cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:jz-jj
title: Massive Dirac fermion behavior in a low bandgap graphene nanoribbon near a topological phase boundary

description: Graphene nanoribbons (GNRs) have attracted much interest due to their largely modifiable electronic properties. Manifestation of these properties requires atomically precise GNRs which can be achieved through a bottom–up synthesis approach. This has recently been applied to the synthesis of width‐modulated GNRs hosting topological electronic quantum phases, with valence electronic properties that are well captured by the Su–Schrieffer–Heeger (SSH) model describing a 1D chain of interacting dimers. In this record we provide data to support our recent publication where we demonstrate that ultralow bandgap GNRs with charge carriers behaving as massive Dirac fermions can be realized when their valence electrons represent an SSH chain close to the topological phase boundary, i.e., when the intra‐ and interdimer coupling become approximately equal. Such a system has been achieved via on‐surface synthesis based on readily available pyrene‐based precursors and the resulting GNRs are characterized by scanning probe methods. The pyrene‐based GNRs (pGNRs) can be processed under ambient conditions and incorporated as the active material in a field effect transistor. A quasi‐metallic transport behavior is observed at room temperature, whereas at low temperature, the pGNRs behave as quantum dots showing single‐electron tunneling and Coulomb blockade. This study may enable the realization of devices based on carbon nanomaterials with exotic quantum properties.
DOI: 10.24435/materialscloud:b9-sq
title: How to extract adsorption energies, adsorbate-adsorbate interaction parameters, and saturation coverages from temperature programmed desorption experiments

description: We present a scheme to extract the adsorption energy, adsorbate interaction parameter and the saturation coverage from temperature programmed desorption (TPD) experiments. We propose that the coverage dependent adsorption energy can be fit using a functional form including the configurational entropy and linear adsorbate-adsorbate interaction terms. As one example of this scheme, we analyze TPD of CO desorption on Au(211) and Au(310) surfaces. We determine that under atmospheric CO pressure, the steps of both facets adsorb between 0.4-0.9 ML coverage of CO*. We compare this result against energies obtained from five density functionals, RPBE, PBE, PBE-D3, RPBE-D3 and BEEF-vdW. We find that the energies and equilibrium coverages from RPBE-D3 and PBE are closest to the values determined from the TPD. This dataset contains all the DFT calculations run using AiiDA.
DOI: 10.24435/materialscloud:7g-4p
title: How to extract adsorption energies, adsorbate-adsorbate interaction parameters, and saturation coverages from temperature programmed desorption experiments

description: We present a scheme to extract the adsorption energy, adsorbate interaction parameter and the saturation coverage from temperature programmed desorption (TPD) experiments. We propose that the coverage dependent adsorption energy can be fit using a functional form including the configurational entropy and linear adsorbate-adsorbate interaction terms. As one example of this scheme, we analyze TPD of CO desorption on Au(211) and Au(310) surfaces. We determine that under atmospheric CO pressure, the steps of both facets adsorb between 0.4-0.9 ML coverage of CO*. We compare this result against energies obtained from five density functionals, RPBE, PBE, PBE-D3, RPBE-D3 and BEEF-vdW. We find that the energies and equilibrium coverages from RPBE-D3 and PBE are closest to the values determined from the TPD. This dataset contains all the DFT calculations run using AiiDA.
DOI: 10.24435/materialscloud:sf-tz
title: QMrxn20: Thousands of reactants and transition states for competing E2 and SN2 reactions

description: For competing E2 and SN2 reactions, we report 4'400 validated transition state geometries and 143'200 reactant complex geometries including conformers obtained at MP2/6-311G(d) and DF-LCCSD/cc-pVTZ//MP2/6-311G(d) level of theory. The data covers the chemical compound space spanned by the substituents NO2, CN, CH3, and NH2 and early halogens (F, Cl, Br) as nucleophiles and leaving groups based on an ethane skeleton. Ready-to-use activation energies are given for the different levels of theory where in some cases relaxation effects have been treated with machine learning surrogate models.
DOI: 10.24435/materialscloud:gd-h8
title: QMrxn20: Thousands of reactants and transition states for competing E2 and SN2 reactions

