cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:kp-82
title: Local kernel regression and neural network approaches to the conformational landscapes of oligopeptides

description: The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been proposed, ranging from simple linear models to kernel regression and highly nonlinear neural networks. Here we apply two widely different approaches to the same, challenging problem - the sampling of the conformational landscape of polypeptides at finite temperature. We develop a Local Kernel Regression (LKR) coupled with a supervised sparsity method and compare it with a more established approach based on Behler-Parrinello type Neural Networks. In the context of the LKR, we discuss how the supervised selection of the reference pool of environments is crucial to achieve accurate potential energy surfaces at a competitive computational cost and leverage the locality of the model to infer which chemical environments are poorly described by the DFTB baseline. We then discuss the relative merits of the two frameworks and perform Hamiltonian-reservoir replica-exchange Monte Carlo sampling and metadynamics simulations, respectively, to demonstrate that both frameworks can achieve converged and transferable sampling of the conformational landscape of complex and flexible biomolecules with comparable accuracy and computational cost.
DOI: 10.24435/materialscloud:bk-z0
title: Local kernel regression and neural network approaches to the conformational landscapes of oligopeptides

description: The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been proposed, ranging from simple linear models to kernel regression and highly nonlinear neural networks. Here we apply two widely different approaches to the same, challenging problem - the sampling of the conformational landscape of polypeptides at finite temperature. We develop a Local Kernel Regression (LKR) coupled with a supervised sparsity method and compare it with a more established approach based on Behler-Parrinello type Neural Networks. In the context of the LKR, we discuss how the supervised selection of the reference pool of environments is crucial to achieve accurate potential energy surfaces at a competitive computational cost and leverage the locality of the model to infer which chemical environments are poorly described by the DFTB baseline. We then discuss the relative merits of the two frameworks and perform Hamiltonian-reservoir replica-exchange Monte Carlo sampling and metadynamics simulations, respectively, to demonstrate that both frameworks can achieve converged and transferable sampling of the conformational landscape of complex and flexible biomolecules with comparable accuracy and computational cost.
DOI: 10.24435/materialscloud:wy-bn
title: Reaction pathway towards 7-atom-wide armchair graphene nanoribbon formation and identification of intermediate species on Au(111)

description: In this record we provide data supporitng our recent results discussed in the characterization of the fabrication process of graphene nanoribbons. The prototypical surface reaction of 10,10′-dibromo-9,9′-bianthryl (DBBA) toward the seven-atom-wide armchair graphene nanoribbon (7-AGNR) on the Au(111) surface has been investigated by means of vibrational spectroscopy, thermal desorption spectroscopy, and density functional theory. Specifically, a direct correlation between annealing temperature and the formation of various intermediate species is derived. By comparison of IR spectra with results from DFT calculations, an identification of reaction intermediates has been achieved, allowing for a precise mapping of individual reaction steps. Thereby, we identify a prior unknown partially dehalogenated and strongly tilted DBBA* monoradical species (DBBA-1Br) after mild annealing (380–450 K). This inclined adsorption geometry stabilizes the second Br atom, preventing full dehalogenation. According to our calculations, dimerization of DBBA-1Br reverses this inclination and provides an efficient way to enable abstraction of the second Br atom and initiate polyanthracene chain formation by means of DBBA* addition. As a competitive process to dimerization, a small number of the DBBA* monoradicals recombine with previously released bromine atoms, leading to associative DBBA desorption. In the course of thermal processing, hydrogen release signals planarization of the twisted anthracene units of DBBA by means of cyclodehydrogenation and formation of the final 7-AGNR. Based on the results of this work, an in-depth understanding of the on-surface synthesis of 7-AGNR on Au(111), starting with a DBBA molecular precursor, has been attained.
DOI: 10.24435/materialscloud:p0-jb
title: Reaction pathway towards 7-atom-wide armchair graphene nanoribbon formation and identification of intermediate species on Au(111)

