cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:kj-24
title: Scaling relations and dynamical predictiveness of electric dipole strength on 2e- ORR catalytic property

description: Efficient O₂ reduction to H₂O₂, vital for energy conversion and environmental cleanup, relies on precise control of heterogeneous catalysts interacting with reaction species. Through high-throughput density functional theory calculations, consisting of 369 single atom catalysts, we identified the polarized descriptor (electric dipole strength) on two-dimensional carbon materials, revealing insights into the catalytic effect of support polarization. Surprisingly, this descriptor exhibits advanced scaling relationships towards H₂O₂ synthesis, incorporating factors such as active metals, coordination environments, and surface curvatures, highlighting its widespread significance. Furthermore, it demonstrates reliable predictability for O₂ adsorption in dynamic water environments, with optimal reactivity observed within the range of -1.40 to -1.00 e·Å, as confirmed by dynamic and static simulations of the 2e- pathway of O₂ reduction. In essence, these findings offer valuable insights for the rational design of electrocatalysts tailored for selective O₂ reduction.
DOI: 10.24435/materialscloud:nw-3j
title: ReDD-COFFEE: A ready-to-use database of covalent organic framework structures and accurate force fields to enable high-throughput screenings

description: Covalent organic frameworks (COFs) are a versatile class of nanoporous materials that can be used for a broad range of applications. They possess strong covalent bonds and low densities. Owing to their building block nature, the number of hypothetical COFs envisioned by reticular synthesis is enormous. Since experimental screening is not possible, computational high-throughput screenings offer a valuable alternative to characterize the material space and speed-up materials discovery. These screening studies typically require a diverse materials database and accurate interatomic potentials to accurately predict the macroscopic behavior of each hypothetical COF. Here, we present ReDD-COFFEE, the Ready-to-use and Diverse Database of Covalent Organic Frameworks with Force field based Energy Evaluation. Our database contains 268 687 COFs and accompanying ab initio derived, system-specific force fields for each of them. We hope this database may inspire other researchers to further explore the potential of these intriguing functional materials.
DOI: 10.24435/materialscloud:f9-m5
title: ReDD-COFFEE: A ready-to-use database of covalent organic framework structures and accurate force fields to enable high-throughput screenings

description: Covalent organic frameworks (COFs) are a versatile class of nanoporous materials that can be used for a broad range of applications. They possess strong covalent bonds and low densities. Owing to their building block nature, the number of hypothetical COFs envisioned by reticular synthesis is enormous. Since experimental screening is not possible, computational high-throughput screenings offer a valuable alternative to characterize the material space and speed-up materials discovery. These screening studies typically require a diverse materials database and accurate interatomic potentials to accurately predict the macroscopic behavior of each hypothetical COF. Here, we present ReDD-COFFEE, the Ready-to-use and Diverse Database of Covalent Organic Frameworks with Force field based Energy Evaluation. Our database contains 268 687 COFs and accompanying ab initio derived, system-specific force fields for each of them. We hope this database may inspire other researchers to further explore the potential of these intriguing functional materials.
DOI: 10.24435/materialscloud:2d-4b
title: Thermal conductivity predictions with foundation atomistic models

description: Advances in machine learning have led to the development of foundation models for atomistic materials chemistry, enabling quantum-accurate descriptions of interatomic forces across diverse compounds at reduced computational cost. Hitherto, these models have been benchmarked relying on descriptors based on atoms' interaction energies or harmonic vibrations; their accuracy and efficiency in predicting observable and technologically relevant heat-conduction properties remains unknown. Here, we introduce a framework that leverages foundation models and the Wigner formulation of heat transport to overcome the major bottlenecks of current methods for designing heat-management materials: high cost, limited transferability, or lack of physics awareness. We present the standards needed to achieve first-principles accuracy in conductivity predictions through model's fine-tuning, discussing benchmark metrics and precision/cost trade-offs. We apply our framework to a database of solids with diverse compositions and structures, demonstrating its potential to discover materials for next-gen technologies ranging from thermal insulation to neuromorphic computing.
DOI: 10.24435/materialscloud:ph-jb
title: FINALES (06/2022) – Electrolyte Optimization for Minimum Density and Maximum Viscosity

description: This study presents the initial implementation of the Fast INtention-Agnostic LEarning Server (FINALES) in a demonstration of a distributed Materials Acceleration Platform (MAP) including experimental and computational methods and a machine learning (ML)-based optimizer. In this demonstration, the optimizer was configured to minimize the density of the electrolyte solutions while maximizing the viscosity by exploiting experimental and computational results. The tenants (the units connected to FINALES in the MAP) are shortly described in the following:

