cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:9g-11
title: Temperature dependence of electronic eigenenergies in the adiabatic harmonic approximation

description: The renormalization of electronic eigenenergies due to electron-phonon interactions (temperature dependence and zero-point motion effect) is important in many materials. We address it in the adiabatic harmonic approximation, based on first principles (e.g., density-functional theory), from different points of view: directly from atomic position fluctuations or, alternatively, from Janak's theorem generalized to the case where the Helmholtz free energy, including the vibrational entropy, is used. We prove their equivalence, based on the usual form of Janak's theorem and on the dynamical equation. We then also place the Allen-Heine-Cardona (AHC) theory of the renormalization in a first-principles context. The AHC theory relies on the rigid-ion approximation, and naturally leads to a self-energy (Fan) contribution and a Debye-Waller contribution. Such a splitting can also be done for the complete harmonic adiabatic expression, in which the rigid-ion approximation is not required. A numerical study within the density-functional perturbation theory framework allows us to compare the AHC theory with frozen-phonon calculations, with or without the rigid-ion approximation. For the two different numerical approaches without non-rigid-ion terms, the agreement is better than 7 μeV in the case of diamond, which represent an agreement to five significant digits. The magnitude of the non-rigid-ion terms in this case is also presented, distinguishing specific phonon modes contributions to different electronic eigenenergies.
DOI: 10.24435/materialscloud:3t-1w
title: ONETEP + TOSCAM: uniting dynamical mean field theory and linear-scaling density functional theory

description: We introduce the unification of dynamical mean field theory (DMFT) and linear-scaling density functional theory (DFT), as recently implemented in ONETEP, a linear-scaling DFT package, and TOSCAM, a DMFT toolbox. This code can account for strongly correlated electronic behavior while simultaneously including the effects of the environment, making it ideally suited for studying complex and heterogeneous systems that contain transition metals and lanthanides, such as metalloproteins. We systematically introduce the necessary formalism, which must account for the non-orthogonal basis set used by ONETEP. In order to demonstrate the capabilities of this code, we apply it to carbon monoxide-ligated iron porphyrin and explore the distinctly quantum-mechanical character of the iron 3d electrons during the process of photodissociation.

This archive record contains example input and output files for the DFT, DFT+U, and DFT+DMFT calculations presented in the associated journal article.
DOI: 10.24435/materialscloud:hh-c1
title: Light-induced renormalization of the Dirac quasiparticles in the nodal-line semimetal ZrSiSe

description: In nodal-line semimetals, linearly dispersing states form Dirac loops in the reciprocal space with a high degree of electron-hole symmetry and a reduced density of states near the Fermi level. The result is reduced electronic screening and enhanced correlations between Dirac quasiparticles. Here we investigate the electronic structure of ZrSiSe, by combining time- and angle-resolved photoelectron spectroscopy with ab initio density functional theory (DFT) complemented by an extended Hubbard model (DFT+U+V) and by time-dependent DFT+U+V. We show that electronic correlations are reduced on an ultrashort timescale by optical excitation of high-energy electrons-hole pairs, which transiently screen the Coulomb interaction. Our findings demonstrate an all-optical method for engineering the band structure of a quantum material.

This record contains the ARPES raw data in txt format used to create the figures in the referenced publication.
DOI: 10.24435/materialscloud:jc-rm
title: Controlled selectivity for ethanol steam reforming reaction over doped CeO2 surfaces: The role of gallium

description: The ethanol steam reforming reaction, together with the adsorption and decomposition of ethanol was studied on CeO2 and gallium-doped ceria (CeGaOx) by a combined experimental and theoretical approach using infrared spectroscopy (IR), mass spectrometry (MS) and density functional theory (DFT) calculations. At 100 °C, different types of monodentate ethoxy species were identified as standing-up (SU) on Ce4+ and lying-down (LD) on Ce4+ and Ga3+, with the alkyl chain more perpendicular or parallel to the surface, respectively. It is suggested that the incorporation of Ga into the ceria lattice changes the decomposition pathway of LD species, which converts to acetate instead of ethylene, attributed to the increased lattice oxygen lability in the Ce―O―Ga interface upon doping and the propensity to form Ga―H surface species. Under ethanol steam reforming conditions, Ga doping of ceria-based materials has a drastic effect by improving the H2:CO2 ratio, changing the product distribution and reducing coke formation.
DOI: 10.24435/materialscloud:w5-tk
title: Light-induced renormalization of the Dirac quasiparticles in the nodal-line semimetal ZrSiSe

description: In nodal-line semimetals, linearly dispersing states form Dirac loops in the reciprocal space with a high degree of electron-hole symmetry and a reduced density of states near the Fermi level. The result is reduced electronic screening and enhanced correlations between Dirac quasiparticles. Here we investigate the electronic structure of ZrSiSe, by combining time- and angle-resolved photoelectron spectroscopy with ab initio density functional theory (DFT) complemented by an extended Hubbard model (DFT+U+V) and by time-dependent DFT+U+V. We show that electronic correlations are reduced on an ultrashort timescale by optical excitation of high-energy electrons-hole pairs, which transiently screen the Coulomb interaction. Our findings demonstrate an all-optical method for engineering the band structure of a quantum material.