description: In this record we provide data supporitng our recent results discussed in the characterization of the fabrication process of graphene nanoribbons. The prototypical surface reaction of 10,10′-dibromo-9,9′-bianthryl (DBBA) toward the seven-atom-wide armchair graphene nanoribbon (7-AGNR) on the Au(111) surface has been investigated by means of vibrational spectroscopy, thermal desorption spectroscopy, and density functional theory. Specifically, a direct correlation between annealing temperature and the formation of various intermediate species is derived. By comparison of IR spectra with results from DFT calculations, an identification of reaction intermediates has been achieved, allowing for a precise mapping of individual reaction steps. Thereby, we identify a prior unknown partially dehalogenated and strongly tilted DBBA* monoradical species (DBBA-1Br) after mild annealing (380–450 K). This inclined adsorption geometry stabilizes the second Br atom, preventing full dehalogenation. According to our calculations, dimerization of DBBA-1Br reverses this inclination and provides an efficient way to enable abstraction of the second Br atom and initiate polyanthracene chain formation by means of DBBA* addition. As a competitive process to dimerization, a small number of the DBBA* monoradicals recombine with previously released bromine atoms, leading to associative DBBA desorption. In the course of thermal processing, hydrogen release signals planarization of the twisted anthracene units of DBBA by means of cyclodehydrogenation and formation of the final 7-AGNR. Based on the results of this work, an in-depth understanding of the on-surface synthesis of 7-AGNR on Au(111), starting with a DBBA molecular precursor, has been attained.
DOI: 10.24435/materialscloud:rs-1t
title: High Li-ion conductivity in tetragonal LGPO: a comparative first-principles study against known LISICON and LGPS phases

description: This work presents extensive first-principles (Car-Parrinello) molecular dynamics simulations of the solid-state electrolyte Li10GeP2O12 (LGPO) in a tetragonal phase -not synthesized so far- that is isostructural to the highly Li-ion conductive tetragonal phase of the sulfide analogue Li10GeP2S12 (LGPS). We provide comparative simulations of the experimentally known orthorhombic phase of LGPO (that we call here LISICON, from the family of superionic conductors to which LGPO belongs) and of the two experimentally known phases of LGPS, quasi-orthorhombic (called thio-LISICON) and tetragonal. We extract diffusion coefficients from fixed-cell simulations in the canonical ensemble and we study dynamical stability from variable-cell simulations in the isobaric-isothermal ensemble. The main outcome of this work is that, according to these simulations, although tetragonal LGPO is less stable than its orthorhombic allotrope, it exhibits a much higher conductivity, comparable to that presently estimated for both phases of LGPS. These results indicate that hypothetical tetragonal LGPO, if synthesized, could make a very attractive Li-ion conductor.
DOI: 10.24435/materialscloud:33-2e
title: High Li-ion conductivity in tetragonal LGPO: a comparative first-principles study against known LISICON and LGPS phases

description: This work presents extensive first-principles (Car-Parrinello) molecular dynamics simulations of the solid-state electrolyte Li10GeP2O12 (LGPO) in a tetragonal phase -not synthesized so far- that is isostructural to the highly Li-ion conductive tetragonal phase of the sulfide analogue Li10GeP2S12 (LGPS). We provide comparative simulations of the experimentally known orthorhombic phase of LGPO (that we call here LISICON, from the family of superionic conductors to which LGPO belongs) and of the two experimentally known phases of LGPS, quasi-orthorhombic (called thio-LISICON) and tetragonal. We extract diffusion coefficients from fixed-cell simulations in the canonical ensemble and we study dynamical stability from variable-cell simulations in the isobaric-isothermal ensemble. The main outcome of this work is that, according to these simulations, although tetragonal LGPO is less stable than its orthorhombic allotrope, it exhibits a much higher conductivity, comparable to that presently estimated for both phases of LGPS. These results indicate that hypothetical tetragonal LGPO, if synthesized, could make a very attractive Li-ion conductor.
DOI: 10.24435/materialscloud:p6-zh
title: Rules of formation of H–C–N–O compounds at high pressure and the fates of planetary ices

description: Results of an ab initio structure search on the H+C+N+O quaternary space at 500GPa.

