cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:a6-2z
title: On-surface synthesis of π-conjugated ladder-type polymers comprising nonbenzenoid moieties

description: In this record we provide data to support our recent findings related to the fabrication of π-conjugated ladder-type polymers comprising nonbenzenoid moieties. On-surface synthesis provides a powerful approach toward the atomically precise fabrication of π-conjugated ladder polymers (CLPs). In the manuscript we report the surface-assisted synthesis of nonbenzenoid CLPs from cyclopenta-annulated anthracene monomers on Au(111) under ultrahigh vacuum conditions. Successive thermal annealing steps reveal the dehalogenative homocoupling to yield an intermediate 1D polymer and the subsequent cyclodehydrogenation to form the fully conjugated ladder polymer. Notably, neighbouring monomers may fuse in two different ways, resulting in six- and five-membered rings, respectively. The structure and electronic properties of the reaction products have been investigated via low-temperature scanning tunneling microscopy and spectroscopy, complemented by density-functional theory calculations. Our results provide perspectives for the on-surface synthesis of nonbenzenoid CLPs with the potential to be used for organic electronic devices.
DOI: 10.24435/materialscloud:nj-2g
title: The importance of nuclear quantum effects for NMR crystallography

description: The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of the computational prediction of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential energy surface.
To test the limits of this approximation, we perform a systematic study of the role of finite-temperature and quantum nuclear fluctuations on 1H, 13C, and 15N chemical shieldings in molecular crystals -- considering the paradigmatic examples of the different polymorphs of benzene, glycine, and succinic acid.
We find the effect of quantum fluctuations to be comparable in size to the typical errors of predictions of chemical shieldings for static nuclei with respect to experimental measurements, and to improve the match between experiments and theoretical predictions, translating to more reliable assignment of the NMR spectra to the correct candidate structure.
Thanks to the use of integrated machine-learning models trained on both first-principles configurational energies and chemical shieldings, the accurate sampling of thermal and quantum fluctuations of the structures can be achieved at an affordable cost, setting a new standard for the calculations that underlie solid-state structural determination by NMR.

This archive contains the machine-learning potentials and shielding models, sample inputs, and scripts to reproduce the results discussed in the manuscript. It further contains the reference data and scripts to reconstruct the shielding models.
DOI: 10.24435/materialscloud:yx-1z
title: On-surface synthesis of π-conjugated ladder-type polymers comprising nonbenzenoid moieties

description: In this record we provide data to support our recent findings related to the fabrication of π-conjugated ladder-type polymers comprising nonbenzenoid moieties. On-surface synthesis provides a powerful approach toward the atomically precise fabrication of π-conjugated ladder polymers (CLPs). In the manuscript we report the surface-assisted synthesis of nonbenzenoid CLPs from cyclopenta-annulated anthracene monomers on Au(111) under ultrahigh vacuum conditions. Successive thermal annealing steps reveal the dehalogenative homocoupling to yield an intermediate 1D polymer and the subsequent cyclodehydrogenation to form the fully conjugated ladder polymer. Notably, neighbouring monomers may fuse in two different ways, resulting in six- and five-membered rings, respectively. The structure and electronic properties of the reaction products have been investigated via low-temperature scanning tunneling microscopy and spectroscopy, complemented by density-functional theory calculations. Our results provide perspectives for the on-surface synthesis of nonbenzenoid CLPs with the potential to be used for organic electronic devices.
DOI: 10.24435/materialscloud:jc-fv
title: The importance of nuclear quantum effects for NMR crystallography