description: For competing E2 and SN2 reactions, we report 4'400 validated transition state geometries and 143'200 reactant complex geometries including conformers obtained at MP2/6-311G(d) and DF-LCCSD/cc-pVTZ//MP2/6-311G(d) level of theory. The data covers the chemical compound space spanned by the substituents NO2, CN, CH3, and NH2 and early halogens (F, Cl, Br) as nucleophiles and leaving groups based on an ethane skeleton. Ready-to-use activation energies are given for the different levels of theory where in some cases relaxation effects have been treated with machine learning surrogate models.
DOI: 10.24435/materialscloud:ta-66
title: Koopmans spectral functionals: an open-source periodic-boundary implementation

description: Koopmans' spectral  functionals aim to describe simultaneously ground state properties and charged excitations of atoms, molecules, nanostructures and periodic crystals. This is achieved augmenting standard density functionals with simple but physically motivated orbital-density-dependent corrections. These corrections act on a set of localized orbitals that, in periodic systems, resembles maximally localized Wannier function. At variance with a direct supercell implementation, we discuss here i) the complex but efficient formalism required for a periodic-boundary code using explicit Brillouin zone sampling, and ii) the calculation of the screened Koopmans' corrections with density-functional perturbation theory. The implementation in the Quantum ESPRESSO distribution and the application to prototypical insulating and semiconducting systems are presented and discussed.
DOI: 10.24435/materialscloud:hn-6f
title: Heat and charge transport in H2O at ice-giant conditions from ab initio molecular dynamics simulations

description: The impact of the inner structure and thermal history of planets on their observable features, such as luminosity or magnetic field, crucially depends on the poorly known heat and charge transport properties of their internal layers. The thermal and electric conductivities of different phases of water (liquid, solid, and super-ionic) occurring in the interior of ice giant planets, such as Uranus or Neptune, are evaluated from equilibrium ab initio molecular dynamics, leveraging recent progresses in the theory and data analysis of transport in extended systems. In this record we collect the ab-initio time series of the energy flux and of the electronic and ionic charge fluxes for H2O at different planetary conditions, from which the related transport coefficients are extracted according to the Green-Kubo theory of linear response.
DOI: 10.24435/materialscloud:5f-14
title: RMapDB: chemical reaction route map data for quantum mechanical-based data chemistry

description: The record contains quantum mechanical (QM) global reaction route map (r-map) data. R-map is chemical reaction pathway networks, which compose equilibrium (EQ) and dissociation channel (DC) and transition state (TS) structures connected via intrinsic reaction coordinates (IRC) obtained by QM calculations. These are contents of RMapDB, which was published first in 2010 as a prototype and has been available online since 2014. The record contains global r-maps of three- to five-atom molecular systems. It contains also a conformational r-map of alpha-D-glucose and a global r-map of C2H4O2, which were discussed in potential energy surface-based conformational analysis and three-dimensional substructure search with the general root mean square deviation (G-RMSD) method. The RMapDB database has been developed in the RMap project for data chemistry based on QM chemical reaction route maps, together with an analytical tool RMapViewer and a server system RMapServer.
DOI: 10.24435/materialscloud:c3-r7
title: Heat and charge transport in H2O at ice-giant conditions from ab initio molecular dynamics simulations

description: The impact of the inner structure and thermal history of planets on their observable features, such as luminosity or magnetic field, crucially depends on the poorly known heat and charge transport properties of their internal layers. The thermal and electric conductivities of different phases of water (liquid, solid, and super-ionic) occurring in the interior of ice giant planets, such as Uranus or Neptune, are evaluated from equilibrium ab initio molecular dynamics, leveraging recent progresses in the theory and data analysis of transport in extended systems. In this record we collect the ab-initio time series of the energy flux and of the electronic and ionic charge fluxes for H2O at different planetary conditions, from which the related transport coefficients are extracted according to the Green-Kubo theory of linear response.
DOI: 10.24435/materialscloud:z9-cr
title: RMapDB: chemical reaction route map data for quantum mechanical-based data chemistry

description: The record contains quantum mechanical (QM) global reaction route map (r-map) data. R-map is chemical reaction pathway networks, which compose equilibrium (EQ) and dissociation channel (DC) and transition state (TS) structures connected via intrinsic reaction coordinates (IRC) obtained by QM calculations. These are contents of RMapDB, which was published first in 2010 as a prototype and has been available online since 2014. The record contains global r-maps of three- to five-atom molecular systems. It contains also a conformational r-map of alpha-D-glucose and a global r-map of C2H4O2, which were discussed in potential energy surface-based conformational analysis and three-dimensional substructure search with the general root mean square deviation (G-RMSD) method. The RMapDB database has been developed in the RMap project for data chemistry based on QM chemical reaction route maps, together with an analytical tool RMapViewer and a server system RMapServer.
DOI: 10.24435/materialscloud:g3-cp
title: Band gap engineering in blended organic semiconductor films based on dielectric interactions

