cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:61-x8
title: Learning a reactive potential for silica-water through uncertainty attribution

description: The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential can accurately capture silicate-water reactivity. The model was trained on a new dataset comprising 400,000 energies and forces of molecular clusters at the 𝜔-B97XD\def2-TVZP level. To ensure the robustness of the model, we introduce a new and general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. Our trained potential is found to reproduce static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.
DOI: 10.24435/materialscloud:9r-97
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.
DOI: 10.24435/materialscloud:64-wr
title: Auxetic polymer networks: The role of crosslinking, density, and disorder

description: Low-crosslinked polymer networks have recently been found to behave auxetically when subjected to small tensions, that is, their Poisson's ratio ν becomes negative. In addition, for specific state points, numerical simulations revealed that diamond-like networks reach the limit of mechanical stability, exhibiting values of ν = −1, a condition that we define as hyper-auxeticity. This behavior is interesting per se for its consequences in materials science but is also appealing for fundamental physics because the mechanical instability is accompanied by evidence of criticality. In this work, we deepen our understanding of this phenomenon by performing a large set of equilibrium and stress–strain simulations in combination with phenomenological elasticity theory. The two approaches are found to be in good agreement, confirming the above results. We also extend our investigations to disordered polymer networks and find that the hyper-auxetic behavior also holds in this case, still manifesting a similar critical-like behavior as in the diamond one. Finally, we highlight the role of the number density, which is found to be a relevant control parameter determining the elastic properties of the system. The validity of the results under disordered conditions paves the way for an experimental investigation of this phenomenon in real systems, such as hydrogels.
DOI: 10.24435/materialscloud:nj-vg
title: Learning a reactive potential for silica-water through uncertainty attribution

description: The reactivity of silicates in aqueous solution is relevant to various chemistries ranging from silicate minerals in geology, to the C-S-H phase in cement, nanoporous zeolite catalysts, or highly porous precipitated silica. While simulations of chemical reactions can provide insight at the molecular level, balancing accuracy and scale in reactive simulations in the condensed phase is a challenge. Here, we demonstrate how a machine-learning reactive interatomic potential can accurately capture silicate-water reactivity. The model was trained on a new dataset comprising 400,000 energies and forces of molecular clusters at the 𝜔-B97XD\def2-TVZP level. To ensure the robustness of the model, we introduce a new and general active learning strategy based on the attribution of the model uncertainty, that automatically isolates uncertain regions of bulk simulations to be calculated as small-sized clusters. Our trained potential is found to reproduce static and dynamic properties of liquid water and solid crystalline silicates, despite having been trained exclusively on cluster data. Furthermore, we utilize enhanced sampling simulations to recover the self-ionization reactivity of water accurately, and the acidity of silicate oligomers, and lastly study the silicate dimerization reaction in a water solution at neutral conditions and find that the reaction occurs through a flanking mechanism.
DOI: 10.24435/materialscloud:59-87
title: Auxetic polymer networks: The role of crosslinking, density, and disorder

description: Low-crosslinked polymer networks have recently been found to behave auxetically when subjected to small tensions, that is, their Poisson's ratio ν becomes negative. In addition, for specific state points, numerical simulations revealed that diamond-like networks reach the limit of mechanical stability, exhibiting values of ν = −1, a condition that we define as hyper-auxeticity. This behavior is interesting per se for its consequences in materials science but is also appealing for fundamental physics because the mechanical instability is accompanied by evidence of criticality. In this work, we deepen our understanding of this phenomenon by performing a large set of equilibrium and stress–strain simulations in combination with phenomenological elasticity theory. The two approaches are found to be in good agreement, confirming the above results. We also extend our investigations to disordered polymer networks and find that the hyper-auxetic behavior also holds in this case, still manifesting a similar critical-like behavior as in the diamond one. Finally, we highlight the role of the number density, which is found to be a relevant control parameter determining the elastic properties of the system. The validity of the results under disordered conditions paves the way for an experimental investigation of this phenomenon in real systems, such as hydrogels.
DOI: 10.24435/materialscloud:5w-g1
title: Identifying the structure of supported metal catalysts using vibrational fingerprints from ab initio nanoscale models

description: The anharmonic infrared spectrum of adsorbed CO is simulated using density functional theory (DFT) to gain insight into the nature of Pd nanoparticles (NPs) supported on ceria. The authors systematically determine how the simulated infrared spectra are affected by CO coverage, NP size (0.5–1.5 nm), NP morphology (octahedral, icosahedral), and metal-support contact angle, by exploring a diversity of realistic models inspired by ab initio molecular dynamics.
DOI: 10.24435/materialscloud:33-ch
title: Oxidation of interfacial cobalt controls the pH-dependence of the oxygen evolution reaction

