory simulations to detect the stable adsorbate structures of (1S)-camphor on the Cu(111) surface. We identify the optimal structure among 8 unique types of stable adsorbates, in which camphor chemisorbs via oxygen (global minimum) or physisorbs via hydrocarbons to the Cu(111) surface. This study demonstrates that new cross-disciplinary tools, like BOSS, facilitate the description of complex surface structures and their properties, and ultimately allow us to tune the functionality of advanced materials.

cond-mat.mtrl-sci↗

arXiv2020

Atomic and electronic structure of cesium lead triiodide surfaces

The (001) surface of the emerging photovoltaic material cesium lead triiodide (CsPbI3 ) is studied. Using first-principles methods, we investigate the atomic and electronic structure of cubic (α) and orthorhombic (γ) CsPbI3 . For both phases, we find that CsI-termination is more stable than PbI2-termination. For the CsI-terminated surface, we then compute and analyse the surface phase diagram. We observe that surfaces with added or removed units of nonpolar CsI and PbI2 are most stable. The corresponding band structures reveal that the α phase exhibits surface states that derive from the conduction band. The surface reconstructions do not introduce new states in the band gap of CsPbI3, but for the α phase we find additional surface states at the conduction band edge.

cond-mat.mtrl-sci↗

arXiv2020

Predicting Gas-Particle Partitioning Coefficients of Atmospheric Molecules with Machine Learning

The formation, properties and lifetime of secondary organic aerosols in the atmosphere are largely determined by gas-particle partitioning coefficients of the participating organic vapours. Since these coefficients are often difficult to measure or compute, we developed a machine learning (ML) model to predict them given molecular structure as input. Our data-driven approach is based on the dataset by Wang et al. (Atmos. Chem. Phys., 17, 7529 (2017)), who computed the partitioning coefficients and saturation vapour pressures of 3414 atmospheric oxidation products from the master chemical mechanism using the COSMOtherm program. We train a kernel ridge regression (KRR) ML model on the saturation vapour pressure ($P_{sat}$), and on two equilibrium partitioning coefficients: between a water-insoluble organic matter phase and the gas phase ($K_{WIOM/G}$), and between an infinitely dilute solution with pure water and the gas phase ($K_{W/G}$). For the input representation of the atomic structure of each organic molecule to the machine, we test different descriptors. Our best ML model predicts $P_{sat}$ and $K_{WIOM/G}$ to within 0.3 and $K_{W/G}$ to within 0.4 logarithmic units of the original COSMOtherm calculations. This is equal or better than the typical accuracy of COSMOtherm predictions compared to experimental data. We then apply our ML model to a dataset of 35,383 molecules that we generated based on a carbon 10 backbone and functionalized with 0 to 6 carboxyl, carbonyl or hydroxyl groups to evaluate its performance for polyfunctional compounds with potentially low $P_{sat}$. The resulting $P_{sat}$ and partitioning coefficient distributions were physico-chemically reasonable, and the volatility predictions for the most highly oxidized compounds were in qualitative agreement with experimentally inferred volatilities of atmospheric oxidation products with similar elemental composition.

physics.chem-ph↗

arXiv2020

Electron-phonon coupling in d-electron solids: A temperature dependent study of rutile TiO2 by first-principles theory and two-photon photoemission

Rutile TiO2 is a paradigmatic transition metal oxide with applications in optics, electronics, photocatalysis, etc., that are subject to pervasive electron-phonon interaction. To understand how energies of its electronic bands, and in general semiconductors or metals where the frontier orbitals have a strong d-band character, depend on temperature, we perform a comprehensive theoretical and experimental study of the effects of electron-phonon (e-p) interactions. In a two-photon photoemission (2PP) spectroscopy study we observe an unusual temperature dependence of electronic band energies within the conduction band of reduced rutile TiO2, which is contrary to the well understood sp-band semiconductors and points to a so far unexplained dichotomy in how the e-p interactions affect differently the materials where the frontier orbitals are derived from the sp- and d-orbitals. To develop a broadly applicable model, we employ state-of-the-art first-principles calculations that explain how phonons promote interactions between the Ti-3d orbitals of the conduction band within the octahedral crystal field. The characteristics differences in e-p interactions experienced by the Ti 3d-orbitals of rutile TiO2 crystal lattice are contrasted with the more familiar behavior of the Si 2s-orbitals of stishovite SiO2 polymorph, in which the frontier 2s-orbital experience a similar crystal field with the opposite effect...

cond-mat.mtrl-sci↗