This record contains the ARPES raw data in txt format used to create the figures in the referenced publication.
DOI: 10.24435/materialscloud:7q-1c
title: Controlled selectivity for ethanol steam reforming reaction over doped CeO2 surfaces: The role of gallium

description: The ethanol steam reforming reaction, together with the adsorption and decomposition of ethanol was studied on CeO2 and gallium-doped ceria (CeGaOx) by a combined experimental and theoretical approach using infrared spectroscopy (IR), mass spectrometry (MS) and density functional theory (DFT) calculations. At 100 °C, different types of monodentate ethoxy species were identified as standing-up (SU) on Ce4+ and lying-down (LD) on Ce4+ and Ga3+, with the alkyl chain more perpendicular or parallel to the surface, respectively. It is suggested that the incorporation of Ga into the ceria lattice changes the decomposition pathway of LD species, which converts to acetate instead of ethylene, attributed to the increased lattice oxygen lability in the Ce―O―Ga interface upon doping and the propensity to form Ga―H surface species. Under ethanol steam reforming conditions, Ga doping of ceria-based materials has a drastic effect by improving the H2:CO2 ratio, changing the product distribution and reducing coke formation.
DOI: 10.24435/materialscloud:e8-aq
title: Observation of fractional edge excitations in nanographene spin chains

description: Fractionalization is a phenomenon in which strong interactions in a quantum system drive the emergence of excitations with quantum numbers that are absent in the building blocks. Outstanding examples are excitations with charge e/3 in the fractional quantum Hall effect, solitons in one-dimensional conducting polymers and Majorana states in topological superconductors. Fractionalization is also predicted to manifest itself in low-dimensional quantum magnets, such as one-dimensional antiferromagnetic S = 1 chains. The fundamental features of this system are gapped excitations in the bulk and, remarkably, S = 1/2 edge states at the chain termini, leading to a four-fold degenerate ground state that reflects the underlying symmetry-protected topological order. This record contains data to support the result in a recent publication of ours where we use on-surface synthesis to fabricate one-dimensional spin chains that contain the S = 1 polycyclic aromatic hydrocarbon triangulene as the building block. Using scanning tunneling microscopy and spectroscopy at 4.5 K, we probe length-dependent magnetic excitations at the atomic scale in both open-ended and cyclic spin chains, and directly observe gapped spin excitations and fractional edge states therein. Exact diagonalization calculations provide conclusive evidence that the spin chains are described by the S = 1 bilinear-biquadratic Hamiltonian in the Haldane symmetry-protected topological phase. Our results open a bottom-up approach to study strongly correlated phases in purely organic materials, with the potential for the realization of measurement-based quantum computation.
DOI: 10.24435/materialscloud:hp-1a
title: Observation of fractional edge excitations in nanographene spin chains

description: Fractionalization is a phenomenon in which strong interactions in a quantum system drive the emergence of excitations with quantum numbers that are absent in the building blocks. Outstanding examples are excitations with charge e/3 in the fractional quantum Hall effect, solitons in one-dimensional conducting polymers and Majorana states in topological superconductors. Fractionalization is also predicted to manifest itself in low-dimensional quantum magnets, such as one-dimensional antiferromagnetic S = 1 chains. The fundamental features of this system are gapped excitations in the bulk and, remarkably, S = 1/2 edge states at the chain termini, leading to a four-fold degenerate ground state that reflects the underlying symmetry-protected topological order. This record contains data to support the result in a recent publication of ours where we use on-surface synthesis to fabricate one-dimensional spin chains that contain the S = 1 polycyclic aromatic hydrocarbon triangulene as the building block. Using scanning tunneling microscopy and spectroscopy at 4.5 K, we probe length-dependent magnetic excitations at the atomic scale in both open-ended and cyclic spin chains, and directly observe gapped spin excitations and fractional edge states therein. Exact diagonalization calculations provide conclusive evidence that the spin chains are described by the S = 1 bilinear-biquadratic Hamiltonian in the Haldane symmetry-protected topological phase. Our results open a bottom-up approach to study strongly correlated phases in purely organic materials, with the potential for the realization of measurement-based quantum computation.
DOI: 10.24435/materialscloud:d3-3r
title: Machine learning for metallurgy: a neural network potential for Al-Cu

