cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:9m-4n
title: Modeling of precipitate strengthening with near-chemical accuracy: case study of Al-6xxx alloys

description: Many metal alloys are strengthened by controlling precipitation to achieve an optimal peak-aged condition where the strength-limiting processes of precipitate shearing and Orowan looping are thought to be comparable. Qualitative models have long captured the basic mechanisms but realistic predictions have been challenging due to both the lack of accurate material parameters and an inability to quantitatively validate the models. Here, dislocation/precipitate interaction mechanisms are studied in Al-6xxx Al–Mg–Si alloys using atomistic simulations in tandem with a near-chemically-accurate Al–Mg–Si neural network interatomic potentials. Results show that a given precipitate can exhibit shearing or looping depending on the relative orientation of the precipitate and dislocation, as influenced by the matrix and precipitate coherency stresses, direction-dependence of precipitate shearing energies, and dislocation line tension. Analytic models for shearing and calibrated discrete dislocation models of looping accurately capture the trends and magnitudes of strengthening in most cases. Good agreement with experiments is then approached by using the theories together with the more-accurate first-principles material properties. The combination of theories and simulations demonstrated here constitutes a path for understanding and predicting the role of chemistry and microstructure on alloy strength that can be applied in many different alloys.
DOI: 10.24435/materialscloud:yf-wc
title: Data-driven discovery of organic electronic materials enabled by hybrid top-down/bottom-up design

description: The high-throughput molecular exploration and screening of organic electronic materials often starts with either a 'top-down' mining of existing repositories, or the 'bottom-up' assembly of fragments based on predetermined rules and known synthetic templates. In both instances, the datasets used are often produced on a case-by-case basis, and require the high-quality computation of electronic properties and extensive user input: curation in the top-down approach, and the construction of a fragment library and introduction of rules for linking them in the bottom-up approach. Both approaches are time-consuming and require significant computational resources. Here, we generate a top-down set named FORMED consisting of 117K synthesized molecules containing their optimized structures, associated electronic and topological properties and chemical composition, and use these structures as a vast library of molecular building blocks for bottom-up fragment-based materials design. A tool is developed to automate the coupling of these building block units based on their available Csp2-H bonds, thus providing a fundamental link between the two philosophies of dataset construction. Statistical models are trained on this dataset and a subset of the resulting hybrid top-down/bottom-up compounds (selected dimers), which enable on-the-fly prediction of key ground state (frontier molecular orbital gaps) and excited state (S1 and T1 energies) properties from molecular geometries with high accuracy across all known p-block organic compound space.
With access to ab initio-quality optical properties in hand, it is possible to apply this bottom-up pipeline using existing compounds as molecular building blocks to any materials design campaign. To illustrate this, we construct and screen over a million molecular candidates (predicted dimers) for efficient intramolecular singlet fission, the leading candidates of which provide insight into the structural features that may promote this multiexciton-generating process.
DOI: 10.24435/materialscloud:3z-qr
title: Modeling of precipitate strengthening with near-chemical accuracy: case study of Al-6xxx alloys

description: Many metal alloys are strengthened by controlling precipitation to achieve an optimal peak-aged condition where the strength-limiting processes of precipitate shearing and Orowan looping are thought to be comparable. Qualitative models have long captured the basic mechanisms but realistic predictions have been challenging due to both the lack of accurate material parameters and an inability to quantitatively validate the models. Here, dislocation/precipitate interaction mechanisms are studied in Al-6xxx Al–Mg–Si alloys using atomistic simulations in tandem with a near-chemically-accurate Al–Mg–Si neural network interatomic potentials. Results show that a given precipitate can exhibit shearing or looping depending on the relative orientation of the precipitate and dislocation, as influenced by the matrix and precipitate coherency stresses, direction-dependence of precipitate shearing energies, and dislocation line tension. Analytic models for shearing and calibrated discrete dislocation models of looping accurately capture the trends and magnitudes of strengthening in most cases. Good agreement with experiments is then approached by using the theories together with the more-accurate first-principles material properties. The combination of theories and simulations demonstrated here constitutes a path for understanding and predicting the role of chemistry and microstructure on alloy strength that can be applied in many different alloys.
DOI: 10.24435/materialscloud:2w-ev
title: Total energies of atoms from integral-equation radial solver

description: We present a numerical tool for solving the non-relativistic Kohn-Sham problem for spherically-symmetric atoms. It treats the Schrödinger equation as an integral equation relying heavily on convolutions. The solver supports different types of exchange-correlation functionals including screened and long-range corrected hybrids. We implement a new method for treating range separation based on the complementary error function kernel. The present tool is applied in spin-restricted non-relativistic total energy calculations of atoms. A comparison with ultra-precise reference data[Cinal, JOMC 58, 1571 (2020)] shows a 14-digit agreement for Hartree-Fock results. We provide further benchmark data obtained with 5 different exchange-correlation functionals: VWN5 (the local-density approximation), PBE (the generalized gradient approximation), PBE0 and B3LYP (hybrids with a Fock exchange) and LC-BLYP (hybrid with a long-range corrected exchange).
DOI: 10.24435/materialscloud:1b-1j
title: Total energies of atoms from integral-equation radial solver