The solar system's outer planets, and many of their moons, are dominated by matter from the H–C–N–O chemical space, based on solar system abundances of hydrogen and the planetary ices H2O, CH4 , and NH3 . In the planetary interiors, these ices will experience extreme pressure conditions, around 5 Mbar at the Neptune mantle–core boundary, and it is expected that they undergo phase transitions, decompose, and form entirely new compounds. While temperature will dictate the formation of compounds, ground- state density functional theory allows us to probe the chemical effects resulting from pressure alone. These structural developments in turn determine the planets' interior structures, thermal evolution, and magnetic field generation, among others. Despite its importance, the H–C–N–O system has not been surveyed systematically to explore which compounds emerge at high-pressure conditions, and what governs their stability. Here, we report on and analyze an unbiased crystal structure search among H–C–N–O compounds between 1 and 5 Mbar. We demonstrate that simple chemical rules drive stability in this composition space, which explains why the simplest possible quaternary mixture HCNO—isoelectronic to diamond—emerges as a stable compound and discuss dominant decomposition products of planetary ice mixtures.
DOI: 10.24435/materialscloud:p1-xn
title: Rules of formation of H–C–N–O compounds at high pressure and the fates of planetary ices

description: Results of an ab initio structure search on the H+C+N+O quaternary space at 500GPa.

The solar system's outer planets, and many of their moons, are dominated by matter from the H–C–N–O chemical space, based on solar system abundances of hydrogen and the planetary ices H2O, CH4 , and NH3 . In the planetary interiors, these ices will experience extreme pressure conditions, around 5 Mbar at the Neptune mantle–core boundary, and it is expected that they undergo phase transitions, decompose, and form entirely new compounds. While temperature will dictate the formation of compounds, ground- state density functional theory allows us to probe the chemical effects resulting from pressure alone. These structural developments in turn determine the planets' interior structures, thermal evolution, and magnetic field generation, among others. Despite its importance, the H–C–N–O system has not been surveyed systematically to explore which compounds emerge at high-pressure conditions, and what governs their stability. Here, we report on and analyze an unbiased crystal structure search among H–C–N–O compounds between 1 and 5 Mbar. We demonstrate that simple chemical rules drive stability in this composition space, which explains why the simplest possible quaternary mixture HCNO—isoelectronic to diamond—emerges as a stable compound and discuss dominant decomposition products of planetary ice mixtures.
DOI: 10.24435/materialscloud:fr-ts
title: Learning local equivariant representations for large-scale atomistic dynamics

description: A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences. In pursuit of this goal, neural message passing has lead to a paradigm shift by describing many-body correlations of atoms through iteratively passing messages along an atomistic graph. This propagation of information, however, makes parallel computation difficult and limits the length scales that can be studied. Strictly local descriptor-based methods, on the other hand, can scale to large systems but do not currently match the high accuracy observed with message passing approaches. This work introduces Allegro, a strictly local equivariant deep learning interatomic potential that simultaneously exhibits excellent accuracy and scalability of parallel computation. Allegro learns many-body functions of atomic coordinates using a series of tensor products of learned equivariant representations, but without relying on message passing. Allegro obtains improvements over state-of-the-art methods on the QM9 and revised MD-17 data sets. A single tensor product layer is shown to outperform existing deep message passing neural networks and transformers on the QM9 benchmark. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular dynamics simulations based on Allegro recover structural and kinetic properties of an amorphous phosphate electrolyte in excellent agreement with first principles calculations. Finally, we demonstrate the parallel scaling of Allegro with a dynamics simulation of 100 million atoms.
DOI: 10.24435/materialscloud:6w-p5
title: Learning local equivariant representations for large-scale atomistic dynamics