description: Blending organic molecules to tune their energy levels is currently investigated as an approach to engineer the bulk and interfacial optoelectronic properties of organic semiconductors. It has been proven that the ionization energy (IE) and electron affinity (EA) can be equally shifted in the same direction by electrostatic effects controlled by blending similar halogenated derivatives with different energetics. Here, we show that the energy gap of organic semiconductors can be tuned by blending as well. We use oligothiophenes with different numbers of thiophene rings as example and investigate their structure and electronic properties. Photoelectron spectroscopy and inverse photoelectron spectroscopy show tunability of the single-particle gap, with the optical gaps showing similar, but smaller effects. Theoretical analysis shows that this tuning is mainly caused by a change in the dielectric constant with blend ratio. Further studies will explore the practical impact of this energy-level engineering strategy in optoelectronic devices.
DOI: 10.24435/materialscloud:d8-kq
title: Band gap engineering in blended organic semiconductor films based on dielectric interactions

description: Blending organic molecules to tune their energy levels is currently investigated as an approach to engineer the bulk and interfacial optoelectronic properties of organic semiconductors. It has been proven that the ionization energy (IE) and electron affinity (EA) can be equally shifted in the same direction by electrostatic effects controlled by blending similar halogenated derivatives with different energetics. Here, we show that the energy gap of organic semiconductors can be tuned by blending as well. We use oligothiophenes with different numbers of thiophene rings as example and investigate their structure and electronic properties. Photoelectron spectroscopy and inverse photoelectron spectroscopy show tunability of the single-particle gap, with the optical gaps showing similar, but smaller effects. Theoretical analysis shows that this tuning is mainly caused by a change in the dielectric constant with blend ratio. Further studies will explore the practical impact of this energy-level engineering strategy in optoelectronic devices.
DOI: 10.24435/materialscloud:sf-pv
title: Suppressed charge recombination in hematite photoanode via protonation and annealing

description: Hematite as promising photoanode for solar water splitting suffers from severe bulk and surface charge recombination. This work describes that a protonation−annealing treatment can effectively suppress both bulk and surface charge recombination in hematite. Protons/electrons are electrochemically incorporated into hematite under 0.2 VRHE followed by annealing at 120 °C. The photocurrent density increases from ∼0.9 to 1.8 mA cm−2 at 1.23 VRHE under 1 sun, and further to 2.7 mA cm−2 after loading cobalt phosphate, stabilizing at round 2.4 mA cm−2. A cathodic shift of the onset potential of photocurrent is also observed. H2O2 oxidation, impedance spectroscopy, and Mott−Schottky measurements show that the protonation suppresses bulk recombination and enhances donor density, but introducing more surface recombination. The annealing reduces surface recombination, while preserving relatively high bulk charge separation efficiency. Different from previous reports on the electrochemically reduced hematite, this work demonstrates that the performance improvement should be ascribed to the proton incorporation instead of the formation of Fe3O4 or metal Fe. This facile treatment by protonation and annealing could be applied in other semiconductors to promote the development of high performing photoelectrodes.
DOI: 10.24435/materialscloud:w0-ff
title: Suppressed charge recombination in hematite photoanode via protonation and annealing

description: Hematite as promising photoanode for solar water splitting suffers from severe bulk and surface charge recombination. This work describes that a protonation−annealing treatment can effectively suppress both bulk and surface charge recombination in hematite. Protons/electrons are electrochemically incorporated into hematite under 0.2 VRHE followed by annealing at 120 °C. The photocurrent density increases from ∼0.9 to 1.8 mA cm−2 at 1.23 VRHE under 1 sun, and further to 2.7 mA cm−2 after loading cobalt phosphate, stabilizing at round 2.4 mA cm−2. A cathodic shift of the onset potential of photocurrent is also observed. H2O2 oxidation, impedance spectroscopy, and Mott−Schottky measurements show that the protonation suppresses bulk recombination and enhances donor density, but introducing more surface recombination. The annealing reduces surface recombination, while preserving relatively high bulk charge separation efficiency. Different from previous reports on the electrochemically reduced hematite, this work demonstrates that the performance improvement should be ascribed to the proton incorporation instead of the formation of Fe3O4 or metal Fe. This facile treatment by protonation and annealing could be applied in other semiconductors to promote the development of high performing photoelectrodes.
DOI: 10.24435/materialscloud:f5-bx
title: Bayesian probabilistic assignment of chemical shifts in organic solids

