cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:f4-94
title: Interplay between ferroelectricity and metallicity in BaTiO3

description: We explore the interplay between ferroelectricity and metallicity, which are generally considered to be contra-indicated properties, in the prototypical ferroelectric barium titanate, BaTiO3. Using first-principles density functional theory, we calculate the effects of electron and hole doping, first by introducing a hypothetical background charge, and second through the introduction of explicit impurities (La, Nb and V for electron doping, and K, Al and Sc for hole doping). We find that, apart from a surprising increase in polarization at small hole concentrations, both charge-carrier types decrease the tendency towards ferroelectricity, with the strength of the polarization suppression, which is different for electrons and holes, determined by the detailed structure of the conduction and valence bands. Doping with impurity atoms increases the complexity and allows us to identify three factors that influence the ferroelectricity: structural effects arising largely from the size of the impurity ion, electronic effects from the introduction of charge carriers, and changes in unit-cell volume and shape. A competing balance between these contributions can result in an increase or decrease in ferroelectricity with doping.
DOI: 10.24435/materialscloud:4v-0w
title: Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water

description: The study of chemical reactions in aqueous media is very important for its implications in several fields of science, from biology to industrial processes. However, modeling these reactions is difficult when water directly participates in the reaction, since it requires a fully quantum mechanical description of the system. Ab-initio molecular dynamics is the ideal candidate to shed light on these processes. However, its scope is limited by a high computational cost. A popular alternative is to perform molecular dynamics simulations powered by machine learning potentials, trained on an extensive set of quantum mechanical calculations. Doing so reliably for reactive processes is difficult because it requires including very many intermediate and transition state configurations. In this study we used an active learning procedure accelerated by enhanced sampling to harvest such structures and to build a neural-network potential to study the urea decomposition process in water. This allowed us to obtain the free energy profiles of this important reaction in a wide range of temperatures, to discover several novel metastable states, and improve the accuracy of the kinetic rates calculations. Furthermore, we found that the formation of the zwitterionic intermediate has the same probability of occurring via an acidic or a basic pathway, which could be the cause of the insensitivity of reaction rates to the solution pH.
DOI: 10.24435/materialscloud:yr-m7
title: Interplay between ferroelectricity and metallicity in BaTiO3

description: We explore the interplay between ferroelectricity and metallicity, which are generally considered to be contra-indicated properties, in the prototypical ferroelectric barium titanate, BaTiO3. Using first-principles density functional theory, we calculate the effects of electron and hole doping, first by introducing a hypothetical background charge, and second through the introduction of explicit impurities (La, Nb and V for electron doping, and K, Al and Sc for hole doping). We find that, apart from a surprising increase in polarization at small hole concentrations, both charge-carrier types decrease the tendency towards ferroelectricity, with the strength of the polarization suppression, which is different for electrons and holes, determined by the detailed structure of the conduction and valence bands. Doping with impurity atoms increases the complexity and allows us to identify three factors that influence the ferroelectricity: structural effects arising largely from the size of the impurity ion, electronic effects from the introduction of charge carriers, and changes in unit-cell volume and shape. A competing balance between these contributions can result in an increase or decrease in ferroelectricity with doping.
DOI: 10.24435/materialscloud:90-r5
title: Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water

description: The study of chemical reactions in aqueous media is very important for its implications in several fields of science, from biology to industrial processes. However, modeling these reactions is difficult when water directly participates in the reaction, since it requires a fully quantum mechanical description of the system. Ab-initio molecular dynamics is the ideal candidate to shed light on these processes. However, its scope is limited by a high computational cost. A popular alternative is to perform molecular dynamics simulations powered by machine learning potentials, trained on an extensive set of quantum mechanical calculations. Doing so reliably for reactive processes is difficult because it requires including very many intermediate and transition state configurations. In this study we used an active learning procedure accelerated by enhanced sampling to harvest such structures and to build a neural-network potential to study the urea decomposition process in water. This allowed us to obtain the free energy profiles of this important reaction in a wide range of temperatures, to discover several novel metastable states, and improve the accuracy of the kinetic rates calculations. Furthermore, we found that the formation of the zwitterionic intermediate has the same probability of occurring via an acidic or a basic pathway, which could be the cause of the insensitivity of reaction rates to the solution pH.
DOI: 10.24435/materialscloud:m7-09
title: Insights into water permeation through hBN nanocapillaries by ab initio machine learning molecular dynamics simulations

