cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:b8-bk
title: The role of metal adatoms in a surface-assisted cyclodehydrogenation reaction on a gold surface

description: Dehydrogenation reactions are key steps in many metal-catalyzed chemical processes and in the on-surface synthesis of atomically precise nanomaterials. The principal role of the metal substrate in these reactions is undisputed, but the role of metal adatoms remains, to a large extent, unanswered, particularly on gold substrates. In a recent publication, we discuss their importance by studying the surface-assisted cyclodehydrogenation on Au(111) as an ideal model case. We choose a polymer theoretically predicted to give one of two cyclization products depending on the presence or absence of gold adatoms. Scanning probe microscopy experiments observe only the product associated with adatoms. We challenge the prevalent understanding of surface-assisted cyclodehydrogenation, unveiling the catalytic role of adatoms and their effect on regioselectivity. The study adds new perspectives to the understanding of metal catalysis and the design of on-surface synthesis protocols for novel carbon nanomaterials. The record contains data for the CP2K-based calculations that support the results.
DOI: 10.24435/materialscloud:px-wh
title: Heat conductivity from energy-density fluctuations

description: We present a method, based on the classical Green-Kubo theory of linear response, to compute the heat conductivity of extended systems, leveraging energy-density, rather than energy-current, fluctuations, thus avoiding the need to devise an analytical expression for the macroscopic energy flux. The implementation of this method requires the evaluation of the long-wavelength and low-frequency limits of a suitably defined correlation function, which we perform using a combination of recently-introduced cepstral-analysis and Bayesian extrapolation techniques. Our methodology is demonstrated against standard current-based Green-Kubo results for liquid argon and water, and solid amorphous Silica, and compared with a recently proposed similar technique, which utilizes mass-density, instead of energy-density, fluctuations.
DOI: 10.24435/materialscloud:ek-ey
title: The role of metal adatoms in a surface-assisted cyclodehydrogenation reaction on a gold surface

description: Dehydrogenation reactions are key steps in many metal-catalyzed chemical processes and in the on-surface synthesis of atomically precise nanomaterials. The principal role of the metal substrate in these reactions is undisputed, but the role of metal adatoms remains, to a large extent, unanswered, particularly on gold substrates. In a recent publication, we discuss their importance by studying the surface-assisted cyclodehydrogenation on Au(111) as an ideal model case. We choose a polymer theoretically predicted to give one of two cyclization products depending on the presence or absence of gold adatoms. Scanning probe microscopy experiments observe only the product associated with adatoms. We challenge the prevalent understanding of surface-assisted cyclodehydrogenation, unveiling the catalytic role of adatoms and their effect on regioselectivity. The study adds new perspectives to the understanding of metal catalysis and the design of on-surface synthesis protocols for novel carbon nanomaterials. The record contains data for the CP2K-based calculations that support the results.
DOI: 10.24435/materialscloud:ka-x7
title: Machine learning molecular dynamics simulation of CO-driven formation of Cu clusters on the Cu(111) surface

description: The behavior of adsorbate-induced surface transformation can be clearly understood given the mechanical aspects of such phenomenon are well described at the atomic level. In this study, we provide the atomic-level description on the formation of Cu clusters on the Cu(111) surface by performing set of molecular dynamics simulations driven by machine-learning force-field. The machine learning technique called Gaussian Process (GP), as implemented in FLARE v1.1.2 (https://github.com/mir-group/flare/tree/1.1.2) was used to construct the machine-learning force-field. The dynamics simulations were performed using LAMMPS v29Sept2021 (https://github.com/lammps/lammps/tree/stable_29Sep2021_update2). This archive contains some supplementary data including the validation structures and also the MGP potential used to drive the MD simulations in LAMMPS. Additionally, the GP potential (before mapping) containing the database of atomic environment is also made available. 