- Autonomous Synthesis and Analysis of Battery electrolytes (ASAB) setup: an experimental tenant providing density and viscosity data using a densimeter of the type DMA 4100M and a viscometer of type Lovis 2000 both by Anton Paar Germany
- Molecular dynamics tenant: a computational tenant capable of providing radial distribution functions, diffusion coefficients, ionic conductivity, transference numbers, heat capacity and density data to the MAP
- Optimizer: the tenant guiding the optimization by processing the available data and generating requests for electrolyte formulations to be tested subsequently

This dataset accompanies the publication:
Vogler, M., Busk, J., Hajiyani, H., Jørgensen, P. B., Safaei, N., Castelli, I. E., Ramirez, F. F., Carlsson, J., Pizzi, G., Clark, S., Hanke, F., Bhowmik, A. & Stein, H. S. Brokering between tenants for an international materials acceleration platform. Matter 6, 2647–2665 (2023). DOI: https://doi.org/10.1016/j.matt.2023.07.016
DOI: 10.24435/materialscloud:p1-se
title: FINALES (06/2022) – Electrolyte Optimization for Minimum Density and Maximum Viscosity

description: This study presents the initial implementation of the Fast INtention-Agnostic LEarning Server (FINALES) in a demonstration of a distributed Materials Acceleration Platform (MAP) including experimental and computational methods and a machine learning (ML)-based optimizer. In this demonstration, the optimizer was configured to minimize the density of the electrolyte solutions while maximizing the viscosity by exploiting experimental and computational results. The tenants (the units connected to FINALES in the MAP) are shortly described in the following:

- Autonomous Synthesis and Analysis of Battery electrolytes (ASAB) setup: an experimental tenant providing density and viscosity data using a densimeter of the type DMA 4100M and a viscometer of type Lovis 2000 both by Anton Paar Germany
- Molecular dynamics tenant: a computational tenant capable of providing radial distribution functions, diffusion coefficients, ionic conductivity, transference numbers, heat capacity and density data to the MAP
- Optimizer: the tenant guiding the optimization by processing the available data and generating requests for electrolyte formulations to be tested subsequently

This dataset accompanies the publication:
Vogler, M., Busk, J., Hajiyani, H., Jørgensen, P. B., Safaei, N., Castelli, I. E., Ramirez, F. F., Carlsson, J., Pizzi, G., Clark, S., Hanke, F., Bhowmik, A. & Stein, H. S. Brokering between tenants for an international materials acceleration platform. Matter 6, 2647–2665 (2023). DOI: https://doi.org/10.1016/j.matt.2023.07.016
DOI: 10.24435/materialscloud:w3-3c
title: Engineering epitaxial interfaces for topological insulator – superconductor hybrid devices with Al electrodes

description: Proximity-induced superconductivity in hybrid devices of topological insulators and superconductors offers a promising platform for the pursuit of elusive topological superconductivity and its anticipated applications, such as fault-tolerant quantum computing. To study and harness such hybrid devices, a key challenge is the realization of highly functional material interfaces with a suitable superconductor featuring 2e-periodic parity-conserving transport to ensure a superconducting hard-gap free of unpaired electrons, which is important for Majorana physics. A superconductor well-known for this characteristic is Al, however, its direct integration into devices based on tetradymite topological insulators has so far been found to yield non-transparent interfaces. By focusing on Bi₂Te₃-Al heterostructures, this study identifies detrimental interdiffusion processes at the interface through atomically resolved structural and chemical analysis, and showcase their mitigation by leveraging different interlayers – namely Nb, Ti, Pd, and Pt – between Bi₂Te₃ and Al. Through structural transformation of the interlayer materials (X) into their respective tellurides (XTe₂) atomically-sharp epitaxial interfaces are engineered and further characterized in low-temperature transport experiments on Al-X-Bi₂Te₃-X-Al Josephson junctions and in complementary density functional theory calculations. By demonstrating functional interfaces between Bi₂Te₃ and Al, this work provides key insights and paves the way for the next generation of sophisticated topological devices.
DOI: 10.24435/materialscloud:m1-7c
title: Engineering epitaxial interfaces for topological insulator – superconductor hybrid devices with Al electrodes