description: Water permeation between stacked layers of hBN sheets forming 2D nanochannels is investigated using large-scale ab initio-quality molecular dynamics simulations. A high-dimensional neural network potential trained on density functional theory calculations is employed. We simulate water in van der Waals nanocapillaries and study the impact of nanometric confinement on the structure and dynamics of water using both equilibrium and nonequilibrium methods. At an interlayer distance of 10.2 Å confinement induces a first-order phase transition resulting in a well-defined AA-stacked bilayer of hexagonal ice. In contrast, for h < 9 Å, the 2D water monolayer consists of a mixture of different locally ordered patterns of squares, pentagons, and hexagons. We found a significant change in the transport properties of confined water, particularly for monolayer water where the water−solid friction coefficient decreases to half and the diffusion coefficient increases by a factor of 4 as compared to bulk water. Accordingly, the slip-velocity is found to increase under confinement and we found that the overall permeation is dominated by monolayer water adjacent to the hBN membranes at extreme confinements. We conclude that monolayer water in addition to bilayer ice has a major contribution to water transport through 2D nanochannels.
DOI: 10.24435/materialscloud:ag-t9
title: Insights into water permeation through hBN nanocapillaries by ab initio machine learning molecular dynamics simulations

description: Water permeation between stacked layers of hBN sheets forming 2D nanochannels is investigated using large-scale ab initio-quality molecular dynamics simulations. A high-dimensional neural network potential trained on density functional theory calculations is employed. We simulate water in van der Waals nanocapillaries and study the impact of nanometric confinement on the structure and dynamics of water using both equilibrium and nonequilibrium methods. At an interlayer distance of 10.2 Å confinement induces a first-order phase transition resulting in a well-defined AA-stacked bilayer of hexagonal ice. In contrast, for h < 9 Å, the 2D water monolayer consists of a mixture of different locally ordered patterns of squares, pentagons, and hexagons. We found a significant change in the transport properties of confined water, particularly for monolayer water where the water−solid friction coefficient decreases to half and the diffusion coefficient increases by a factor of 4 as compared to bulk water. Accordingly, the slip-velocity is found to increase under confinement and we found that the overall permeation is dominated by monolayer water adjacent to the hBN membranes at extreme confinements. We conclude that monolayer water in addition to bilayer ice has a major contribution to water transport through 2D nanochannels.
DOI: 10.24435/materialscloud:wg-d5
title: Carrier lifetimes and polaronic mass enhancement in the hybrid halide perovskite CH₃NH₃PbI₃ from multiphonon Fröhlich coupling

description: We elucidate the nature of the electron-phonon interaction in the archetypal hybrid perovskite CH₃NH₃PbI₃ using ab initio many-body calculations and an exactly solvable model. We demonstrate that electrons and holes near the band edges primarily interact with three distinct groups of longitudinal-optical vibrations, in order of importance: the stretching of the Pb-I bond, the bending of the Pb-I-Pb bonds, and the libration of the organic cations. These polar phonons induce ultrafast intraband carrier relaxation over timescales of 6–30 fs and yield polaron effective masses 28% heavier than the bare band masses. These findings allow us to rationalize previous experimental observations and provide a key to understanding carrier dynamics in halide perovskites.
DOI: 10.24435/materialscloud:h6-eg
title: Carrier lifetimes and polaronic mass enhancement in the hybrid halide perovskite CH₃NH₃PbI₃ from multiphonon Fröhlich coupling

description: We elucidate the nature of the electron-phonon interaction in the archetypal hybrid perovskite CH₃NH₃PbI₃ using ab initio many-body calculations and an exactly solvable model. We demonstrate that electrons and holes near the band edges primarily interact with three distinct groups of longitudinal-optical vibrations, in order of importance: the stretching of the Pb-I bond, the bending of the Pb-I-Pb bonds, and the libration of the organic cations. These polar phonons induce ultrafast intraband carrier relaxation over timescales of 6–30 fs and yield polaron effective masses 28% heavier than the bare band masses. These findings allow us to rationalize previous experimental observations and provide a key to understanding carrier dynamics in halide perovskites.
DOI: 10.24435/materialscloud:c2-5a
title: The new perspective for the superconductor

description: The superconductor theory based on the electron pair is reviewed and several viewpoints are proposed. A demonstrated case reveals the speed of each electron in the electron pair at Fermi level about 1.82x10^6 m/s in Pb. However, the fastest longitudinal and transverse speeds of crystal waves in Pb at 0 K are 2.18x10^3 m/s and 1.29x10^3 m/s in [100] direction, respectively. It seems to be very hard even impossible that the mediated phonon can real-time transfer momentum and energy between two so high-speed and antiparallel-momentum electrons in the superconducting state. In this research, we focus on single electron based on the experiments of Transmission Electron Microscopy. The new fitting temperature-dependent model for the London penetration depth is proposed. This model is much better than the one- and two-gap models and matches three experimental data much well. Then it further gives the temperature-dependent effective electron mass for the Nb superconductor film. Finally, the expression for the resistivity is deduced which can explain why the resistance is almost zero in the superconductor. All these new results are obtained by using the concept of single electron.
DOI: 10.24435/materialscloud:xj-bb
title: On-surface activation of benzylic C-H bonds for the synthesis of pentagon-fused graphene nanoribbons

