cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:2019.0007/v1
title: Mining the C-C Cross-Coupling Genome using Machine Learning

description: Applications of machine-learning (ML) techniques to the study of catalytic processes have begun to appear in the literature with increasing frequency. The computational speed up provided by ML allows the properties and energetics of thousands of prospective catalysts to be rapidly assessed. These results, once compiled into a database containing different properties, can be mined with the goal of establishing relationships between the intrinsic chemical properties of different catalysts and their overall catalytic performance. Previously, we applied ML models to predict the performance of 18,000 prospective catalysts for a Suzuki coupling reaction using molecular volcano plots. Here, we expand on our earlier work by examining a larger section of the C-C cross-coupling genome by using a dimensionality-reducing data-clustering algorithms (i.e., sketch-map) to, first, identify the compatibility of each catalyst with different C-C cross-coupling variants (e.g., Suzuki, Kumada, Negishi, Stille, and/or Hiyama) and, second, to uncover links between the chemical property of a catalyst and its catalytic activity. Our findings, based on the analysis of 18,000 catalysts, reveal strong correlations between a catalyst's HOMO energy and the suitability of its thermodynamic profile. These values can, subsequently, be tuned in order to maximize the thermodynamics of the catalytic cycle through the judicious choice of metal centers and the π-accepting/σ-donating nature of the flanking ligands. Overall, group 10 metals (Ni, Pd, Pt) are best coupled with the strong π-acceptor ligands and group 11 metals (Cu, Ag, Au) with weak π-acceptors, which maximize the thermodynamic drive of the catalytic cycle.
DOI: 10.24435/materialscloud:2019.0004/v2
title: Making the best of a bad situation: a multiscale approach to free energy calculation

description: Many enhanced sampling techniques rely on the identification of a number of collective variables that describe all the slow modes of the system. By constructing a bias potential in this reduced space one is then able to sample efficiently and reconstruct the free energy landscape. In methods like metadynamics, the quality of these collective variables plays a key role in convergence efficiency. Unfortunately in many systems of interest it is not possible to identify an optimal collective variable, and one must deal with the non-ideal situation of a system in which some slow modes are not accelerated. 
We propose a two-step approach in which, by taking into account the residual multiscale nature of the problem, one is able to significantly speed up convergence. To do so, we combine an exploratory metadynamics run with an optimization of the free energy difference between metastable states, based on the recently proposed variationally enhanced sampling method. This new method is well parallelizable and is especially suited for complex systems, because of its simplicity and clear underlying physical picture.
DOI: 10.24435/materialscloud:2019.0082/v1
title: On the Relative Stability of Near-Surface Oxygen Vacancies at the CeO2 (111) Surface upon Zr Doping

description: The effects of Zr doping on the stability of the CeO2(111) surface as a function of the dopant concentration and distribution, as well as on the relative stability of surface and subsurface oxygen vacancies, were studied by means of density functional theory (DFT+U) calculations. For a given Zr content, the more stable structures do not correspond to those configurations with Zr located in the topmost O-Ce-O trilayer (TL1), but in inner layers, and the stability decreases with increasing Zr concentration. For the undoped CeO2(111) surface, the preference of subsurface vacancies with next-nearest neighbor (NNN) Ce3+ configuration has earlier been predicted. For the Zr-doped surface, the formation of vacancies was studied using a surface unit cell with 2x2 periodicity, and it was found that the most stable configuration corresponds to the  Zr atom located in the surface layer (TL1) neighboring a subsurface oxygen vacancy with NNN Ce3+, being the  formation energy equal to 1.16 eV. The corresponding surface oxygen vacancy is 0.16 eV less stable. These values are by 0.73 and 0.92 eV, respectively, smaller than the corresponding ones for the pure CeO2(111) surface. Moreover, when Zr is located in TL2 the subsurface vacancy becomes by 0.10 eV less stable, compared to Zr in the TL1. The Ce3+ preference for the next-nearest neighbor cationic sites to both surface and subsurface vacancies at CeO2 (111), becomes more pronounced upon Zr doping. The results are explained in terms of Zr- and vacancy-induced lattice relaxation effects.
DOI: 10.24435/materialscloud:ae-qb
title: Relative Stability of Near-Surface Oxygen Vacancies at the CeO2 (111) Surface upon Zirconium Doping