description: Proximity-induced superconductivity in hybrid devices of topological insulators and superconductors offers a promising platform for the pursuit of elusive topological superconductivity and its anticipated applications, such as fault-tolerant quantum computing. To study and harness such hybrid devices, a key challenge is the realization of highly functional material interfaces with a suitable superconductor featuring 2e-periodic parity-conserving transport to ensure a superconducting hard-gap free of unpaired electrons, which is important for Majorana physics. A superconductor well-known for this characteristic is Al, however, its direct integration into devices based on tetradymite topological insulators has so far been found to yield non-transparent interfaces. By focusing on Bi₂Te₃-Al heterostructures, this study identifies detrimental interdiffusion processes at the interface through atomically resolved structural and chemical analysis, and showcase their mitigation by leveraging different interlayers – namely Nb, Ti, Pd, and Pt – between Bi₂Te₃ and Al. Through structural transformation of the interlayer materials (X) into their respective tellurides (XTe₂) atomically-sharp epitaxial interfaces are engineered and further characterized in low-temperature transport experiments on Al-X-Bi₂Te₃-X-Al Josephson junctions and in complementary density functional theory calculations. By demonstrating functional interfaces between Bi₂Te₃ and Al, this work provides key insights and paves the way for the next generation of sophisticated topological devices.
DOI: 10.24435/materialscloud:rb-24
title: First-principles study of the gap in the spin excitation spectrum of the CrI₃ honeycomb ferromagnet

description: The nature of the gap observed at the zone border in the spin excitation spectrum of CrI₃ quasi-two-dimensional single crystals is still controversial. We perform first-principles calculations based on time-dependent density functional perturbation theory, which indicate that the observed gap results from a combination of spin-orbit and interlayer interaction effects. The former give rise to the anisotropic spin-spin interactions that are responsible for its very existence, while the latter determine both its displacement from the K point of the Brillouin zone, due to the in-plane lattice distortions induced by them, and an enhancement of its magnitude, in agreement with experiments and previous theoretical work based on a lattice model.
DOI: 10.24435/materialscloud:xa-mx
title: First-principles study of the gap in the spin excitation spectrum of the CrI₃ honeycomb ferromagnet

description: The nature of the gap observed at the zone border in the spin excitation spectrum of CrI₃ quasi-two-dimensional single crystals is still controversial. We perform first-principles calculations based on time-dependent density functional perturbation theory, which indicate that the observed gap results from a combination of spin-orbit and interlayer interaction effects. The former give rise to the anisotropic spin-spin interactions that are responsible for its very existence, while the latter determine both its displacement from the K point of the Brillouin zone, due to the in-plane lattice distortions induced by them, and an enhancement of its magnitude, in agreement with experiments and previous theoretical work based on a lattice model.
DOI: 10.24435/materialscloud:j2-58
title: Electronic excited states from physically-constrained machine learning

description: Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be combined explicitly with physically-grounded operations. We present an example of an integrated modeling approach, in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those that it is trained on, and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parameterization corresponding to a minimal atom-centered basis. Our results on a comprehensive dataset of hydrocarbons emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency, and providing a blueprint for developing ML-augmented electronic-structure methods.
Here we include the dataset, accompanying the paper linked below, of hydrocarbons including ethane, ethene, butadiene, hexane, hexatriene, isoprene, styrene, polyalkenes (dodecahexaene, tetradecaheptaene, hexadecaoctaene, octadecanonaene, eicosadecaene), aromatics (benzene, azulene, naphthalene, biphenyl), anthracene, beta-carotene, fullerene. We also provide scripts to generate the Fock and overlap matrices in this dataset. The code for machine learning can be found at the Software reference below.
DOI: 10.24435/materialscloud:5s-gm
title: Electronic excited states from physically-constrained machine learning

description: Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be combined explicitly with physically-grounded operations. We present an example of an integrated modeling approach, in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those that it is trained on, and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parameterization corresponding to a minimal atom-centered basis. Our results on a comprehensive dataset of hydrocarbons emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency, and providing a blueprint for developing ML-augmented electronic-structure methods.
Here we include the dataset, accompanying the paper linked below, of hydrocarbons including ethane, ethene, butadiene, hexane, hexatriene, isoprene, styrene, polyalkenes (dodecahexaene, tetradecaheptaene, hexadecaoctaene, octadecanonaene, eicosadecaene), aromatics (benzene, azulene, naphthalene, biphenyl), anthracene, beta-carotene, fullerene. We also provide scripts to generate the Fock and overlap matrices in this dataset. The code for machine learning can be found at the Software reference below.
DOI: 10.24435/materialscloud:fj-j4
title: Electronic excited states from physically-constrained machine learning