description: In the record we provide the inputs and outputs for the calculations that support our recent results in the synthesis of pentagon-fused graphene nanoribbons (GNRs). GNRs have potential for applications in electronic devices. A key issue, thereby, is the fine-tuning of their electronic characteristics, which can be achieved through subtle structural modifications. These are not limited to the conventional armchair, zigzag, and cove edges, but also possible through incorporation of non-hexagonal rings. On-surface synthesis enables the fabrication and visualization of GNRs with atomically precise chemical structures, but strategies for the incorporation of non-hexagonal rings have been underexplored. In the manuscript, we describe the on-surface synthesis of armchair-edged GNRs with incorporated five-membered rings through the C-H activation and cyclization of benzylic methyl groups. ortho-Tolyl-substituted dibromobianthryl was employed as the precursor monomer, and visualization of the resulting structures after annealing at 300 °C on a gold surface by high-resolution noncontact atomic force microscopy clearly revealed the formation of methylene-bridged pentagons at the GNR edges. These persisted after annealing at 340 °C, along with a few fully conjugated pentagons having singly-hydrogenated apexes. The benzylic methyl groups could also migrate or cleave-off, resulting in defects lacking the five-membered rings. Moreover, unexpected and unique structural rearrangements, including the formation of embedded heptagons, were observed, providing novel insights into the on-surface reactions en route to functional carbon nanomaterials.
DOI: 10.24435/materialscloud:vs-cv
title: On-surface activation of benzylic C-H bonds for the synthesis of pentagon-fused graphene nanoribbons

description: In the record we provide the inputs and outputs for the calculations that support our recent results in the synthesis of pentagon-fused graphene nanoribbons (GNRs). GNRs have potential for applications in electronic devices. A key issue, thereby, is the fine-tuning of their electronic characteristics, which can be achieved through subtle structural modifications. These are not limited to the conventional armchair, zigzag, and cove edges, but also possible through incorporation of non-hexagonal rings. On-surface synthesis enables the fabrication and visualization of GNRs with atomically precise chemical structures, but strategies for the incorporation of non-hexagonal rings have been underexplored. In the manuscript, we describe the on-surface synthesis of armchair-edged GNRs with incorporated five-membered rings through the C-H activation and cyclization of benzylic methyl groups. ortho-Tolyl-substituted dibromobianthryl was employed as the precursor monomer, and visualization of the resulting structures after annealing at 300 °C on a gold surface by high-resolution noncontact atomic force microscopy clearly revealed the formation of methylene-bridged pentagons at the GNR edges. These persisted after annealing at 340 °C, along with a few fully conjugated pentagons having singly-hydrogenated apexes. The benzylic methyl groups could also migrate or cleave-off, resulting in defects lacking the five-membered rings. Moreover, unexpected and unique structural rearrangements, including the formation of embedded heptagons, were observed, providing novel insights into the on-surface reactions en route to functional carbon nanomaterials.
DOI: 10.24435/materialscloud:gg-mx
title: Structure determination of an amorphous drug through large-scale NMR predictions

description: Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous molecular solids has so far not been possible. Solid-state NMR is among the most popular methods to characterize amorphous materials, and Molecular Dynamics (MD) simulations can help describe the structure of disordered materials. However, directly relating MD to NMR experiments in molecular solids has been out of reach until now because of the large size of these simulations. Here, using a machine learning model of chemical shifts, we determine the atomic-level structure of the hydrated amorphous drug AZD5718 by combining dynamic nuclear polarization-enhanced solid-state NMR experiments with predicted chemical shifts for MD simulations of large systems. From these amorphous structures we then identify H-bonding motifs and relate them to local intermolecular complex formation energies.
DOI: 10.24435/materialscloud:s6-nq
title: Randomly-displaced methane configurations

description: Most of the datasets to benchmark machine-learning models contain minimum-energy structures, or small fluctuations around stable geometries, and focus on the diversity of chemical compositions, or the presence of different phases. This dataset provides a large number (7732489) configurations for a simple CH4 composition, that are generated in an almost completely unbiased fashion. Hydrogen atoms are randomly distributed in a 3A sphere centered around the carbon atom, and the only structures that are discarded are those with atoms that are closer than 0.5A, or such that the reference DFT calculation does not converge.
This dataset is ideal to benchmark structural representations and regression algorithms, verifying whether they allow reaching arbitrary accuracy in the data rich regime.
DOI: 10.24435/materialscloud:x7-fg
title: Structure determination of an amorphous drug through large-scale NMR predictions