description: The effects of Zr doping on the stability of the CeO2(111) surface as a function of the dopant concentration and distribution, as well as on the relative stability of surface and subsurface oxygen vacancies, were studied by means of density functional theory (DFT+U) calculations. For a given Zr content, the more stable structures do not correspond to those configurations with Zr located in the topmost O-Ce-O trilayer (TL1), but in inner layers, and the stability decreases with increasing Zr concentration. For the undoped CeO2(111) surface, the preference of subsurface vacancies with next-nearest neighbor (NNN) Ce3+ configuration has earlier been predicted. For the Zr-doped surface, the formation of vacancies was studied using a surface unit cell with 2x2 periodicity, and it was found that the most stable configuration corresponds to the  Zr atom located in the surface layer (TL1) neighboring a subsurface oxygen vacancy with NNN Ce3+, being the  formation energy equal to 1.16 eV. The corresponding surface oxygen vacancy is 0.16 eV less stable. These values are by 0.73 and 0.92 eV, respectively, smaller than the corresponding ones for the pure CeO2(111) surface. Moreover, when Zr is located in TL2 the subsurface vacancy becomes by 0.10 eV less stable, compared to Zr in the TL1. The Ce3+ preference for the next-nearest neighbor cationic sites to both surface and subsurface vacancies at CeO2 (111), becomes more pronounced upon Zr doping. The results are explained in terms of Zr- and vacancy-induced lattice relaxation effects.
DOI: 10.24435/materialscloud:2019.0046/v1
title: Can metal–organic frameworks be used for cannabis breathalyzers?

description: ∆9-tetrahydrocannabinol (THC) is the principal psychoactive component of cannabis, and there is an urgent need to build low-cost and portable devices that can detect its presence from breath. Similarly to alcohol detectors, these tools can be used by law enforcement to determine driver intoxication and enforce safer and more regulated use of cannabis. In this work, we propose to use a class of microporous crystals, metal–organic frameworks (MOFs), to selectively adsorb THC that can be later detected using optical, electrochemical, or fluorescence-based sensing methods. We screened computationally more than 5000 MOFs, highlighting the materials that have the largest affinity with THC, as well as the highest selectivity against water, showing that it is thermodynamically feasible for MOFs to adsorb THC from humid breath. We propose and compare different models for THC and different computational protocols to rank the promising materials, also presenting a novel approach to assess the permeability of nonspherical molecules in a porous framework. We identified three adsorption motifs in MOFs with high affinity to THC, which we refer to as "narrow channels", "thick walls" and "parking spots". Therefore, we expect our protocols and our findings to be generalizable for different classes of microporous materials, and also for investigating the adsorption properties of other large molecules that, like THC, have a nonspherical shape.
DOI: 10.24435/materialscloud:np-27
title: Can metal–organic frameworks be used for cannabis breathalyzers?

description: ∆9-tetrahydrocannabinol (THC) is the principal psychoactive component of cannabis, and there is an urgent need to build low-cost and portable devices that can detect its presence from breath. Similarly to alcohol detectors, these tools can be used by law enforcement to determine driver intoxication and enforce safer and more regulated use of cannabis. In this work, we propose to use a class of microporous crystals, metal–organic frameworks (MOFs), to selectively adsorb THC that can be later detected using optical, electrochemical, or fluorescence-based sensing methods. We screened computationally more than 5000 MOFs, highlighting the materials that have the largest affinity with THC, as well as the highest selectivity against water, showing that it is thermodynamically feasible for MOFs to adsorb THC from humid breath. We propose and compare different models for THC and different computational protocols to rank the promising materials, also presenting a novel approach to assess the permeability of nonspherical molecules in a porous framework. We identified three adsorption motifs in MOFs with high affinity to THC, which we refer to as "narrow channels", "thick walls" and "parking spots". Therefore, we expect our protocols and our findings to be generalizable for different classes of microporous materials, and also for investigating the adsorption properties of other large molecules that, like THC, have a nonspherical shape.
DOI: 10.24435/materialscloud:2020.0009/v1
title: Divalent Path to Enhance p-Type Conductivity in a SnO Transparent Semiconductor