description: We present a numerical tool for solving the non-relativistic Kohn-Sham problem for spherically-symmetric atoms. It treats the Schrödinger equation as an integral equation relying heavily on convolutions. The solver supports different types of exchange-correlation functionals including screened and long-range corrected hybrids. We implement a new method for treating range separation based on the complementary error function kernel. The present tool is applied in spin-restricted non-relativistic total energy calculations of atoms. A comparison with ultra-precise reference data[Cinal, JOMC 58, 1571 (2020)] shows a 14-digit agreement for Hartree-Fock results. We provide further benchmark data obtained with 5 different exchange-correlation functionals: VWN5 (the local-density approximation), PBE (the generalized gradient approximation), PBE0 and B3LYP (hybrids with a Fock exchange) and LC-BLYP (hybrid with a long-range corrected exchange).
DOI: 10.24435/materialscloud:44-pc
title: What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO2 reduction? A data-driven retrospective

description: We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict Faradaic Efficiencies for 11 CO2RR products, as a function of the applied voltage at which the reaction takes place, and the relative amounts of non equivalent surface sites, distinguished according to their nominal coordination. A satisfactory model accuracy is found only when discriminating data according to their provenance. On one hand, this result points at a qualitative agreement across reported experimental CO2RR  trends for single-crystal surfaces with well-defined terminations. On the other, this finding hints at the presence of differences in nominally identical catalysts and/or CO2RR measurements, which result in quantitative disagreement between experiments.
DOI: 10.24435/materialscloud:3w-0e
title: Fluctuations and correlations of local topological order parameters in disordered two-dimensional topological insulators

description: Two-dimensional topological insulators are characterized by an insulating bulk and conductive edge states protected by the nontrivial topology of the bulk electronic structure. They remain robust against moderate disorder until Anderson localization occurs and destroys the topological phase. Interestingly, disorder can also induce a topological phase—known as a topological Anderson insulator—starting from an otherwise pristine trivial phase. While topological invariants are generally regarded as global quantities, we argue that space-resolved topological markers can act as local order parameters, revealing the role of fluctuations and correlations in the local topology under Anderson disorder and vacancies. With this perspective, we perform numerical simulations of disorder-driven topological phase transitions in the Haldane and Kane-Mele models, using supercells with both open and periodic boundary conditions. We find that short-scale fluctuations of topological markers vanish upon coarse-graining, except at the topological phase transition, where their correlation length peaks and large-scale fluctuations remain.
DOI: 10.24435/materialscloud:nd-50
title: What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO2 reduction? A data-driven retrospective

description: We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict Faradaic Efficiencies for 11 CO2RR products, as a function of the applied voltage at which the reaction takes place, and the relative amounts of non equivalent surface sites, distinguished according to their nominal coordination. A satisfactory model accuracy is found only when discriminating data according to their provenance. On one hand, this result points at a qualitative agreement across reported experimental CO2RR  trends for single-crystal surfaces with well-defined terminations. On the other, this finding hints at the presence of differences in nominally identical catalysts and/or CO2RR measurements, which result in quantitative disagreement between experiments.
DOI: 10.24435/materialscloud:sv-ak
title: What do we talk about, when we talk about single-crystal termination-dependent selectivity of Cu electrocatalysts for CO2 reduction? A data-driven retrospective

description: We mine from the literature experimental data on the CO2 electrochemical reduction selectivity of Cu single crystal surfaces. We then probe the accuracy of a machine learning model trained to predict Faradaic Efficiencies for 11 CO2RR products, as a function of the applied voltage at which the reaction takes place, and the relative amounts of non equivalent surface sites, distinguished according to their nominal coordination. A satisfactory model accuracy is found only when discriminating data according to their provenance. On one hand, this result points at a qualitative agreement across reported experimental CO2RR  trends for single-crystal surfaces with well-defined terminations. On the other, this finding hints at the presence of differences in nominally identical catalysts and/or CO2RR measurements, which result in quantitative disagreement between experiments.
DOI: 10.24435/materialscloud:tm-4t
title: Ultrafast frustration-breaking and magnetophononic driving of singlet excitations in a quantum magnet