The simulations at 450 K–550 K show clusters are formed within a hundred of ns when the Cu surface is exposed with CO. On the other hand, no cluster is formed within the same time interval on the clean Cu surface even at 550 K, which signifies the importance of CO exposure to the surface transformation. The effect of temperature to the formation of clusters is also investigated. The CO-decorated Cu clusters ranging from dimer to hexamer are detected within a hundred of ns at 450 K. Lowering the temperature to 350 K does not result in the formation of clusters within a hundred ns due to the scarce detachments of adatom, while raising the temperature to 550 K results in the formation of more clusters, ranging from dimer to heptamer, but with shorter lifetimes. The clusters can be formed directly through instantaneous detachment of a group of step-atoms or indirectly by aggregation of wandering Cu monomers and smaller clusters on the surface terrace. The preference to the indirect mechanism is indicated by the higher frequency of its occurrence. More details on the procedures and results of the research can be found in the paper.
DOI: 10.24435/materialscloud:5k-vh
title: Crystallization kinetics of nanoconfined GeTe slabs in GeTe/TiTe-like superlattices for phase change memories

description: Superlattices made of alternating blocks of the phase change compound Sb₂Te₃ and of TiTe₂ confining layers have been recently proposed for applications in neuromorphic devices. The Sb₂Te₃/TiTe₂ heterostructure allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. However, Sb₂Te₃ suffers from a low data retention due to a low crystallization temperature Tx. Substituting Sb₂Te₃ with a phase change compound with a higher Tx, such as GeTe, seems an interesting option in this respect. Nanoconfinement might, however, alters the crystallization kinetics with respect to the bulk. In this work, we investigated the crystallization process of GeTe nanoconfined in geometries mimicking GeTe/TiTe₂ superlattices by means of molecular dynamics simulations with a machine learning potential. The simulations reveal that nanoconfinement induces a mild reduction in the crystal growth velocities which would not hinder the application of GeTe/TiTe₂ heterostructures in neuromorphic devices with superior data retention.
DOI: 10.24435/materialscloud:qj-ca
title: Machine learning molecular dynamics simulation of CO-driven formation of Cu clusters on the Cu(111) surface

description: The behavior of adsorbate-induced surface transformation can be clearly understood given the mechanical aspects of such phenomenon are well described at the atomic level. In this study, we provide the atomic-level description on the formation of Cu clusters on the Cu(111) surface by performing set of molecular dynamics simulations driven by machine-learning force-field. The machine learning technique called Gaussian Process (GP), as implemented in FLARE v1.1.2 (https://github.com/mir-group/flare/tree/1.1.2) was used to construct the machine-learning force-field. The dynamics simulations were performed using LAMMPS v29Sept2021 (https://github.com/lammps/lammps/tree/stable_29Sep2021_update2). This archive contains some supplementary data including the validation structures and also the MGP potential used to drive the MD simulations in LAMMPS. Additionally, the GP potential (before mapping) containing the database of atomic environment is also made available. 

The simulations at 450 K–550 K show clusters are formed within a hundred of ns when the Cu surface is exposed with CO. On the other hand, no cluster is formed within the same time interval on the clean Cu surface even at 550 K, which signifies the importance of CO exposure to the surface transformation. The effect of temperature to the formation of clusters is also investigated. The CO-decorated Cu clusters ranging from dimer to hexamer are detected within a hundred of ns at 450 K. Lowering the temperature to 350 K does not result in the formation of clusters within a hundred ns due to the scarce detachments of adatom, while raising the temperature to 550 K results in the formation of more clusters, ranging from dimer to heptamer, but with shorter lifetimes. The clusters can be formed directly through instantaneous detachment of a group of step-atoms or indirectly by aggregation of wandering Cu monomers and smaller clusters on the surface terrace. The preference to the indirect mechanism is indicated by the higher frequency of its occurrence. More details on the procedures and results of the research can be found in the paper.
DOI: 10.24435/materialscloud:85-kr
title: Crystallization kinetics of nanoconfined GeTe slabs in GeTe/TiTe-like superlattices for phase change memories