description: Transition metal oxides (e.g., cobalt oxides) often undergo a dynamic surface reconstruction under oxygen evolution reaction (OER) conditions to form the active state, which differs in response to the electrolyte pH. The resulting pH-dependency of OER activity is commonly observed but poorly understood. Herein, we demonstrate that operando X-ray absorption spectroscopy (XAS) characterization enables tracking of the Co oxidation transformation at different pH-directed (hydr)oxide/electrolyte interfaces. Combined with in situ electrochemical analyses, correlations between Co redox dynamics, flat band potential and Co oxidation transformations are established to explain the pH-dependency of OER activity. In alkaline environments, the low flat band potential allows a low-potential Co redox transformation, which in turns favors surface reconstruction. In neutral and acidic environments, an anodic shift of the Co redox transformation increases the OER overpotential, particularly in an acidic environment. The largest OER overpotential, in a neutral environment, is further attributable to the poor polarizability of Co atoms and the slowest Co oxidation transformation with respect to the change in applied potential (or OER current). These findings reveal that the Co oxidation transformation at the interface is the factor directly determining the pH-dependency of OER activity, therefore providing insight into designing efficient OER catalysts in different pH environments.
DOI: 10.24435/materialscloud:ep-3z
title: Identifying the structure of supported metal catalysts using vibrational fingerprints from ab initio nanoscale models

description: The anharmonic infrared spectrum of adsorbed CO is simulated using density functional theory (DFT) to gain insight into the nature of Pd nanoparticles (NPs) supported on ceria. The authors systematically determine how the simulated infrared spectra are affected by CO coverage, NP size (0.5–1.5 nm), NP morphology (octahedral, icosahedral), and metal-support contact angle, by exploring a diversity of realistic models inspired by ab initio molecular dynamics.
DOI: 10.24435/materialscloud:a7-88
title: Oxidation of interfacial cobalt controls the pH-dependence of the oxygen evolution reaction

description: Transition metal oxides (e.g., cobalt oxides) often undergo a dynamic surface reconstruction under oxygen evolution reaction (OER) conditions to form the active state, which differs in response to the electrolyte pH. The resulting pH-dependency of OER activity is commonly observed but poorly understood. Herein, we demonstrate that operando X-ray absorption spectroscopy (XAS) characterization enables tracking of the Co oxidation transformation at different pH-directed (hydr)oxide/electrolyte interfaces. Combined with in situ electrochemical analyses, correlations between Co redox dynamics, flat band potential and Co oxidation transformations are established to explain the pH-dependency of OER activity. In alkaline environments, the low flat band potential allows a low-potential Co redox transformation, which in turns favors surface reconstruction. In neutral and acidic environments, an anodic shift of the Co redox transformation increases the OER overpotential, particularly in an acidic environment. The largest OER overpotential, in a neutral environment, is further attributable to the poor polarizability of Co atoms and the slowest Co oxidation transformation with respect to the change in applied potential (or OER current). These findings reveal that the Co oxidation transformation at the interface is the factor directly determining the pH-dependency of OER activity, therefore providing insight into designing efficient OER catalysts in different pH environments.
DOI: 10.24435/materialscloud:hc-zb
title: Machine learning enables the discovery of 2D Invar and anti-Invar monolayers

description: Materials demonstrating positive thermal expansion (PTE) or negative thermal expansion (NTE) are quite common, whereas those exhibiting zero thermal expansion (ZTE) are notably scarce. In this work, we identify the mechanical descriptors, namely in-plane tensile stiffness and out-of-plane bending stiffness, that can effectively classify PTE and NTE 2D crystals. By utilizing high throughput calculations and the state-of-the-art symbolic regression method, these descriptors aid in the discovery of ZTE or 2D Invar monolayers with the linear thermal expansion coefficient (LTEC) within ±2×10⁻⁶ K⁻¹ in the middle range of temperatures. Additionally, the descriptors assist the discovery of large PTE and NTE 2D monolayers with the LTEC larger than ±15×10⁻⁶ K⁻¹, which are so-called 2D anti-Invar monolayers. Advancing our understanding of materials with exceptionally low or high thermal expansion is of substantial scientific and technological interest, particularly in developing next-generation electronics at the nanometer even Ångstrom scale.
DOI: 10.24435/materialscloud:2z-89
title: Machine learning enables the discovery of 2D Invar and anti-Invar monolayers