description: Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or be combined explicitly with physically-grounded operations. We present an example of an integrated modeling approach, in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those that it is trained on, and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parameterization corresponding to a minimal atom-centered basis. Our results on a comprehensive dataset of hydrocarbons emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency, and providing a blueprint for developing ML-augmented electronic-structure methods.
Here we include the dataset, accompanying the paper linked below, of hydrocarbons including ethane, ethene, butadiene, hexane, hexatriene, isoprene, styrene, polyalkenes (dodecahexaene, tetradecaheptaene, hexadecaoctaene, octadecanonaene, eicosadecaene), aromatics (benzene, azulene, naphthalene, biphenyl), anthracene, beta-carotene, fullerene. We also provide scripts to generate the Fock and overlap matrices in this dataset. The code for machine learning can be found at the Software reference below.
DOI: 10.24435/materialscloud:y4-7s
title: Radicals in aqueous solution: Assessment of density-corrected SCAN functional

description: We study self-interaction effects in solvated and strongly-correlated cationic molecular clusters, with a focus on the solvated hydroxyl radical. To address the self-interaction issue, we apply the DC-r²SCAN method, with the auxiliary density matrix approach. Validating our method through simulations of bulk liquid water, we demonstrate that DC-r²SCAN maintains the structural accuracy of r²SCAN while effectively addressing spin density localization issues. Extending our analysis to solvated cationic molecular clusters, we find that the hemibonded motif in the [CH₃S∴CH₃SH]⁺ cluster is disrupted in the DC-r²SCAN simulation, in contrast to r²SCAN that preserves the (three-electron-two-center)-bonded motif. Similarly, for the [SH∴SH₂]⁺ cluster, r²SCAN restores the hemibonded motif through spin leakage, while DC-r²SCAN predicts a weaker hemibond formation influenced by solvent-solute interactions. Our findings demonstrate the potential of DC-r²SCAN combined with the auxiliary density matrix method to improve electronic structure calculations, providing insights into the properties of solvated cationic molecular clusters. This work contributes to the advancement of self-interaction corrected electronic structure theory and offers a computational framework for modeling condensed phase systems with intricate correlation effects.
DOI: 10.24435/materialscloud:pg-9p
title: Radicals in aqueous solution: Assessment of density-corrected SCAN functional

description: We study self-interaction effects in solvated and strongly-correlated cationic molecular clusters, with a focus on the solvated hydroxyl radical. To address the self-interaction issue, we apply the DC-r²SCAN method, with the auxiliary density matrix approach. Validating our method through simulations of bulk liquid water, we demonstrate that DC-r²SCAN maintains the structural accuracy of r²SCAN while effectively addressing spin density localization issues. Extending our analysis to solvated cationic molecular clusters, we find that the hemibonded motif in the [CH₃S∴CH₃SH]⁺ cluster is disrupted in the DC-r²SCAN simulation, in contrast to r²SCAN that preserves the (three-electron-two-center)-bonded motif. Similarly, for the [SH∴SH₂]⁺ cluster, r²SCAN restores the hemibonded motif through spin leakage, while DC-r²SCAN predicts a weaker hemibond formation influenced by solvent-solute interactions. Our findings demonstrate the potential of DC-r²SCAN combined with the auxiliary density matrix method to improve electronic structure calculations, providing insights into the properties of solvated cationic molecular clusters. This work contributes to the advancement of self-interaction corrected electronic structure theory and offers a computational framework for modeling condensed phase systems with intricate correlation effects.
DOI: 10.24435/materialscloud:1e-c7
title: Solute strengthening of prism edge dislocations in Mg alloys

description: The poor ductility of hcp Mg is attributed to the low activity of non-basal slip systems and so activation of prismatic slip can aid ductility in rolled sheets by providing three additional  Burgers vector slip systems.  Experimental studies show that dilute additions of alloying elements such as Zn and Al leads to softening of prismatic slip at low temperatures but strengthening at higher temperatures.  Here, the role of solute strengthening of prismatic edge dislocations is investigated as a possible explanation for the higher-T strengthening.  Mg-Zn is studied using first-principles inputs in a parameter-free solute strengthening theory.  First-principles DFT is necessary to accurately assess the strong solute chemical interaction energies in the core of the compact edge dislocation.   Such calculations are subtle due to motion of the dislocation in the presence of the solute, and methods to obtain reliable results with acceptable computational cost are discussed.   While interaction energies of Zn in the prism edge core can be quite large (+/- 0.34 eV), the edge solute strengthening of prismatic slip in dilute Mg-Zn remains well below experiments at high temperatures.  However, the large difference (0.68 eV) in Zn/dislocation interaction energies across the core of the edge dislocation suggests strengthening by dynamic strain aging as an explanation for the higher-T strengthening.