description: Superlattices made of alternating blocks of the phase change compound Sb₂Te₃ and of TiTe₂ confining layers have been recently proposed for applications in neuromorphic devices. The Sb₂Te₃/TiTe₂ heterostructure allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. However, Sb₂Te₃ suffers from a low data retention due to a low crystallization temperature Tx. Substituting Sb₂Te₃ with a phase change compound with a higher Tx, such as GeTe, seems an interesting option in this respect. Nanoconfinement might, however, alters the crystallization kinetics with respect to the bulk. In this work, we investigated the crystallization process of GeTe nanoconfined in geometries mimicking GeTe/TiTe₂ superlattices by means of molecular dynamics simulations with a machine learning potential. The simulations reveal that nanoconfinement induces a mild reduction in the crystal growth velocities which would not hinder the application of GeTe/TiTe₂ heterostructures in neuromorphic devices with superior data retention.
DOI: 10.24435/materialscloud:kb-wq
title: Crystallization kinetics of nanoconfined GeTe slabs in GeTe/TiTe-like superlattices for phase change memories

description: Superlattices made of alternating blocks of the phase change compound Sb₂Te₃ and of TiTe₂ confining layers have been recently proposed for applications in neuromorphic devices. The Sb₂Te₃/TiTe₂ heterostructure allows for a better control of multiple intermediate resistance states and for a lower drift with time of the electrical resistance of the amorphous phase. However, Sb₂Te₃ suffers from a low data retention due to a low crystallization temperature Tx. Substituting Sb₂Te₃ with a phase change compound with a higher Tx, such as GeTe, seems an interesting option in this respect. Nanoconfinement might, however, alters the crystallization kinetics with respect to the bulk. In this work, we investigated the crystallization process of GeTe nanoconfined in geometries mimicking GeTe/TiTe₂ superlattices by means of molecular dynamics simulations with a machine learning potential. The simulations reveal that nanoconfinement induces a mild reduction in the crystal growth velocities which would not hinder the application of GeTe/TiTe₂ heterostructures in neuromorphic devices with superior data retention.
DOI: 10.24435/materialscloud:g2-7d
title: Massively parallel implementation of gradients within the Random Phase Approximation: Application to the polymorphs of benzene

description: The Random-Phase approximation (RPA) provides an appealing framework for semi-local density functional theory. In its current formulation, it is cost-effective and has a better scaling behaviour compared to other wavefunction based correlation methods. To broaden the application field for RPA, it is necessary to have first order properties available. RPA nuclear gradients allow for structure optimizations and data sampling for machine learning applications. We report on an efficient implementation of RPA nuclear gradients for massively parallel computers. We apply the implementation to two polymorphs of the benzene crystal obtaining very good cohesive and relative energies. Different correction and extrapolation schemes are investigated for further improvement of the results and in order to estimate error bars.
DOI: 10.24435/materialscloud:1e-e0
title: Massively parallel implementation of gradients within the Random Phase Approximation: Application to the polymorphs of benzene

description: The Random-Phase approximation (RPA) provides an appealing framework for semi-local density functional theory. In its current formulation, it is cost-effective and has a better scaling behaviour compared to other wavefunction based correlation methods. To broaden the application field for RPA, it is necessary to have first order properties available. RPA nuclear gradients allow for structure optimizations and data sampling for machine learning applications. We report on an efficient implementation of RPA nuclear gradients for massively parallel computers. We apply the implementation to two polymorphs of the benzene crystal obtaining very good cohesive and relative energies. Different correction and extrapolation schemes are investigated for further improvement of the results and in order to estimate error bars.
DOI: 10.24435/materialscloud:4s-yq
title: Massively parallel implementation of gradients within the Random Phase Approximation: Application to the polymorphs of benzene