description: High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic-scale phenomena, particularly during early-stage nucleation and growth.  Atomistic modeling using interatomic potentials is a desirable tool for understanding the detailed phenomena involved in precipitation and strengthening, which requires length and time scales far larger than those accessible by first-principles methods.  Current interatomic potentials for alloys are not, however, sufficiently accurate for such studies.  Here, a family of neural-network potentials (NNPs) for the Al-Cu system is presented as a first example of a machine-learning potential that can achieve near-first-principles accuracy for many different metallurgically-important aspects of this alloy.  High fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, generalized stacking fault energies, and surfaces for slip in matrix and precipitates, antisite defect energies, and other quantities, are shown.  The NNPs also capture the subtle entropically-induced transition between θ' and θ at temperatures around 600K.  Many comparisons are made with the state-of-the-art Angular-Dependent Potential for Al-Cu, demonstrating the significant quantitative benefit of a machine-learning approach.  A preliminary kinetic Monte Carlo study shows the NNP to predict the emergence of GP zones in Al 4%Cu at T=300K in agreement with experiments.  The NNP shows some significant transferability to defects and properties outside the structures used to develop the NNP but also shows some errors, highlighting that the use of any interatomic potential requires careful validation in application to specific metallurgical problems of interest.
DOI: 10.24435/materialscloud:ws-7t
title: Unified mechanistic understanding of CO2 reduction to CO on transition metal and single atom catalysts

description: CO is the simplest product from CO2 electroreduction (CO2R), but the identity and nature of its rate limiting step remains controversial. Here we investigate the activity of both transition metals (TMs) and metal-nitrogen doped carbon catalysts (MNCs), and a present unified mechanistic picture of CO2R for both these classes of catalysts. By consideration of the electronic structure through a Newns-Andersen model, we find that on MNCs, like TMs, electron transfer to CO2 is facile, such that CO2 (g) adsorption is driven by adsorbate dipole-field interactions. Using density functional theory with explicit consideration of the interfacial field, we find CO2 * adsorption to generally be limiting on TMs, while MNCs can be limited by either CO2* adsorption or by the proton-electron transfer reaction to form COOH*. We evaluate these computed mechanisms against pH-dependent experimental activity measurements on CO2R to CO activity for Au, FeNC, and NiNC. We present a unified activity volcano that, in contrast to previous analyses, includes the decisive CO2* and COOH* binding strengths as well as the critical adsorbate dipole-field interactions. This dataset contains all DFT calculations used in this work.
DOI: 10.24435/materialscloud:d7-bm
title: Unified mechanistic understanding of CO2 reduction to CO on transition metal and single atom catalysts

description: CO is the simplest product from CO2 electroreduction (CO2R), but the identity and nature of its rate limiting step remains controversial. Here we investigate the activity of both transition metals (TMs) and metal-nitrogen doped carbon catalysts (MNCs), and a present unified mechanistic picture of CO2R for both these classes of catalysts. By consideration of the electronic structure through a Newns-Andersen model, we find that on MNCs, like TMs, electron transfer to CO2 is facile, such that CO2 (g) adsorption is driven by adsorbate dipole-field interactions. Using density functional theory with explicit consideration of the interfacial field, we find CO2 * adsorption to generally be limiting on TMs, while MNCs can be limited by either CO2* adsorption or by the proton-electron transfer reaction to form COOH*. We evaluate these computed mechanisms against pH-dependent experimental activity measurements on CO2R to CO activity for Au, FeNC, and NiNC. We present a unified activity volcano that, in contrast to previous analyses, includes the decisive CO2* and COOH* binding strengths as well as the critical adsorbate dipole-field interactions. This dataset contains all DFT calculations used in this work.
DOI: 10.24435/materialscloud:6k-je
title: Machine learning for metallurgy: a neural network potential for Al-Cu