description: The role of the divalent nature of tin is explored in tin monoxide, revealing a novel path for enhancing p-type conductivity. The consequences of oxygen off-stoichiometry indicate that a defect complex formed by a tin vacancy (VSn) and an impurity interstitial (Di) leads to an increased number of free carriers as well as improved acceptor state stability when compared with the isolated VSn. In this study, we identify several elements that are able to stabilize such a defect complex configuration. The enhanced ionization of the resulting complex arises from the divalent nature of Sn, which allows Sn(II) and Sn(IV) oxidation states to form. Such a novel doping mechanism not only offers a path for creating a high-performance p-type transparent SnO, but reveals an as-of-yet unexplored route to improve conductivity in other compounds formed by multivalent elements, for example, Sn(II)-based thermoelectrics.
DOI: 10.24435/materialscloud:2019.0047/v1
title: Local electronic descriptors for solute-defect interactions in bcc refractory metals

description: The interactions between solute atoms and crystalline defects such as vacancies, dislocations, and grain boundaries are essential in determining alloy properties. Here we present a general linear correlation between two descriptors of local electronic structures and the solute-defect interaction energies in binary alloys of body-centered-cubic (bcc) refractory metals (such as W and Ta) with transition-metal substitutional solutes. One electronic descriptor is the bimodality of the d-orbital local density of states for a matrix atom at the substitutional site, and the other is related to the hybridization strength between the valance sp- and d-bands for the same matrix atom. For a particular pair of solute-matrix elements, this linear correlation is valid independent of types of defects and the locations of substitutional sites. These results provide the possibility to apply local electronic descriptors for quantitative and efficient predictions on the solute-defect interactions and defect properties in alloys.
DOI: 10.24435/materialscloud:jh-9k
title: Divalent Path to Enhance p-Type Conductivity in a SnO Transparent Semiconductor

description: The role of the divalent nature of tin is explored in tin monoxide, revealing a novel path for enhancing p-type conductivity. The consequences of oxygen off-stoichiometry indicate that a defect complex formed by a tin vacancy (VSn) and an impurity interstitial (Di) leads to an increased number of free carriers as well as improved acceptor state stability when compared with the isolated VSn. In this study, we identify several elements that are able to stabilize such a defect complex configuration. The enhanced ionization of the resulting complex arises from the divalent nature of Sn, which allows Sn(II) and Sn(IV) oxidation states to form. Such a novel doping mechanism not only offers a path for creating a high-performance p-type transparent SnO, but reveals an as-of-yet unexplored route to improve conductivity in other compounds formed by multivalent elements, for example, Sn(II)-based thermoelectrics.
DOI: 10.24435/materialscloud:af-hh
title: Local electronic descriptors for solute-defect interactions in bcc refractory metals

description: The interactions between solute atoms and crystalline defects such as vacancies, dislocations, and grain boundaries are essential in determining alloy properties. Here we present a general linear correlation between two descriptors of local electronic structures and the solute-defect interaction energies in binary alloys of body-centered-cubic (bcc) refractory metals (such as W and Ta) with transition-metal substitutional solutes. One electronic descriptor is the bimodality of the d-orbital local density of states for a matrix atom at the substitutional site, and the other is related to the hybridization strength between the valance sp- and d-bands for the same matrix atom. For a particular pair of solute-matrix elements, this linear correlation is valid independent of types of defects and the locations of substitutional sites. These results provide the possibility to apply local electronic descriptors for quantitative and efficient predictions on the solute-defect interactions and defect properties in alloys.
DOI: 10.24435/materialscloud:2019.0004/v3
title: Making the best of a bad situation: a multiscale approach to free energy calculation