description: The Random-Phase approximation (RPA) provides an appealing framework for semi-local density functional theory. In its current formulation, it is cost-effective and has a better scaling behaviour compared to other wavefunction based correlation methods. To broaden the application field for RPA, it is necessary to have first order properties available. RPA nuclear gradients allow for structure optimizations and data sampling for machine learning applications. We report on an efficient implementation of RPA nuclear gradients for massively parallel computers. We apply the implementation to two polymorphs of the benzene crystal obtaining very good cohesive and relative energies. Different correction and extrapolation schemes are investigated for further improvement of the results and in order to estimate error bars.
DOI: 10.24435/materialscloud:6r-v1
title: Reduction of interlayer interaction in multilayer stacking graphene with carbon nanotube insertion: Insights from experiment and simulation

description: We insert carbon nanotubes (CNT) as nanospacers to modulate the microstructure of multilayer stacking graphene. Nanospacers can increase interlayer distance and reduce interlayer interaction. The graphene/CNT stacking structure is experimentally fabricated and Raman spectroscopy verifies the reduction in interlayer interaction within the stacking structure. Thus, we study the microstructure of the stacked graphene and CNTs by molecular dynamics simulation to systematically investigate the effect of CNT insertion. The distribution distance, size, and arrangement of the CNT can modulate the interlayer distance. The graphene/CNT stacking structure exhibits two stable configurations: the upper-layer suspension and interlayer adsorption.
DOI: 10.24435/materialscloud:fd-4j
title: Reduction of interlayer interaction in multilayer stacking graphene with carbon nanotube insertion: Insights from experiment and simulation

description: We insert carbon nanotubes (CNT) as nanospacers to modulate the microstructure of multilayer stacking graphene. Nanospacers can increase interlayer distance and reduce interlayer interaction. The graphene/CNT stacking structure is experimentally fabricated and Raman spectroscopy verifies the reduction in interlayer interaction within the stacking structure. Thus, we study the microstructure of the stacked graphene and CNTs by molecular dynamics simulation to systematically investigate the effect of CNT insertion. The distribution distance, size, and arrangement of the CNT can modulate the interlayer distance. The graphene/CNT stacking structure exhibits two stable configurations: the upper-layer suspension and interlayer adsorption.
DOI: 10.24435/materialscloud:wc-yf
title: Unearthing the foundational role of anharmonicity in heat transport in glasses

description: The time-honored Allen-Feldman theory of heat transport in glasses is generally assumed to predict a finite value for the thermal conductivity, even if it neglects the anharmonic broadening of vibrational normal modes. We demonstrate that the harmonic approximation predicts that the bulk lattice thermal conductivity of harmonic solids inevitably diverges at any temperature, irrespective of configurational disorder, and that its ability to represent the heat-transport properties observed experimentally in most glasses is implicitly due to finite-size effects. Our theoretical analysis is thoroughly benchmarked against careful numerical simulations. Our findings thus reveal that a proper account of anharmonic effects is indispensable to predict a finite value for the bulk thermal conductivity in any solid material, be it crystalline or glassy. This record contains data and scripts to support the findings of the manuscript and ensure their reproducibility.
DOI: 10.24435/materialscloud:a8-p8
title: Unearthing the foundational role of anharmonicity in heat transport in glasses

description: The time-honored Allen-Feldman theory of heat transport in glasses is generally assumed to predict a finite value for the thermal conductivity, even if it neglects the anharmonic broadening of vibrational normal modes. We demonstrate that the harmonic approximation predicts that the bulk lattice thermal conductivity of harmonic solids inevitably diverges at any temperature, irrespective of configurational disorder, and that its ability to represent the heat-transport properties observed experimentally in most glasses is implicitly due to finite-size effects. Our theoretical analysis is thoroughly benchmarked against careful numerical simulations. Our findings thus reveal that a proper account of anharmonic effects is indispensable to predict a finite value for the bulk thermal conductivity in any solid material, be it crystalline or glassy. This record contains data and scripts to support the findings of the manuscript and ensure their reproducibility.
DOI: 10.24435/materialscloud:zn-y8
title: Tilted-plane structure of the energy of finite quantum systems