description: The resolving power of solid-state nuclear magnetic resonance (NMR) crystallography depends heavily on the accuracy of the computational prediction of NMR chemical shieldings of candidate structures, which are usually taken to be local minima in the potential energy surface.
To test the limits of this approximation, we perform a systematic study of the role of finite-temperature and quantum nuclear fluctuations on 1H, 13C, and 15N chemical shieldings in molecular crystals -- considering the paradigmatic examples of the different polymorphs of benzene, glycine, and succinic acid.
We find the effect of quantum fluctuations to be comparable in size to the typical errors of predictions of chemical shieldings for static nuclei with respect to experimental measurements, and to improve the match between experiments and theoretical predictions, translating to more reliable assignment of the NMR spectra to the correct candidate structure.
Thanks to the use of integrated machine-learning models trained on both first-principles configurational energies and chemical shieldings, the accurate sampling of thermal and quantum fluctuations of the structures can be achieved at an affordable cost, setting a new standard for the calculations that underlie solid-state structural determination by NMR.

This archive contains the machine-learning potentials and shielding models, sample inputs, and scripts to reproduce the results discussed in the manuscript. It further contains the reference data and scripts to reconstruct the shielding models.
DOI: 10.34740/kaggle/dsv/12268223
title: Trending Youtube Video Statistics (113 Countries)
DOI: 10.24435/materialscloud:vp-jf
title: Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine

description: Structure prediction for molecular crystals is a longstanding challenge, as often minuscule free energy differences between polymorphs are sensitively affected by the description of electronic structure, the statistical mechanics of the nuclei and the cell, and thermal expansion. The importance of these effects has been individually established, but rigorous free energy calculations, which simultaneously account for all terms, have not been computationally viable.
Here we reproduce the experimental stabilities of polymorphs of prototypical compounds -- benzene, glycine, and succinic acid -- by computing rigorous first-principles Gibbs free energies, at a fraction of the cost of conventional methods. This is achieved by a bottom-up approach, which involves generating machine-learning potentials to calculate surrogate free energies and subsequently calculating true first-principles free energies using inexpensive free energy perturbations.
Accounting for all relevant physical effects is no longer a daunting task and provides the foundation for structure predictions for more complex systems of industrial importance.

This Materials Cloud archive contains first-principles training, validation, and test data for polymorphs of benzene, succinic acid, and glycine underlying the above-mentioned machine-learning potentials. 
For each compound the archive provides two sets of data: the first based on DFT calculations with the semi-local PBE functional and the Tkatchenko-Scheffler dispersion correction, and the second based on DFT calculations with the hybrid PBE0 functional and the many-body dispersion correction of Tkatchenko et al. For each compound and both levels of electronic-structure theory, structure datasets in libatom extended-xyz format provide representative, thermalised configurations and the associated configurational energies, atomic forces, and stresses on the simulation cell. The configurations are extracted from a combination of classical temperature replica exchange molecular dynamics simulations and path-integral molecular dynamics for a representative set of perturbed unit cells based on the experimental structures of: forms I, II, Ihp and V' of benzene, alpha- and beta-succinic acid, and alpha-, beta-, gamma-, and delta-glycine. The detailed data provenance and dataset architecture is described in the attached README file.
DOI: 10.24435/materialscloud:1s-h9
title: Semi-local and hybrid functional DFT data for thermalised snapshots of polymorphs of benzene, succinic acid, and glycine

description: Structure prediction for molecular crystals is a longstanding challenge, as often minuscule free energy differences between polymorphs are sensitively affected by the description of electronic structure, the statistical mechanics of the nuclei and the cell, and thermal expansion. The importance of these effects has been individually established, but rigorous free energy calculations, which simultaneously account for all terms, have not been computationally viable.
Here we reproduce the experimental stabilities of polymorphs of prototypical compounds -- benzene, glycine, and succinic acid -- by computing rigorous first-principles Gibbs free energies, at a fraction of the cost of conventional methods. This is achieved by a bottom-up approach, which involves generating machine-learning potentials to calculate surrogate free energies and subsequently calculating true first-principles free energies using inexpensive free energy perturbations.
Accounting for all relevant physical effects is no longer a daunting task and provides the foundation for structure predictions for more complex systems of industrial importance.