description: Many enhanced sampling techniques rely on the identification of a number of collective variables that describe all the slow modes of the system. By constructing a bias potential in this reduced space one is then able to sample efficiently and reconstruct the free energy landscape. In methods like metadynamics, the quality of these collective variables plays a key role in convergence efficiency. Unfortunately in many systems of interest it is not possible to identify an optimal collective variable, and one must deal with the non-ideal situation of a system in which some slow modes are not accelerated. 
We propose a two-step approach in which, by taking into account the residual multiscale nature of the problem, one is able to significantly speed up convergence. To do so, we combine an exploratory metadynamics run with an optimization of the free energy difference between metastable states, based on the recently proposed variationally enhanced sampling method. This new method is well parallelizable and is especially suited for complex systems, because of its simplicity and clear underlying physical picture.
DOI: 10.24435/materialscloud:2019.0007/v2
title: Mining the C-C Cross-Coupling Genome using Machine Learning

description: Applications of machine-learning (ML) techniques to the study of catalytic processes have begun to appear in the literature with increasing frequency. The computational speed up provided by ML allows the properties and energetics of thousands of prospective catalysts to be rapidly assessed. These results, once compiled into a database containing different properties, can be mined with the goal of establishing relationships between the intrinsic chemical properties of different catalysts and their overall catalytic performance. Previously, we applied ML models to predict the performance of 18,000 prospective catalysts for a Suzuki coupling reaction using molecular volcano plots. Here, we expand on our earlier work by examining a larger section of the C-C cross-coupling genome by using a dimensionality-reducing data-clustering algorithms (i.e., sketch-map) to, first, identify the compatibility of each catalyst with different C-C cross-coupling variants (e.g., Suzuki, Kumada, Negishi, Stille, and/or Hiyama) and, second, to uncover links between the chemical property of a catalyst and its catalytic activity. Our findings, based on the analysis of 18,000 catalysts, reveal strong correlations between a catalyst's HOMO energy and the suitability of its thermodynamic profile. These values can, subsequently, be tuned in order to maximize the thermodynamics of the catalytic cycle through the judicious choice of metal centers and the π-accepting/σ-donating nature of the flanking ligands. Overall, group 10 metals (Ni, Pd, Pt) are best coupled with the strong π-acceptor ligands and group 11 metals (Cu, Ag, Au) with weak π-acceptors, which maximize the thermodynamic drive of the catalytic cycle.
DOI: 10.24435/materialscloud:az-j0
title: Making the best of a bad situation: a multiscale approach to free energy calculation

description: Many enhanced sampling techniques rely on the identification of a number of collective variables that describe all the slow modes of the system. By constructing a bias potential in this reduced space one is then able to sample efficiently and reconstruct the free energy landscape. In methods like metadynamics, the quality of these collective variables plays a key role in convergence efficiency. Unfortunately in many systems of interest it is not possible to identify an optimal collective variable, and one must deal with the non-ideal situation of a system in which some slow modes are not accelerated. 
We propose a two-step approach in which, by taking into account the residual multiscale nature of the problem, one is able to significantly speed up convergence. To do so, we combine an exploratory metadynamics run with an optimization of the free energy difference between metastable states, based on the recently proposed variationally enhanced sampling method. This new method is well parallelizable and is especially suited for complex systems, because of its simplicity and clear underlying physical picture.
DOI: 10.24435/materialscloud:cm-7p
title: Relative abundance of Z2 topological order in exfoliable two-dimensional insulators