description: A simultaneously accurate and computationally efficient parametrization of the energy and atomic forces of molecules and materials is a long-standing goal in the natural sciences. In pursuit of this goal, neural message passing has lead to a paradigm shift by describing many-body correlations of atoms through iteratively passing messages along an atomistic graph. This propagation of information, however, makes parallel computation difficult and limits the length scales that can be studied. Strictly local descriptor-based methods, on the other hand, can scale to large systems but do not currently match the high accuracy observed with message passing approaches. This work introduces Allegro, a strictly local equivariant deep learning interatomic potential that simultaneously exhibits excellent accuracy and scalability of parallel computation. Allegro learns many-body functions of atomic coordinates using a series of tensor products of learned equivariant representations, but without relying on message passing. Allegro obtains improvements over state-of-the-art methods on the QM9 and revised MD-17 data sets. A single tensor product layer is shown to outperform existing deep message passing neural networks and transformers on the QM9 benchmark. Furthermore, Allegro displays remarkable generalization to out-of-distribution data. Molecular dynamics simulations based on Allegro recover structural and kinetic properties of an amorphous phosphate electrolyte in excellent agreement with first principles calculations. Finally, we demonstrate the parallel scaling of Allegro with a dynamics simulation of 100 million atoms.
DOI: 10.24435/materialscloud:bf-cz
title: Importance of intersite Hubbard interactions in β-MnO2: A first-principles DFT+U+V study

description: We present a first-principles investigation of the structural, electronic, and magnetic properties of pyrolusite (β-MnO2) using conventional and extended Hubbard-corrected density-functional theory (DFT+U and DFT+U+V). The onsite U and intersite V Hubbard parameters are computed using linear-response theory in the framework of density-functional perturbation theory. We show that while the inclusion of the onsite U is crucial to describe the localized nature of the Mn(3d) states, the intersite V is key to capture accurately the strong hybridization between neighboring Mn(3d) and O(2p) states. In this framework, we stabilize the simplified collinear antiferromagnetic (AFM) ordering (suggested by the Goodenough-Kanamori rule) that is commonly used as an approximation to the experimentally-observed noncollinear screw-type spiral magnetic ordering. A detailed investigation of the ferromagnetic and of other three collinear AFM spin configurations is also presented. The findings from Hubbard-corrected DFT are discussed using two kinds of Hubbard manifolds -- nonorthogonalized and orthogonalized atomic orbitals -- showing that special attention must be given to the choice of the Hubbard projectors, with orthogonalized manifolds providing more accurate results than nonorthogonalized ones within DFT+U+V. This work paves the way for future studies of complex transition-metal compounds containing strongly localized electrons in the presence of pronounced covalent interactions.
DOI: 10.24435/materialscloud:32-xv
title: Importance of intersite Hubbard interactions in β-MnO2: A first-principles DFT+U+V study

description: We present a first-principles investigation of the structural, electronic, and magnetic properties of pyrolusite (β-MnO2) using conventional and extended Hubbard-corrected density-functional theory (DFT+U and DFT+U+V). The onsite U and intersite V Hubbard parameters are computed using linear-response theory in the framework of density-functional perturbation theory. We show that while the inclusion of the onsite U is crucial to describe the localized nature of the Mn(3d) states, the intersite V is key to capture accurately the strong hybridization between neighboring Mn(3d) and O(2p) states. In this framework, we stabilize the simplified collinear antiferromagnetic (AFM) ordering (suggested by the Goodenough-Kanamori rule) that is commonly used as an approximation to the experimentally-observed noncollinear screw-type spiral magnetic ordering. A detailed investigation of the ferromagnetic and of other three collinear AFM spin configurations is also presented. The findings from Hubbard-corrected DFT are discussed using two kinds of Hubbard manifolds -- nonorthogonalized and orthogonalized atomic orbitals -- showing that special attention must be given to the choice of the Hubbard projectors, with orthogonalized manifolds providing more accurate results than nonorthogonalized ones within DFT+U+V. This work paves the way for future studies of complex transition-metal compounds containing strongly localized electrons in the presence of pronounced covalent interactions.
DOI: 10.24435/materialscloud:2b-x9
title: Crystal-graph attention networks for the prediction of stable materials