This Materials Cloud archive contains first-principles training, validation, and test data for polymorphs of benzene, succinic acid, and glycine underlying the above-mentioned machine-learning potentials. 
For each compound the archive provides two sets of data: the first based on DFT calculations with the semi-local PBE functional and the Tkatchenko-Scheffler dispersion correction, and the second based on DFT calculations with the hybrid PBE0 functional and the many-body dispersion correction of Tkatchenko et al. For each compound and both levels of electronic-structure theory, structure datasets in libatom extended-xyz format provide representative, thermalised configurations and the associated configurational energies, atomic forces, and stresses on the simulation cell. The configurations are extracted from a combination of classical temperature replica exchange molecular dynamics simulations and path-integral molecular dynamics for a representative set of perturbed unit cells based on the experimental structures of: forms I, II, Ihp and V' of benzene, alpha- and beta-succinic acid, and alpha-, beta-, gamma-, and delta-glycine. The detailed data provenance and dataset architecture is described in the attached README file.
DOI: 10.24435/materialscloud:m2-31
title: Coherent energy exchange between carriers and phonons in Peierls-distorted bismuth unveiled by broadband XUV pulses

description: In Peierls-distorted materials, photoexcitation leads to a strongly coupled transient response between structural and electronic degrees of freedom, always measured independently of each other. Here we use transient reflectivity in the extreme ultraviolet to quantify both responses in photoexcited bismuth in a single measurement. With the help of first-principles calculations based on density-functional theory (DFT) and time-dependent DFT, the real-space atomic motion and the temperature of both electrons and holes as a function of time are captured simultaneously, retrieving an anticorrelation between the A1g phonon dynamics and carrier temperature. The results reveal a coherent, bi-directional energy exchange between carriers and phonons, which is a dynamical counterpart of the static Peierls-Jones distortion, providing first-time validation of previous theoretical predictions.
DOI: 10.24435/materialscloud:1v-p7
title: Coherent energy exchange between carriers and phonons in Peierls-distorted bismuth unveiled by broadband XUV pulses

description: In Peierls-distorted materials, photoexcitation leads to a strongly coupled transient response between structural and electronic degrees of freedom, always measured independently of each other. Here we use transient reflectivity in the extreme ultraviolet to quantify both responses in photoexcited bismuth in a single measurement. With the help of first-principles calculations based on density-functional theory (DFT) and time-dependent DFT, the real-space atomic motion and the temperature of both electrons and holes as a function of time are captured simultaneously, retrieving an anticorrelation between the A1g phonon dynamics and carrier temperature. The results reveal a coherent, bi-directional energy exchange between carriers and phonons, which is a dynamical counterpart of the static Peierls-Jones distortion, providing first-time validation of previous theoretical predictions.
DOI: 10.24435/materialscloud:d4-bj
title: Molecular dynamics based cohesive law for epoxy-graphene interfaces

description: Molecular dynamics (MD) simulations are performed to obtain mode I and II fracture energies and cohesive laws for bulk epoxy and interfaces formed between epoxy and single-layer graphene (SLG), multilayer graphene (MLG), and multilayer graphene oxide (MLGO). The elastic moduli and ultimate tensile and shear strengths of epoxy--graphene interfaces are calculated from uniaxial tension and simple shear loadings. The results show that Young's modulus and the ultimate tensile strength increase relative to bulk epoxy, whereas the shear modulus and ultimate shear strength are reduced. Failure of epoxy--graphene interfaces in tension occurs due to the formation of voids in the epoxy. Failure in shear is due to tangential slipping at the interface. Under mixed mode conditions, the shear modulus and shear strength decrease with increasing tensile load. The critical energy release rate G_c for the studied epoxy--SLG/MLG/MLGO systems are obtained using a continuum fracture mechanics approach and are found to be significantly lower than for bulk epoxy. All of the results are combined to define mode I and II cohesive laws for bulk epoxy and epoxy--SLG/MLG/MLGO interfaces that can be used in theoretical models and numerical methods, such as finite elements, that employ cohesive zones.
DOI: 10.24435/materialscloud:68-6p
title: Machine learning of superconducting critical temperature from Eliashberg theory