description: Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous molecular solids has so far not been possible. Solid-state NMR is among the most popular methods to characterize amorphous materials, and Molecular Dynamics (MD) simulations can help describe the structure of disordered materials. However, directly relating MD to NMR experiments in molecular solids has been out of reach until now because of the large size of these simulations. Here, using a machine learning model of chemical shifts, we determine the atomic-level structure of the hydrated amorphous drug AZD5718 by combining dynamic nuclear polarization-enhanced solid-state NMR experiments with predicted chemical shifts for MD simulations of large systems. From these amorphous structures we then identify H-bonding motifs and relate them to local intermolecular complex formation energies.
DOI: 10.24435/materialscloud:9a-7y
title: Biomimetic high performance artificial muscle built on sacrificial coordination network and mechanical training process

description: Artificial muscle materials promise incredible applications in actuators, robotics and medical apparatus, yet the ability to mimic the full characteristics of skeletal muscles into synthetic materials remains a huge challenge. Herein, inspired by the dynamic sacrificial bonds in biomaterials and the self-strengthening of skeletal muscles by physical exercise, high performance artificial muscle material is prepared by rearrangement of sacrificial coordination bonds in the polyolefin elastomer via a repetitive mechanical training process. Biomass lignin is incorporated as a green reinforcer for the construction of interfacial coordination bonds. The prepared artificial muscle material exhibits high actuation strain (>40%), high actuation stress (1.5 MPa) which can lift more than 10000 times its own weight with 30% strain, characteristics of excellent self-strengthening by mechanical training, strain-adaptive stiffening, and heat/electric programmable actuation performance. In this work, we show a facile strategyfor the fabrication of intelligent materials using easily available raw materials.
DOI: 10.24435/materialscloud:pc-9x
title: Biomimetic high performance artificial muscle built on sacrificial coordination network and mechanical training process

description: Artificial muscle materials promise incredible applications in actuators, robotics and medical apparatus, yet the ability to mimic the full characteristics of skeletal muscles into synthetic materials remains a huge challenge. Herein, inspired by the dynamic sacrificial bonds in biomaterials and the self-strengthening of skeletal muscles by physical exercise, high performance artificial muscle material is prepared by rearrangement of sacrificial coordination bonds in the polyolefin elastomer via a repetitive mechanical training process. Biomass lignin is incorporated as a green reinforcer for the construction of interfacial coordination bonds. The prepared artificial muscle material exhibits high actuation strain (>40%), high actuation stress (1.5 MPa) which can lift more than 10000 times its own weight with 30% strain, characteristics of excellent self-strengthening by mechanical training, strain-adaptive stiffening, and heat/electric programmable actuation performance. In this work, we show a facile strategyfor the fabrication of intelligent materials using easily available raw materials.
DOI: 10.24435/materialscloud:8z-2p
title: Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation

description: The on-top pair density [Π(r)] is a local quantum chemical property, which reflects the probability of two electrons of any spin to occupy the same position in space. Simplest quantity related to the two-particles density matrix, the on-top pair density is a powerful indicator of electron correlation effects and, as such, it has been extensively used to combine density functional theory and multireference wavefunction theory. The widespread application of Π(r) is currently hindered by the need for post-Hartree-Fock or multireference computations for its accurate evaluation. In this work, we propose the construction of a machine learning model capable of predicting the CASSCF-quality on-top pair density of a molecule only from its structure and composition. Our model, trained on the GDB11-AD-3165 database, is able to predict with minimal error the on-top pair density of organic molecules bypassing completely the need for ab-initio computations. The accuracy of the regression is demonstrated using the on-top ratio as a visual metric of electron correlation effects and bond-breaking in real-space. In addition, we report the construction of a specialized basis set, built to fit the on-top pair density in a single, atom-centered expansion. This basis, cornerstone of the regression, could be potentially used also in the same spirit of the resolution-of-the-identity approximation for the electron density.
DOI: 10.24435/materialscloud:1g-kb
title: Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations

description: We study the phase equilibrium between liquid water and ice Ih modeled by the TIP4P/Ice interatomic potential using enhanced sampling molecular dynamics simulations. Our approach is based on the calculation of ice Ih-liquid free energy differences from simulations that visit reversibly both phases. The reversible interconversion is achieved by introducing a static bias potential as a function of an order parameter. The order parameter was tailored to crystallize the hexagonal diamond structure of oxygen in ice Ih. We analyze the effect of the system size on the ice Ih-liquid free energy differences, and we obtain a melting temperature of 270 K in the thermodynamic limit. This result is in agreement with estimates from thermodynamic integration (272 K) and coexistence simulations (270 K). Since the order parameter does not include information about the coordinates of the protons, the spontaneously formed solid configurations contain proton disorder as expected for ice Ih.