description: Materials demonstrating positive thermal expansion (PTE) or negative thermal expansion (NTE) are quite common, whereas those exhibiting zero thermal expansion (ZTE) are notably scarce. In this work, we identify the mechanical descriptors, namely in-plane tensile stiffness and out-of-plane bending stiffness, that can effectively classify PTE and NTE 2D crystals. By utilizing high throughput calculations and the state-of-the-art symbolic regression method, these descriptors aid in the discovery of ZTE or 2D Invar monolayers with the linear thermal expansion coefficient (LTEC) within ±2×10⁻⁶ K⁻¹ in the middle range of temperatures. Additionally, the descriptors assist the discovery of large PTE and NTE 2D monolayers with the LTEC larger than ±15×10⁻⁶ K⁻¹, which are so-called 2D anti-Invar monolayers. Advancing our understanding of materials with exceptionally low or high thermal expansion is of substantial scientific and technological interest, particularly in developing next-generation electronics at the nanometer even Ångstrom scale.
DOI: 10.24435/materialscloud:mb-da
title: Comparative study of defects in graphene flake grown on amorphous and crystalline copper surfaces

description: We performed a computational study using Density Functional Theory calculations on a copper-graphene system. A global minima search was performed using the Minima Hopping algorithm to find multiple graphene isomers that can appear on different substrates. As a model of copper substrate, we used a 112 copper atom cluster and as a model of graphene flake, we used 40 carbon atoms on top of the copper surface. The system was placed in vacuum to guarantee no interaction between images.

The dataset contains coordinates of graphene flake isomers on crystalline and amorphous Cu substrates.
DOI: 10.24435/materialscloud:1h-0d
title: Comparative study of defects in graphene flake grown on amorphous and crystalline copper surfaces

description: We performed a computational study using Density Functional Theory calculations on a copper-graphene system. A global minima search was performed using the Minima Hopping algorithm to find multiple graphene isomers that can appear on different substrates. As a model of copper substrate, we used a 112 copper atom cluster and as a model of graphene flake, we used 40 carbon atoms on top of the copper surface. The system was placed in vacuum to guarantee no interaction between images.

The dataset contains coordinates of graphene flake isomers on crystalline and amorphous Cu substrates.
DOI: 10.24435/materialscloud:eq-s8
title: Ge(110) c(8×10) reconstructions stabilized by vibrations

description: Determining the atomic structure of a surface is essential for reliable simulations and in-depth exploration of chemical and atomic-scale physical processes. Using Ge(110) c(8×10) as a case study, this work employs Density Functional Theory (DFT) calculations to examine the role of vibrational entropy in surface reconstruction stability. The Ge(110) c(8×10) unit cell consists of interstitial-based pentamers (Universal Building Block model, UBB) interspersed with regions appearing in STM images as unreconstructed. DFT calculations predict that adding more pentamers lowers the surface energy, contradicting experimental findings. This discrepancy is resolved when vibrational entropy is accounted for and sur-face divacancies are introduced in addition to the UBB pentamers. These divacancies are similar to those proposed earlier in the Tetramer-Heptagonal and Tetragonal Ring (THTR) reconstruction model. The nearest neighbors of the vacancy sites are rebonded as on monatomic step edges. The differences in the vibrational entropy contributed by pentamers, divacancies, and unreconstructed surface stabilize Ge(110) c(8×10) reconstructions with the pentamer density observed experimentally. The presence of divacancies is conceptually consistent with the presence of monatomic steps in Ge(110) "16×2", the most stable reconstruction of this surface.
DOI: 10.24435/materialscloud:d3-za
title: Ge(110) c(8×10) reconstructions stabilized by vibrations

description: Determining the atomic structure of a surface is essential for reliable simulations and in-depth exploration of chemical and atomic-scale physical processes. Using Ge(110) c(8×10) as a case study, this work employs Density Functional Theory (DFT) calculations to examine the role of vibrational entropy in surface reconstruction stability. The Ge(110) c(8×10) unit cell consists of interstitial-based pentamers (Universal Building Block model, UBB) interspersed with regions appearing in STM images as unreconstructed. DFT calculations predict that adding more pentamers lowers the surface energy, contradicting experimental findings. This discrepancy is resolved when vibrational entropy is accounted for and sur-face divacancies are introduced in addition to the UBB pentamers. These divacancies are similar to those proposed earlier in the Tetramer-Heptagonal and Tetragonal Ring (THTR) reconstruction model. The nearest neighbors of the vacancy sites are rebonded as on monatomic step edges. The differences in the vibrational entropy contributed by pentamers, divacancies, and unreconstructed surface stabilize Ge(110) c(8×10) reconstructions with the pentamer density observed experimentally. The presence of divacancies is conceptually consistent with the presence of monatomic steps in Ge(110) "16×2", the most stable reconstruction of this surface.
DOI: 10.24435/materialscloud:kp-2v
title: Spin-dependent interactions in orbital-density-dependent functionals: non-collinear Koopmans spectral functionals