description: The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional electron-phonon superconductors, including the retardation of the interaction and the effect of the Coulomb pseudopotential, to predict the critical temperature Tc and other properties. McMillan, Allen, and Dynes derived approximate closed-form expressions for the critical temperature predicted by this theory, which depends essentially on the electron-phonon spectral function α²F(ω), using α²F for low-Tc superconductors. Here we show that modern machine learning techniques can substantially improve these formulae, accounting for more general shapes of the α²F function. Using symbolic regression and the sure independence screening and sparsifying operator (SISSO) framework, together with a database of artificially generated α²F functions, ranging from multimodal Einstein-like models to calculated spectra of polyhydrides, as well as numerical solutions of the Eliashberg equations, we derive a formula for Tc that performs as well as Allen-Dynes for low-Tc superconductors and substantially better for higher-Tc ones. The expression identified through our data-driven approach corrects the systematic underestimation of Tc while reproducing the physical constraints originally outlined by Allen and Dynes. This equation should replace the Allen-Dynes formula for the prediction of higher-temperature superconductors and the estimation of λ from experimental data.

This repository contains CSV tables of model inputs, prefactors, and critical temperature predictions used for machine learning and validation in this work. Additionally, the inputs and outputs of the SISSO framework for both rounds of symbolic regression are included. Please see https://github.com/henniggroup/symbolic-regression-utilities for tools to create and analyze SISSO inputs and outputs.
DOI: 10.24435/materialscloud:sj-39
title: Molecular dynamics based cohesive law for epoxy-graphene interfaces

description: Molecular dynamics (MD) simulations are performed to obtain mode I and II fracture energies and cohesive laws for bulk epoxy and interfaces formed between epoxy and single-layer graphene (SLG), multilayer graphene (MLG), and multilayer graphene oxide (MLGO). The elastic moduli and ultimate tensile and shear strengths of epoxy--graphene interfaces are calculated from uniaxial tension and simple shear loadings. The results show that Young's modulus and the ultimate tensile strength increase relative to bulk epoxy, whereas the shear modulus and ultimate shear strength are reduced. Failure of epoxy--graphene interfaces in tension occurs due to the formation of voids in the epoxy. Failure in shear is due to tangential slipping at the interface. Under mixed mode conditions, the shear modulus and shear strength decrease with increasing tensile load. The critical energy release rate G_c for the studied epoxy--SLG/MLG/MLGO systems are obtained using a continuum fracture mechanics approach and are found to be significantly lower than for bulk epoxy. All of the results are combined to define mode I and II cohesive laws for bulk epoxy and epoxy--SLG/MLG/MLGO interfaces that can be used in theoretical models and numerical methods, such as finite elements, that employ cohesive zones.
DOI: 10.24435/materialscloud:ds-2j
title: Machine learning of superconducting critical temperature from Eliashberg theory

description: The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional electron-phonon superconductors, including the retardation of the interaction and the effect of the Coulomb pseudopotential, to predict the critical temperature Tc and other properties. McMillan, Allen, and Dynes derived approximate closed-form expressions for the critical temperature predicted by this theory, which depends essentially on the electron-phonon spectral function α²F(ω), using α²F for low-Tc superconductors. Here we show that modern machine learning techniques can substantially improve these formulae, accounting for more general shapes of the α²F function. Using symbolic regression and the sure independence screening and sparsifying operator (SISSO) framework, together with a database of artificially generated α²F functions, ranging from multimodal Einstein-like models to calculated spectra of polyhydrides, as well as numerical solutions of the Eliashberg equations, we derive a formula for Tc that performs as well as Allen-Dynes for low-Tc superconductors and substantially better for higher-Tc ones. The expression identified through our data-driven approach corrects the systematic underestimation of Tc while reproducing the physical constraints originally outlined by Allen and Dynes. This equation should replace the Allen-Dynes formula for the prediction of higher-temperature superconductors and the estimation of λ from experimental data.