description: The piecewise linearity condition on the total energy with respect to the total magnetization of finite quantum systems is derived using the infinite-separation-limit technique. This generalizes the well-known constancy condition, related to static correlation error, in approximate density functional theory. The magnetic analog of Koopmans' theorem in density functional theory is also derived. Moving to fractional electron count, the tilted-plane condition is derived, lifting certain assumptions in previous works. This generalization of the flat-plane condition characterizes the total energy surface of a finite system for all values of electron count N and magnetization M. This result is used in combination with tabulated spectroscopic data to show the flat-plane structure of the oxygen atom, among others. We find that derivative discontinuities with respect to electron count sometimes occur at noninteger values. A diverse set of tilted-plane structures is shown to occur in d-orbital subspaces, depending on chemical coordination. General occupancy-based total-energy expressions are demonstrated thereby to be necessarily dependent on the symmetry-imposed degeneracies.
DOI: 10.24435/materialscloud:jb-0h
title: Tilted-plane structure of the energy of finite quantum systems

description: The piecewise linearity condition on the total energy with respect to the total magnetization of finite quantum systems is derived using the infinite-separation-limit technique. This generalizes the well-known constancy condition, related to static correlation error, in approximate density functional theory. The magnetic analog of Koopmans' theorem in density functional theory is also derived. Moving to fractional electron count, the tilted-plane condition is derived, lifting certain assumptions in previous works. This generalization of the flat-plane condition characterizes the total energy surface of a finite system for all values of electron count N and magnetization M. This result is used in combination with tabulated spectroscopic data to show the flat-plane structure of the oxygen atom, among others. We find that derivative discontinuities with respect to electron count sometimes occur at noninteger values. A diverse set of tilted-plane structures is shown to occur in d-orbital subspaces, depending on chemical coordination. General occupancy-based total-energy expressions are demonstrated thereby to be necessarily dependent on the symmetry-imposed degeneracies.
DOI: 10.24435/materialscloud:ya-e0
title: Prediction of phonon-mediated superconductivity with high critical temperature in the two-dimensional topological semimetal W2N3

description: Two-dimensional superconductors attract great interest both for their fundamental physics and for their potential applications, especially in the rapidly growing field of quantum computing.  In this, we predict a remarkably high superconducting critical temperature of 21 K in the easily exfoliable, topologically nontrivial 2D semimetal W2N3. Furthermore we study how strain and doping can affect its superconducting properties.
DOI: 10.24435/materialscloud:v1-q5
title: Prediction of phonon-mediated superconductivity with high critical temperature in the two-dimensional topological semimetal W2N3

description: Two-dimensional superconductors attract great interest both for their fundamental physics and for their potential applications, especially in the rapidly growing field of quantum computing.  In this, we predict a remarkably high superconducting critical temperature of 21 K in the easily exfoliable, topologically nontrivial 2D semimetal W2N3. Furthermore we study how strain and doping can affect its superconducting properties.
DOI: 10.24435/materialscloud:yh-fj
title: Tailoring magnetism of graphene nanoflakes via tip-controlled dehydrogenation

description: Atomically precise graphene nanoflakes called nanographenes have emerged as a promising platform to realize carbon magnetism. Their ground state spin configuration can be anticipated by Ovchinnikov-Lieb rules based on the mismatch of π electrons from two sublattices. While rational geometrical design achieves specific spin configurations, further direct control over the π electrons offers a desirable extension for efficient spin manipulations and potential quantum device operations. To this end,  in a recent publication, we applied a site-specific dehydrogenation using a scanning tunneling microscope tip to nanographenes deposited on a Au(111) substrate, which showed the capability of precisely tailoring the underlying π-electron system and therefore efficiently manipulating their magnetism. Through first-principles calculations and tight-binding meanfield-Hubbard modeling, we demonstrated that the dehydrogenation-induced Au—C bond formation along with the resulting hybridization between frontier π orbitals and Au substrate states effectively eliminate the unpaired π electron. Our results establish an efficient technique for controlling the magnetism of nanographenes. This record contains data that support the scientific results discussed in our manuscript.