description: Graph neural networks have enjoyed great success in the prediction of material properties for both molecules and crystals. These networks typically use the atomic positions (usually expanded in a Gaussian basis) and the atomic species as input. Unfortunately, this information is in general not available when predicting new materials, for which the precise geometrical information is unknown. In this work, we circumvent this problem by predicting the thermodynamic stability of crystal structures without using the knowledge of the precise bond distances. We replace this information with embeddings of graph distances, allowing our networks to be used directly in high-throughput studies based on both composition and crystal structure prototype. Using these embeddings, we combine the newest developments in graph neural networks and apply them to the prediction of the distances to the convex hull. To train these networks, we curate a dataset of over 2 million density-functional calculations of crystals with consistent calculation parameters from various sources. The new dataset allows for the creation of a high quality convex hull and a large scale transfer learning approach. We apply the resulting model to the high-throughput search of 15 million tetragonal perovskites of composition ABCD2. As a result, we identify several thousand potentially stable compounds and demonstrate that transfer learning from the newly curated dataset reduces the required training data by 50%.
DOI: 10.24435/materialscloud:sb-cy
title: Symmetry-based computational search for novel binary and ternary 2D materials

description: We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a combinatorial engine' that constructs potential compounds by occupying all possible Wyckoff positions for a certain space group with  combinations of chemical elements. These combinations are restricted by imposing charge neutrality and the Pauling test for electronegativities. The structures are then pre-optimized with a specially crafted universal neural-network force-field, before a final step of geometry optimization using density-functional theory is performed. In this way we unveil an unprecedented variety of two-dimensional materials, covering the whole periodic table in more than 30 different stoichiometries of form AnBm or AnBmCk. Among the found structures we find examples that can be built by decorating nearly all Platonic and Archimedean tesselations as well as their dual Laves or Catalan tilings. We also obtain a rich, and unexpected, polymorphism for some specific compounds. We further accelerate the exploration of the chemical space of two-dimensional materials by employing machine-learning-accelerated prototype search, based on the structural types discovered in the exhaustive search. In total, we obtain around 6500 compounds, not present in previous available databases of 2D materials, with an energy of less than 250 meV/atom above the convex hull of thermodynamic stability.
DOI: 10.24435/materialscloud:d0-fb
title: Interplay between polarization, strain and defect-pairs in Fe-doped  SrMnO3-δ

description: Defect chemistry, strain, and structural, magnetic and electronic degrees of freedom constitute a rich space for the design of functional properties in transition metal oxides. Here, we show that it is possible to engineer polarity and ferroelectricity in non-polar perovskite oxides via polar defect pairs formed by anion vacancies coupled to substitutional cations. We use a self-consistent site-dependent DFT+U approach that accounts for local structural and chemical changes upon defect creation and which is crucial to reconcile predictions with the available experimental data. Our results for Fe-doped oxygen-deficient SrMnO3 show that substitutional Fe and oxygen vacancies can promote polarity due to an o -center displacement of the defect charge resulting in a net electric dipole moment, which polarizes the lattice in the defect neighborhood. The formation of these defects and the resulting polarization can be tuned by epitaxial strain, resulting in enhanced polarization also for strain values lower than the ones necessary to induce a polar phase transition in undoped SrMnO3. For high enough defect concentrations, these defect dipoles couple in a parallel fashion, thus enabling defect- and strain-based engineering of ferroelectricity in SrMnO3.