This repository contains CSV tables of model inputs, prefactors, and critical temperature predictions used for machine learning and validation in this work. Additionally, the inputs and outputs of the SISSO framework for both rounds of symbolic regression are included. Please see https://github.com/henniggroup/symbolic-regression-utilities for tools to create and analyze SISSO inputs and outputs.
DOI: 10.24435/materialscloud:jf-zq
title: Oxygen vacancies in strontium titanate: a DFT+DMFT study

description: We address the long-standing question of the nature of oxygen vacancies in strontium titanate, using a combination of density functional theory and dynamical mean-field theory (DFT+DMFT) to investigate in particular the effect of vacancy-site correlations on the electronic properties. Our approach uses a minimal low-energy electronic subspace including the Ti-t2g orbitals plus an additional vacancy-centered Wannier function, and it provides an intuitive and physically transparent framework to study the effect of the local electron-electron interactions on the excess charge introduced by the oxygen vacancies. We estimate the strength of the screened interaction parameters using the constrained random phase approximation, and we find a sizable Hubbard U parameter for the vacancy orbital. Our main finding, which reconciles previous experimental and computational results, is that the ground state is either a state with double occupation of the localized defect state or a state with a singly occupied vacancy and one electron transferred to the conduction band. The balance between these two competing states is determined by the strength of the interaction both on the vacancy and the Ti sites, and on the Ti-Ti distance across the vacancy. Finally, we contrast the case of vacancy doping in SrTiO3 with doping via La substitution, and we show that the latter is well described by a simple rigid-band picture.
DOI: 10.24435/materialscloud:hf-3h
title: Oxygen vacancies in strontium titanate: a DFT+DMFT study

description: We address the long-standing question of the nature of oxygen vacancies in strontium titanate, using a combination of density functional theory and dynamical mean-field theory (DFT+DMFT) to investigate in particular the effect of vacancy-site correlations on the electronic properties. Our approach uses a minimal low-energy electronic subspace including the Ti-t2g orbitals plus an additional vacancy-centered Wannier function, and it provides an intuitive and physically transparent framework to study the effect of the local electron-electron interactions on the excess charge introduced by the oxygen vacancies. We estimate the strength of the screened interaction parameters using the constrained random phase approximation, and we find a sizable Hubbard U parameter for the vacancy orbital. Our main finding, which reconciles previous experimental and computational results, is that the ground state is either a state with double occupation of the localized defect state or a state with a singly occupied vacancy and one electron transferred to the conduction band. The balance between these two competing states is determined by the strength of the interaction both on the vacancy and the Ti sites, and on the Ti-Ti distance across the vacancy. Finally, we contrast the case of vacancy doping in SrTiO3 with doping via La substitution, and we show that the latter is well described by a simple rigid-band picture.
DOI: 10.24435/materialscloud:ks-qb
title: The nature of the active sites on Ni/CeO2 catalysts for methane conversions

description: Effective catalysts for the direct conversion of methane to methanol and for methane's dry reforming to syngas are Holy Grails of catalysis research toward clean energy technologies. It has recently been discovered that Ni at low loadings on CeO2 is very reactive towards reactants CH4, H2O and CO2 and active for both of these reactions. Revealing the nature of the active sites in such systems is paramount to a rational design of improved catalysts. Here, using a combination of experimental measurements and density functional theory calculations, we show that the most active sites are cationic Ni atoms in clusters at step edges on the CeO2 surface, using the activation of CH4 as an example . We show that the size and morphology of the supported nanoparticles together with strong Ni−support bonding and charge transfer at the step edge are key to the high catalytic activity towards these methane conversions. We anticipate that this knowledge will inspire the development of more efficient catalysts for these reactions.