description: Ideal magnetic frustration forms the basis for the emergence of exotic quantum spin states that are entirely nonmagnetic. Such singlet spin states are the defining feature of the Shastry-Sutherland model, and of its faithful materials realization in the quantum antiferromagnet SrCu₂(BO₃)₂. To address these states on ultrafast timescales, despite their lack of any microscopic order parameter, we introduce a nonlinear magnetophononic mechanism to alter the quantum spin dynamics by driving multiple optical phonon modes coherently and simultaneously. We apply intense terahertz pulses to create a nonequilibrium modulation of the magnetic interactions that breaks the ideal frustration of SrCu₂(BO₃)₂, such that previously forbidden physics can be driven in a coherent manner. Specifically, this driving populates a purely magnetic excitation, the singlet branch of the two-triplon bound state, by resonance with the difference frequency of two pumped phonons. Our results demonstrate how light-driven phonons can be used for the ultrafast and selective manipulation of interactions in condensed matter, even at frequencies far from those of the pump spectrum, offering valuable additional capabilities for the dynamical control of quantum many-body phenomena.
DOI: 10.24435/materialscloud:a7-59
title: Pure isotropic proton NMR spectra in solids using deep learning

description: The resolution of proton solid-state NMR spectra is usually limited by broadening arising from dipolar interactions between spins. Magic-angle spinning alleviates this broadening by inducing coherent averaging. However, even the highest spinning rates experimentally accessible today are not able to completely remove dipolar interactions. Here, we introduce a deep learning approach to determine pure isotropic proton spectra from a two-dimensional set of magic-angle spinning spectra acquired at different spinning rates. Applying the model to 8 organic solids yields high-resolution 1H solid-state NMR spectra with isotropic linewidths in the 50-400 Hz range.
DOI: 10.24435/materialscloud:jk-zx
title: Pure isotropic proton NMR spectra in solids using deep learning

description: The resolution of proton solid-state NMR spectra is usually limited by broadening arising from dipolar interactions between spins. Magic-angle spinning alleviates this broadening by inducing coherent averaging. However, even the highest spinning rates experimentally accessible today are not able to completely remove dipolar interactions. Here, we introduce a deep learning approach to determine pure isotropic proton spectra from a two-dimensional set of magic-angle spinning spectra acquired at different spinning rates. Applying the model to 8 organic solids yields high-resolution 1H solid-state NMR spectra with isotropic linewidths in the 50-400 Hz range.
DOI: 10.24435/materialscloud:rw-rs
title: Thermal conductivity of glasses above the plateau: first-principles theory and applications

description: Predicting the thermal conductivity of glasses from first principles has hitherto been a prohibitively complex problem. In fact, past works have highlighted challenges in achieving computational convergence with respect to length and/or time scales using either the established Allen-Feldman or Green-Kubo formulations, endorsing the concept that atomistic models containing thousands of atoms — thus beyond the capabilities of first-principles calculations — are needed to describe the thermal conductivity of glasses. In addition, these established formulations either neglect anharmonicity (Allen-Feldman) or miss the Bose-Einstein statistics of atomic vibrations (Green Kubo), thus leaving open the question on the relevance of these effects. Here, we present a first-principles formulation to address the thermal conductivity of glasses above the plateau, which can account comprehensively for the effects of structural disorder, anharmonicity, and quantum Bose-Einstein statistics. The protocol combines the Wigner formulation of thermal transport with convergence-acceleration techniques, and is validated in vitreous silica using both first-principles calculations and a quantum-accurate machine-learned interatomic potential. We show that models of vitreous silica containing less than 200 atoms can already reproduce the thermal conductivity in the macroscopic limit and that anharmonicity negligibly affects heat transport in vitreous silica. We discuss the microscopic quantities that determine the trend of the conductivity at high temperature, highlighting the agreement of the calculations with experiments in the temperature range above the plateau where radiative effects remain negligible (50<∼T <∼450 K).
DOI: 10.24435/materialscloud:48-1r
title: Thermal conductivity of glasses: first-principles theory and applications

description: Predicting the thermal conductivity of glasses from first principles has hitherto been a very complex problem. The established Allen-Feldman and Green-Kubo approaches employ approximations with limited validity--the former neglects anharmonicity, the latter misses the quantum Bose-Einstein statistics of vibrations--and require atomistic models that are very challenging for first principles methods. Here, we present a protocol to determine from first-principles the thermal conductivity k(T) of glasses above the plateau (i.e., above the temperature-independent region appearing almost without exceptions in the k(T) of all glasses at cryogenic temperatures). The protocol combines the Wigner formulation of thermal transport with convergence-acceleration techniques, and accounts comprehensively for the effects of structural disorder, anharmonicity, and Bose-Einstein statistics. We validate this approach in vitreous silica, showing that models containing less than 200 atoms can already reproduce k(T) in the macroscopic limit. We discuss the effects of anharmonicity and the mechanisms determining the trend of k(T) at high temperature, reproducing experiments at temperatures where radiative effects remain negligible.
DOI: 10.24435/materialscloud:jz-tf
title: Thermal conductivity of glasses: first-principles theory and applications