description: Quantum spin Hall insulators (QSHIs) make up a class of two-dimensional materials with a finite electronic band gap in the bulk and gapless helical edge states. Some of the phenomena that can be hosted in these materials, from one-dimensional low-dissipation electronic transport to spin filtering, could be promising for many technological applications in the fields of electronics, spintronics, and topological quantum computing. Nevertheless, the rarity of two-dimensional materials that can exhibit nontrivial topological order at room temperature hinders development. In the publication, we report on our screening of a comprehensive database we recently identified of 1825 monolayers that can be exfoliated from experimentally known compounds to search for novel quantum spin Hall insulators. In this entry we provide the AiiDA database with the calculations of the DFT band structures (both with and without spin-orbit coupling) and the DFPT phonon dispersions for the QSHI candidates that we identify. All the data are also displayed in the dedicated DISCOVER section "2D topological insulators" https://www.materialscloud.org/discover/2dtopo/dashboard/ptable .
DOI: 10.24435/materialscloud:2019.0016/v2
title: Multithermal-multibaric molecular simulations from a variational principle

description: We present a method for performing multithermal-multibaric molecular dynamics simulations that sample entire regions of the temperature-pressure (TP) phase diagram. The method uses a variational principle [Valsson and Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to construct a bias that leads to a uniform sampling in energy and volume. The intervals of temperature and pressure are taken as inputs and the relevant energy and volume regions are determined on the fly. In this way the method guarantees adequate statistics for the chosen TP region. We show that our multithermal-multibaric simulations can be used to calculate all static physical quantities for all temperatures and pressures in the targeted region of the TP plane. We illustrate our approach by studying the density anomaly of TIP4P/Ice water.

This record includes input and output files, and Jupyter Notebooks describing the analysis of the simulations and the creation of the figures for the paper.
DOI: 10.24435/materialscloud:4p-q2
title: Relative abundance of Z2 topological order in exfoliable two-dimensional insulators

description: Quantum spin Hall insulators (QSHIs) make up a class of two-dimensional materials with a finite electronic band gap in the bulk and gapless helical edge states. Some of the phenomena that can be hosted in these materials, from one-dimensional low-dissipation electronic transport to spin filtering, could be promising for many technological applications in the fields of electronics, spintronics, and topological quantum computing. Nevertheless, the rarity of two-dimensional materials that can exhibit nontrivial topological order at room temperature hinders development. In the publication, we report on our screening of a comprehensive database we recently identified of 1825 monolayers that can be exfoliated from experimentally known compounds to search for novel quantum spin Hall insulators. In this entry we provide the AiiDA database with the calculations of the DFT band structures (both with and without spin-orbit coupling) and the DFPT phonon dispersions for the QSHI candidates that we identify. All the data are also displayed in the dedicated DISCOVER section "2D topological insulators" https://www.materialscloud.org/discover/2dtopo/dashboard/ptable .
DOI: 10.24435/materialscloud:2019.0007/v3
title: Mining the C-C Cross-Coupling Genome using Machine Learning

description: Applications of machine-learning (ML) techniques to the study of catalytic processes have begun to appear in the literature with increasing frequency. The computational speed up provided by ML allows the properties and energetics of thousands of prospective catalysts to be rapidly assessed. These results, once compiled into a database containing different properties, can be mined with the goal of establishing relationships between the intrinsic chemical properties of different catalysts and their overall catalytic performance. Previously, we applied ML models to predict the performance of 18,000 prospective catalysts for a Suzuki coupling reaction using molecular volcano plots. Here, we expand on our earlier work by examining a larger section of the C-C cross-coupling genome by using a dimensionality-reducing data-clustering algorithms (i.e., sketch-map) to, first, identify the compatibility of each catalyst with different C-C cross-coupling variants (e.g., Suzuki, Kumada, Negishi, Stille, and/or Hiyama) and, second, to uncover links between the chemical property of a catalyst and its catalytic activity. Our findings, based on the analysis of 18,000 catalysts, reveal strong correlations between a catalyst's HOMO energy and the suitability of its thermodynamic profile. These values can, subsequently, be tuned in order to maximize the thermodynamics of the catalytic cycle through the judicious choice of metal centers and the π-accepting/σ-donating nature of the flanking ligands. Overall, group 10 metals (Ni, Pd, Pt) are best coupled with the strong π-acceptor ligands and group 11 metals (Cu, Ag, Au) with weak π-acceptors, which maximize the thermodynamic drive of the catalytic cycle.