description: A pre-requisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei.  Obtaining the assignment experimentally can be challenging and typically requires time-consuming multi-dimensional correlation experiments. An alternative solution for determining the assignment involves statistical analysis of experimental chemical shift databases, but no such database exists for molecular solids. Here, by combining the Cambridge structural database with a machine learning model of chemical shifts, we construct a statistical basis for probabilistic chemical shift assignment of organic crystals by calculating shifts for over 200,000 compounds, enabling the probabilistic assignment of organic crystals directly from their two-dimensional chemical structure. The approach is demonstrated with the 13C and 1H assignment of eleven molecular solids with experimental shifts, and benchmarked on 100 crystals using predicted shifts. The correct assignment was found among the two most probable assignments in over 80% of cases.
DOI: 10.24435/materialscloud:vp-ft
title: Bayesian probabilistic assignment of chemical shifts in organic solids

description: A pre-requisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei.  Obtaining the assignment experimentally can be challenging and typically requires time-consuming multi-dimensional correlation experiments. An alternative solution for determining the assignment involves statistical analysis of experimental chemical shift databases, but no such database exists for molecular solids. Here, by combining the Cambridge structural database with a machine learning model of chemical shifts, we construct a statistical basis for probabilistic chemical shift assignment of organic crystals by calculating shifts for over 200,000 compounds, enabling the probabilistic assignment of organic crystals directly from their two-dimensional chemical structure. The approach is demonstrated with the 13C and 1H assignment of eleven molecular solids with experimental shifts, and benchmarked on 100 crystals using predicted shifts. The correct assignment was found among the two most probable assignments in over 80% of cases.
DOI: 10.24435/materialscloud:vp-h5
title: Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

description: Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol-1 were achieved in predictions of the activation energy. This strategy opens the door for quickly and accurately predicting higher-selectivity catalysts for any reaction from available structural information.
DOI: 10.24435/materialscloud:b5-8r
title: Koopmans spectral functionals: an open-source periodic-boundary implementation

description: Koopmans' spectral  functionals aim to describe simultaneously ground state properties and charged excitations of atoms, molecules, nanostructures and periodic crystals. This is achieved augmenting standard density functionals with simple but physically motivated orbital-density-dependent corrections. These corrections act on a set of localized orbitals that, in periodic systems, resembles maximally localized Wannier function. At variance with a direct supercell implementation, we discuss here i) the complex but efficient formalism required for a periodic-boundary code using explicit Brillouin zone sampling, and ii) the calculation of the screened Koopmans' corrections with density-functional perturbation theory. In addition to delivering improved scaling with system size, the present development makes the calculation of band structures with Koopmans functionals straightforward. The implementation in the Quantum ESPRESSO distribution and the application to prototypical insulating and semiconducting systems are presented and discussed.
DOI: 10.24435/materialscloud:jp-2b
title: Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

description: Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol-1 were achieved in predictions of the activation energy. This strategy opens the door for quickly and accurately predicting higher-selectivity catalysts for any reaction from available structural information.
DOI: 10.24435/materialscloud:2j-jz
title: Magnetic exchange interactions in monolayer CrI₃ from many-body wavefunction calculations

description: The marked interplay between the crystalline, electronic, and magnetic structure of atomically thin magnets has been regarded as the key feature for designing next-generation magneto-optoelectronic devices. In this respect, a detailed understanding of the microscopic interactions underlying the magnetic response of these crystals is of primary importance. Here, we combine model Hamiltonians with multireference configuration interaction wavefunctions to accurately determine the strength of the spin couplings in the prototypical single-layer magnet CrI₃. Our calculations identify the (ferromagnetic) Heisenberg exchange interaction J = −1.44 meV as the dominant term, being the inter-site magnetic anisotropies substantially weaker. We also find that single-layer CrI₃ features an out-of-plane easy axis ensuing from a single-ion anisotropy A = −0.10 meV, and predict g-tensor in-plane components gxx = gyy = 1.90 and out-of-plane component gzz  = 1.92. In addition, we assess the performance of a dozen widely used density functionals against our accurate correlated wavefunctions calculations and available experimental data, thereby establishing reference results for future first-principles investigations. Overall, our findings offer a firm theoretical ground to recent experimental observations.