cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:2018.0011/v4
title: Capturing chemical intuition in synthesis of metal-organic frameworks

description: We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.
DOI: 10.24435/materialscloud:2018.0015/v1
title: Toward GW Calculations on Thousands of Atoms

description: We provide the input files needed to reproduce the results of the article

Toward GW Calculations on Thousands of Atoms
J. Wilhelm, D. Golze, L. Talirz, J. Hutter, C. A. Pignedoli
J. Phys. Chem. Lett.  9, 306–312 (2018) DOI:10.1021/acs.jpclett.7b02740

The GW approximation of many-body perturbation theory is an accurate method
for computing electron addition and removal energies of molecules and solids.
In a canonical implementation, however, its computational cost is  in the
system size N, which prohibits its application to many systems of interest.
We present a full-frequency GW algorithm in a Gaussian-type basis,
whose computational cost scales with N2 to N3.
The implementation is optimized for massively parallel execution on
state-of-the-art supercomputers and is suitable for nanostructures and molecules in the gas,
liquid or condensed phase, using either pseudopotentials or all electrons.
We validate the accuracy of the algorithm on the GW100 molecular test set,
finding mean absolute deviations of 35 meV for ionization potentials and 27 meV
for electron affinities. Furthermore, we study the length-dependence of quasiparticle
energies in armchair graphene nanoribbons of up to 1734 atoms in size, and compute the
local density of states across a nanoscale heterojunction.
DOI: 10.24435/materialscloud:9a-jz
title: Toward GW Calculations on Thousands of Atoms

description: We provide the input files needed to reproduce the results of the article

Toward GW Calculations on Thousands of Atoms
J. Wilhelm, D. Golze, L. Talirz, J. Hutter, C. A. Pignedoli
J. Phys. Chem. Lett.  9, 306–312 (2018) DOI:10.1021/acs.jpclett.7b02740

The GW approximation of many-body perturbation theory is an accurate method
for computing electron addition and removal energies of molecules and solids.
In a canonical implementation, however, its computational cost is  in the
system size N, which prohibits its application to many systems of interest.
We present a full-frequency GW algorithm in a Gaussian-type basis,
whose computational cost scales with N2 to N3.
The implementation is optimized for massively parallel execution on
state-of-the-art supercomputers and is suitable for nanostructures and molecules in the gas,
liquid or condensed phase, using either pseudopotentials or all electrons.
We validate the accuracy of the algorithm on the GW100 molecular test set,
finding mean absolute deviations of 35 meV for ionization potentials and 27 meV
for electron affinities. Furthermore, we study the length-dependence of quasiparticle
energies in armchair graphene nanoribbons of up to 1734 atoms in size, and compute the
local density of states across a nanoscale heterojunction.
DOI: 10.24435/materialscloud:2018.0008/v1
title: In Silico Design of Porous Polymer Networks: High Throughput Screening for Methane Storage Materials

description: Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal–organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure–property relationships that govern methane storage performance were identified. The relationships are translated into experimental guidelines to realize the ideal PPN structure. We found that cooperative methane–methane attractions were present in all of the best-performing materials, highlighting the importance of guest interaction in the design of optimal materials for methane storage.
DOI: 10.24435/materialscloud:pj-6m
title: In Silico Design of Porous Polymer Networks: High Throughput Screening for Methane Storage Materials

description: Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal–organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure–property relationships that govern methane storage performance were identified. The relationships are translated into experimental guidelines to realize the ideal PPN structure. We found that cooperative methane–methane attractions were present in all of the best-performing materials, highlighting the importance of guest interaction in the design of optimal materials for methane storage.
DOI: 10.24435/materialscloud:2d-bz
title: A deep learning dataset for metal multiaxial fatigue life prediction