description: The presence of spin-orbit coupling or non-collinear magnetic spin states can have dramatic effects on the ground-state and spectral properties of materials, in particular on the band structure. Here, we develop non-collinear Koopmans-compliant functionals based on Wannier functions and density-functional perturbation theory, targeting accurate spectral properties in the quasiparticle approximation. Our non-collinear Koopmans-compliant theory involves functionals of four-component orbitals densities, that can be obtained from the charge and spin-vector densities of Wannier functions. We validate our approach on four emblematic non-magnetic and magnetic semiconductors where the effect of spin-orbit coupling goes from small to very large: the III-IV semiconductor GaAs, the transition-metal dichalcogenide WSe₂, the cubic perovskite CsPbBr₃, and the ferromagnetic semiconductor CrI₃. 
The predicted band gaps are comparable in accuracy to state-of-the-art many-body perturbation theory and include spin-dependent interactions and screening effects that are missing in standard diagrammatic approaches based on the random phase approximation. While the inclusion of orbital- and spin-dependent interactions in many-body perturbation theory requires self-screening or vertex corrections, they emerge naturally in the Koopmans-functionals framework.
DOI: 10.24435/materialscloud:2j-mn
title: Spin-dependent interactions in orbital-density-dependent functionals: non-collinear Koopmans spectral functionals

description: The presence of spin-orbit coupling or non-collinear magnetic spin states can have dramatic effects on the ground-state and spectral properties of materials, in particular on the band structure. Here, we develop non-collinear Koopmans-compliant functionals based on Wannier functions and density-functional perturbation theory, targeting accurate spectral properties in the quasiparticle approximation. Our non-collinear Koopmans-compliant theory involves functionals of four-component orbitals densities, that can be obtained from the charge and spin-vector densities of Wannier functions. We validate our approach on four emblematic non-magnetic and magnetic semiconductors where the effect of spin-orbit coupling goes from small to very large: the III-IV semiconductor GaAs, the transition-metal dichalcogenide WSe₂, the cubic perovskite CsPbBr₃, and the ferromagnetic semiconductor CrI₃. 
The predicted band gaps are comparable in accuracy to state-of-the-art many-body perturbation theory and include spin-dependent interactions and screening effects that are missing in standard diagrammatic approaches based on the random phase approximation. While the inclusion of orbital- and spin-dependent interactions in many-body perturbation theory requires self-screening or vertex corrections, they emerge naturally in the Koopmans-functionals framework.
DOI: 10.24435/materialscloud:3d-cn
title: From Methane to Methanol: Pd-iC-CeO2 Catalysts Engineered for High Selectivity via Mechano-Chemical Synthesis

description: In the pursuit of selective conversion of methane directly to methanol in the liquid phase, a common challenge is the concurrent formation of undesirable liquid oxygenates or combustion byproducts. However, we demonstrate that monometallic Pd-CeO2 catalysts, modified by carbon, created by a simple mechanochemical synthesis method exhibit 100% selectivity towards methanol at 75°C, using hydrogen peroxide as oxidizing agent. The solvent-free synthesis yields a distinctive Pd-iC-CeO2 interface, where interfacial carbon (iC) modulates metal-oxide interactions and facilitates tandem methane activation and peroxide decomposition, thus resulting in an exclusive methanol selectivity of 100% with a rate of 117 µmol/gcat at 75°C. Notably, solvent interactions of H2O2 (aq) were found to be critical for methanol selectivity through a DFT-simulated Eley-Rideal-like mechanism. This mechanism uniquely enables the direct conversion of methane into methanol via a solid-liquid-gas process.
DOI: 10.24435/materialscloud:dz-zz
title: From Methane to Methanol: Pd-iC-CeO2 Catalysts Engineered for High Selectivity via Mechano-Chemical Synthesis

description: In the pursuit of selective conversion of methane directly to methanol in the liquid phase, a common challenge is the concurrent formation of undesirable liquid oxygenates or combustion byproducts. However, we demonstrate that monometallic Pd-CeO2 catalysts, modified by carbon, created by a simple mechanochemical synthesis method exhibit 100% selectivity towards methanol at 75°C, using hydrogen peroxide as oxidizing agent. The solvent-free synthesis yields a distinctive Pd-iC-CeO2 interface, where interfacial carbon (iC) modulates metal-oxide interactions and facilitates tandem methane activation and peroxide decomposition, thus resulting in an exclusive methanol selectivity of 100% with a rate of 117 µmol/gcat at 75°C. Notably, solvent interactions of H2O2 (aq) were found to be critical for methanol selectivity through a DFT-simulated Eley-Rideal-like mechanism. This mechanism uniquely enables the direct conversion of methane into methanol via a solid-liquid-gas process.