description: Predicting the thermal conductivity of glasses from first principles has hitherto been a very complex problem. The established Allen-Feldman and Green-Kubo approaches employ approximations with limited validity--the former neglects anharmonicity, the latter misses the quantum Bose-Einstein statistics of vibrations--and require atomistic models that are very challenging for first principles methods. Here, we present a protocol to determine from first-principles the thermal conductivity k(T) of glasses above the plateau (i.e., above the temperature-independent region appearing almost without exceptions in the k(T) of all glasses at cryogenic temperatures). The protocol combines the Wigner formulation of thermal transport with convergence-acceleration techniques, and accounts comprehensively for the effects of structural disorder, anharmonicity, and Bose-Einstein statistics. We validate this approach in vitreous silica, showing that models containing less than 200 atoms can already reproduce k(T) in the macroscopic limit. We discuss the effects of anharmonicity and the mechanisms determining the trend of k(T) at high temperature, reproducing experiments at temperatures where radiative effects remain negligible.
DOI: 10.24435/materialscloud:w2-g5
title: Raman spectra of 2D titanium carbide MXene from machine-learning force field molecular dynamics

description: MXenes represent one of the largest class of 2D materials with promising applications in many fields and their properties tunable by the surface group composition. Raman spectroscopy is expected to yield rich information about the surface composition, but the interpretation of measured spectra has proven challenging. The interpretation is usually done via comparison to simulated spectra, but there are large discrepancies between the experimental and earlier simulated spectra. In this work, we develop a computational approach to simulate Raman spectra of complex materials that combines machine-learning force-field molecular dynamics and reconstruction of Raman tensors via projection to pristine system modes. The approach can account for the effects of finite temperature, mixed surfaces, and disorder. We apply our approach to simulate Raman spectra of titanium carbide MXene and show that all these effects must be included in order to properly reproduce the experimental spectra, in particular the broad features. We discuss the origin of the peaks and how they evolve with surface composition, which can then be used to interpret experimental results.

This record contains input files for MLFF training and production runs, information on the training set (atomic structures, energies and forces) and some of the molecular dynamics trajectories used to obtain Raman spectra.
DOI: 10.24435/materialscloud:gs-zq
title: Raman spectra of 2D titanium carbide MXene from machine-learning force field molecular dynamics

description: MXenes represent one of the largest class of 2D materials with promising applications in many fields and their properties tunable by the surface group composition. Raman spectroscopy is expected to yield rich information about the surface composition, but the interpretation of measured spectra has proven challenging. The interpretation is usually done via comparison to simulated spectra, but there are large discrepancies between the experimental and earlier simulated spectra. In this work, we develop a computational approach to simulate Raman spectra of complex materials that combines machine-learning force-field molecular dynamics and reconstruction of Raman tensors via projection to pristine system modes. The approach can account for the effects of finite temperature, mixed surfaces, and disorder. We apply our approach to simulate Raman spectra of titanium carbide MXene and show that all these effects must be included in order to properly reproduce the experimental spectra, in particular the broad features. We discuss the origin of the peaks and how they evolve with surface composition, which can then be used to interpret experimental results.

This record contains input files for MLFF training and production runs, information on the training set (atomic structures, energies and forces) and some of the molecular dynamics trajectories used to obtain Raman spectra.
DOI: 10.24435/materialscloud:h9-sr
title: Growth optimization and device integration of narrow-bandgap graphene nanoribbons

description: The electronic, optical, and magnetic properties of graphene nanoribbons (GNRs) can be engineered by controlling their edge structure and width with atomic precision through bottom-up fabrication based on molecular precursors. This approach offers a unique platform for all-carbon electronic devices but requires careful optimization of the growth conditions to match structural requirements for successful device integration, with GNR length being the most critical parameter. In a recent work we study, the growth, characterization, and device integration of 5-atom wide armchair GNRs (5-AGNRs), which are expected to have an optimal bandgap as active material in switching devices. 5-AGNRs are obtained via on-surface synthesis under ultrahigh vacuum conditions from Br- and I-substituted precursors. It is shown that the use of I-substituted precursors and the optimization of the initial precursor coverage quintupled the average 5-AGNR length. This significant length increase allowed the integration of 5-AGNRs into devices and the realization of the first field-effect transistor based on narrow bandgap AGNRs that shows switching behavior at room temperature. The study highlights that the optimized growth protocols can successfully bridge between the sub-nanometer scale, where atomic precision is needed to control the electronic properties, and the scale of tens of nanometers relevant for successful device integration of GNRs. The record contains data supporting the results presented in the publication