description: In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed experimental data from 40 different metallic materials, comprising a total of 1195 data points under 48 distinct loading paths. Each data point is stored in a CSV file, capturing the loading path as a time-series with axial and tangential stress or strain values.The primary purpose of this dataset is to support the development and validation of deep learning models aimed at accurately predicting the fatigue life of metals under various loading conditions. This dataset includes stress-controlled and strain-controlled data, ensuring a broad representation of experimental scenarios. Additionally, an Excel file accompanies the dataset, providing detailed mechanical properties of each material, such as elastic modulus, tensile strength, yield strength, and Poisson's ratio, along with references to the original experimental sources.This dataset is intended for researchers in materials science and mechanical engineering, offering a robust foundation for training and testing deep learning algorithms in fatigue analysis. By making this dataset publicly available, we aim to foster collaboration and further advancements in the field of metal fatigue prediction. Researchers are encouraged to utilize and contribute to the dataset, thereby enhancing its scope and applicability.
DOI: 10.24435/materialscloud:ar-nw
title: A deep learning dataset for metal multiaxial fatigue life prediction

description: In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed experimental data from 40 different metallic materials, comprising a total of 1195 data points under 48 distinct loading paths. Each data point is stored in a CSV file, capturing the loading path as a time-series with axial and tangential stress or strain values.The primary purpose of this dataset is to support the development and validation of deep learning models aimed at accurately predicting the fatigue life of metals under various loading conditions. This dataset includes stress-controlled and strain-controlled data, ensuring a broad representation of experimental scenarios. Additionally, an Excel file accompanies the dataset, providing detailed mechanical properties of each material, such as elastic modulus, tensile strength, yield strength, and Poisson's ratio, along with references to the original experimental sources.This dataset is intended for researchers in materials science and mechanical engineering, offering a robust foundation for training and testing deep learning algorithms in fatigue analysis. By making this dataset publicly available, we aim to foster collaboration and further advancements in the field of metal fatigue prediction. Researchers are encouraged to utilize and contribute to the dataset, thereby enhancing its scope and applicability.
DOI: 10.24435/materialscloud:3a-9v
title: Li₄₋ₓGe₁₋ₓPₓO₄, a potential solid-state electrolyte for all-oxide microbatteries

description: Solid-state electrolytes for Li-ion batteries are attracting growing interest as they allow building safer batteries, also using lithium-metal anodes. Here, we studied a compound in the lithium superionic conductor (LISICON) family, i.e. Li₄₋ₓGe₁₋ₓPₓO₄ (LGPO). Thin films were deposited via pulsed laser deposition, and their electrical properties were compared to those of ceramic pellets. A detailed characterization of their microstructures shows that thin films can be deposited fully crystalline at higher temperatures but also partially amorphous at room temperature. The conductivity is not strongly influenced by the presence of grain boundaries, exposure to air, or lithium deficiencies. First-principles molecular dynamics simulations were employed to calculate the lithium-ion diffusion profile and the conductivity at various temperatures of the ideal LGPO crystal. Simulations give the upper limit of conductivity for a defect-free crystal, which is in the range of 10–2 S cm–1 at 300 °C. The ease of thin-film fabrication and room-temperature Li-ion conductivity in the range of a few μS cm–1 make LGPO a very appealing electrolyte material for thin-film all-solid-state all-oxide microbatteries.
DOI: 10.24435/materialscloud:4k-2n
title: Li₄₋ₓGe₁₋ₓPₓO₄, a potential solid-state electrolyte for all-oxide microbatteries

description: Solid-state electrolytes for Li-ion batteries are attracting growing interest as they allow building safer batteries, also using lithium-metal anodes. Here, we studied a compound in the lithium superionic conductor (LISICON) family, i.e. Li₄₋ₓGe₁₋ₓPₓO₄ (LGPO). Thin films were deposited via pulsed laser deposition, and their electrical properties were compared to those of ceramic pellets. A detailed characterization of their microstructures shows that thin films can be deposited fully crystalline at higher temperatures but also partially amorphous at room temperature. The conductivity is not strongly influenced by the presence of grain boundaries, exposure to air, or lithium deficiencies. First-principles molecular dynamics simulations were employed to calculate the lithium-ion diffusion profile and the conductivity at various temperatures of the ideal LGPO crystal. Simulations give the upper limit of conductivity for a defect-free crystal, which is in the range of 10–2 S cm–1 at 300 °C. The ease of thin-film fabrication and room-temperature Li-ion conductivity in the range of a few μS cm–1 make LGPO a very appealing electrolyte material for thin-film all-solid-state all-oxide microbatteries.
DOI: 10.24435/materialscloud:2019.0033/v1
title: Picture of Wet Electron: A Localized Transient State in Liquid Water