description: High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic-scale phenomena, particularly during early-stage nucleation and growth.  Atomistic modeling using interatomic potentials is a desirable tool for understanding the detailed phenomena involved in precipitation and strengthening, which requires length and time scales far larger than those accessible by first-principles methods.  Current interatomic potentials for alloys are not, however, sufficiently accurate for such studies.  Here, a family of neural-network potentials (NNPs) for the Al-Cu system is presented as a first example of a machine-learning potential that can achieve near-first-principles accuracy for many different metallurgically-important aspects of this alloy.  High fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, generalized stacking fault energies, and surfaces for slip in matrix and precipitates, antisite defect energies, and other quantities, are shown.  The NNPs also capture the subtle entropically-induced transition between θ' and θ at temperatures around 600K.  Many comparisons are made with the state-of-the-art Angular-Dependent Potential for Al-Cu, demonstrating the significant quantitative benefit of a machine-learning approach.  A preliminary kinetic Monte Carlo study shows the NNP to predict the emergence of GP zones in Al 4%Cu at T=300K in agreement with experiments.  The NNP shows some significant transferability to defects and properties outside the structures used to develop the NNP but also shows some errors, highlighting that the use of any interatomic potential requires careful validation in application to specific metallurgical problems of interest.
DOI: 10.24435/materialscloud:w5-1e
title: Machine learning for metallurgy: a neural network potential for Al-Cu

description: High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic-scale phenomena, particularly during early-stage nucleation and growth.  Atomistic modeling using interatomic potentials is a desirable tool for understanding the detailed phenomena involved in precipitation and strengthening, which requires length and time scales far larger than those accessible by first-principles methods.  Current interatomic potentials for alloys are not, however, sufficiently accurate for such studies.  Here, a family of neural-network potentials (NNPs) for the Al-Cu system is presented as a first example of a machine-learning potential that can achieve near-first-principles accuracy for many different metallurgically-important aspects of this alloy.  High fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, generalized stacking fault energies, and surfaces for slip in matrix and precipitates, antisite defect energies, and other quantities, are shown.  The NNPs also capture the subtle entropically-induced transition between θ' and θ at temperatures around 600K.  Many comparisons are made with the state-of-the-art Angular-Dependent Potential for Al-Cu, demonstrating the significant quantitative benefit of a machine-learning approach.  A preliminary kinetic Monte Carlo study shows the NNP to predict the emergence of GP zones in Al 4%Cu at T=300K in agreement with experiments.  The NNP shows some significant transferability to defects and properties outside the structures used to develop the NNP but also shows some errors, highlighting that the use of any interatomic potential requires careful validation in application to specific metallurgical problems of interest.
DOI: 10.24435/materialscloud:yz-db
title: Machine learning for metallurgy: a neural network potential for Al-Cu

description: High-strength metal alloys achieve their performance via careful control of precipitates and solutes. The nucleation, growth, and kinetics of precipitation, and the resulting mechanical properties, are inherently atomic-scale phenomena, particularly during early-stage nucleation and growth.  Atomistic modeling using interatomic potentials is a desirable tool for understanding the detailed phenomena involved in precipitation and strengthening, which requires length and time scales far larger than those accessible by first-principles methods.  Current interatomic potentials for alloys are not, however, sufficiently accurate for such studies.  Here, a family of neural-network potentials (NNPs) for the Al-Cu system is presented as a first example of a machine-learning potential that can achieve near-first-principles accuracy for many different metallurgically-important aspects of this alloy.  High fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, generalized stacking fault energies, and surfaces for slip in matrix and precipitates, antisite defect energies, and other quantities, are shown.  The NNPs also capture the subtle entropically-induced transition between θ' and θ at temperatures around 600K.  Many comparisons are made with the state-of-the-art Angular-Dependent Potential for Al-Cu, demonstrating the significant quantitative benefit of a machine-learning approach.  A preliminary kinetic Monte Carlo study shows the NNP to predict the emergence of GP zones in Al 4%Cu at T=300K in agreement with experiments.  The NNP shows some significant transferability to defects and properties outside the structures used to develop the NNP but also shows some errors, highlighting that the use of any interatomic potential requires careful validation in application to specific metallurgical problems of interest.
DOI: 10.24435/materialscloud:m4-by
title: Band alignment at the CaF2/Si(111) interface through advanced electronic structure calculations

description: We determine the band alignment at the CaF2/Si(111) interface through various advanced electronic-structure methods. This interface is experimentally well-studied and serves as an ideal test case to examine the accuracy of theoretical schemes. We use both global and range-separated hybrid functionals as well as GW calculations including self-consistency and vertex corrections. Our calculation procedure accounts for residual strain resulting from the small mismatch in the lateral lattice constants at the interface to minimize the systematic error in the comparison with experiment. Both the hybrid-functional and the GW schemes give band alignments in overall good agreement with the experimental characterization. However, the considered methods yield sizable variations in the calculated band offsets, which do not originate from incorrect evaluations of the band gaps but rather from different inherent relative positions of the band edges. The comparison with experiment reveals that the global hybrid functional and the quasiparticle self-consistent GW with vertex corrections give the most accurate description of the band alignment. We then determine the variation of the band offsets as a function of the amount of excess fluorine at the interface and attribute the experimental spread in the measured offsets to uncontrolled fluorine contamination.