description: A transient state of the excess electron in liquid water preceding the development of the solvation shell, the so-called wet electron, has been invoked to explain spectroscopic observations, but its binding energy and atomic structure have remained highly elusive. Here, we carry out hybrid functional molecular dynamics to unveil the ultrafast solvation mechanism leading to the hydrated electron. In the pre-hydrated regime, the electron is found to repeatedly switch between a quasi-free electron state in the conduction band and a localized state with a binding energy of 0.26 eV, which we assign to the wet electron. This transient state self-traps in a region of the liquid which extends up to ∼4.5 Å and involves a severe disruption of the hydrogen-bond network. Our picture provides an unprecedented view on the nature of the wet electron, which is instrumental to understand the properties of this fundamental species in liquid water.
DOI: 10.34740/kaggle/dsv/12268085
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.24435/materialscloud:ds-y3
title: Picture of Wet Electron: A Localized Transient State in Liquid Water

description: A transient state of the excess electron in liquid water preceding the development of the solvation shell, the so-called wet electron, has been invoked to explain spectroscopic observations, but its binding energy and atomic structure have remained highly elusive. Here, we carry out hybrid functional molecular dynamics to unveil the ultrafast solvation mechanism leading to the hydrated electron. In the pre-hydrated regime, the electron is found to repeatedly switch between a quasi-free electron state in the conduction band and a localized state with a binding energy of 0.26 eV, which we assign to the wet electron. This transient state self-traps in a region of the liquid which extends up to ∼4.5 Å and involves a severe disruption of the hydrogen-bond network. Our picture provides an unprecedented view on the nature of the wet electron, which is instrumental to understand the properties of this fundamental species in liquid water.
DOI: 10.24435/materialscloud:2019.0021/v1
title: Reducing the Number of Mean-Square Deviation Calculations with Floating Close Structure in Metadynamics

description: Biomolecular simulations are computationally expensive. This limits their application in protein folding simulations, protein engineering, drug design and related fields. Enhanced sampling techniques such as metadynamics accelerates slow events in molecular simulation. This and other method apply artificial forces in directions of collective degrees of freedom (aka collective variables). Path collective variables and Property Map collective variables are defined using a series of reference structures of the studied molecular system. They require a huge number of mean square deviation calculations along the simulation. Close Structure algorithm reduces the number of these operations.
DOI: 10.24435/materialscloud:2018.0003/v1
title: In Silico Design of 2D and 3D Covalent Organic Frameworks for Methane Storage Applications

description: Here we present 69,840 covalent organic frameworks (COFs) assembled in silico from a set of 666 distinct organic linkers into 2D-layered and 3D configurations. We investigate the feasibility of using these frameworks for methane storage by using grand-canonical Monte Carlo (GCMC) simulations to calculate their deliverable capacities (DCs). From these calculations, we predict that the best structure in the database is linker91_C_linker91_C_tbd, a structure composed of carbon-carbon bonded triazine linkers in the tbd topology. This structure has a predicted 65-bar DC of 216 v STP/v, greater than that of the best current methane storage material. We also predict other top performing materials, with 305 structures having DCs of over 190 v STP/v, and 34 of these having DCs of over 200 v STP/v. This archive entry contains the database of assembled COF structures (in CIF file format) together with all of their properties. Among the calculated properties for each structure are the framework density, the methane heats of desorption at the storage and depletion pressures, the methane uptakes at the storage and deplation pressures, the supercell volume, and the geometric surface area. Structures are also labeled according to their bond types (amide, amine, imine, carbon-carbon, or mixed) and their dimensionalities (2D or 3D).
DOI: 10.24435/materialscloud:2019.0036/v1
title: The complex non-collinear magnetic orderings in Ba2YOsO6: A new approach to tuning spin-lattice interactions and controlling magnetic orderings in frustrated complex oxides

description: Project abstract: Frustrated magnets are one class of fascinating materials that host many intriguing phases such as spin ice, spin liquid and complex long-range magnetic orderings at low temperatures. In this work we use first-principles calculations to find that in a wide range of magnetically frustrated oxides, at zero temperature a number of non-collinear magnetic orderings are more stable than the type-I collinear ordering that is observed at finite temperatures. The emergence of non-collinear orderings in those complex oxides is due to higher-order exchange interactions that originate from second-row and third-row transition metal elements. This implies a collinear-to-noncollinear spin transition at sufficiently low temperatures in those frustrated complex oxides. Furthermore, we find that in a particular oxide Ba2YOsO6, experimentally feasible uniaxial strain can tune the material between two different non-collinear magnetic orderings. Our work predicts new non- collinear magnetic orderings in frustrated complex oxides at very low temperatures and provides a mechanical route to tuning complex non-collinear magnetic orderings in those materials.

About this entry:  We provide the input files of our DFT calculations for the studied complex oxides.  The structures in POSCAR format and the INCAR files for all stabilized magnetic orderings in our study are all included. These files can be directly used into DFT calculations with VASP.
DOI: 10.24435/materialscloud:v9-6s
title: The complex non-collinear magnetic orderings in Ba2YOsO6: A new approach to tuning spin-lattice interactions and controlling magnetic orderings in frustrated complex oxides

description: Project abstract: Frustrated magnets are one class of fascinating materials that host many intriguing phases such as spin ice, spin liquid and complex long-range magnetic orderings at low temperatures. In this work we use first-principles calculations to find that in a wide range of magnetically frustrated oxides, at zero temperature a number of non-collinear magnetic orderings are more stable than the type-I collinear ordering that is observed at finite temperatures. The emergence of non-collinear orderings in those complex oxides is due to higher-order exchange interactions that originate from second-row and third-row transition metal elements. This implies a collinear-to-noncollinear spin transition at sufficiently low temperatures in those frustrated complex oxides. Furthermore, we find that in a particular oxide Ba2YOsO6, experimentally feasible uniaxial strain can tune the material between two different non-collinear magnetic orderings. Our work predicts new non- collinear magnetic orderings in frustrated complex oxides at very low temperatures and provides a mechanical route to tuning complex non-collinear magnetic orderings in those materials.

About this entry:  We provide the input files of our DFT calculations for the studied complex oxides.  The structures in POSCAR format and the INCAR files for all stabilized magnetic orderings in our study are all included. These files can be directly used into DFT calculations with VASP.
DOI: 10.24435/materialscloud:2018.0011/v1
title: Synthesis of Metal-Organic Frameworks: capturing chemical intuition

description: We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.
DOI: 10.24435/materialscloud:2017.0003/v1
title: The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials (Data Download)

description: Project Abstract: For applications of metal-organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation we develop a simple analytical model that predicts a material's Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework's intrinsic flexibility whereby performance is either improved or reduced with increasing flexibility, depending on the material's pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that the globally optimal separation exists within an entirely rigid pore. Molecular simulations show that our simple model predicts performance trends that are observed when screening the adsorption behavior of flexible MOFs. These flexible simulations provide better agreement with experimental adsorption data in a high performance material that is not captured when modeling this framework as rigid, an approximation typically made in high-throughput screening studies. We conclude that, for shape selective adsorption applications, the globally optimal material will have the optimal pore size/chemistry and minimal intrinsic flexibility even though other non-optimal materials' selectivity can actually be improved by flexibility. Equally important, we find that flexible simulations can be critical for correctly modeling adsorption in these types of systems.

About this entry: You can find the Xe/Kr Henry coefficients and the infinite dilution selectivity of more than 2000 CoRE MOF structures obtained via computational screening when materials are simulated as both flexible and rigid.  For details about the methods used to obtain these results, please see the corresponding paper (DOI: 10.1021/jacs.7b01688).  The data provided here was used to create the scatter plots presented in the original paper and can be used to find the adsorption properties for any material in the CoRE MOF screening.