cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:4g-mx
title: Impact of glutamate carboxylation in the adsorption of the alpha-1 domain of osteocalcin to hydroxyapatite and titania

description: One proposed mechanism of implant fouling is attributed to the nonspecific adsorption of non-collagenous bone matrix proteins (NCPs) onto a newly implanted interface. With the goal of capturing the fundamental mechanistic and thermodynamic forces that govern changes in these NCP recognition domains as a function of γ-carboxyglutamic acid (Gla) post-translational modification and surface chemistry, we probe the adsorption process of the most commonly occurring NCP, osteocalcin, onto a mineral and metal oxide surface. Here, we apply two enhanced sampling methods to independently probe the effects of post-translational modification and peptide structure on adsorption. First, well-tempered metadynamics was used to capture the binding of acetyl and N-methylamide capped glutamic acid and Gla single amino acids onto crystalline hydroxyapatite and titania model surfaces at physiological pH. Following this, parallel tempering metadynamics in the well-tempered ensemble (PTMetaD-WTE) was used to study adsorption of the α-1 domain of osteocalcin onto hydroxyapatite and titania. Simulations were performed for the α-1 domain of osteocalcin in both its fully decarboxylated (dOC) and fully carboxylated (OC) form. Our simulations find that increased charge density due to carboxylation results in increased interactions at the interface, and stronger adsorption of the single amino acids to both surfaces. Interestingly, the role of Gla in promoting compact and helical structure in the α-1 domain resulted in disparate binding modes at the two surfaces, which is attributed to differences in interfacial water behavior. Overall, this work provides a benchmark for understanding the mechanisms that drive adsorption of Gla-containing mineralizing proteins onto different surface chemistries.

This record contains files necessary to reproduce enhanced sampling well-tempered metadynamics (wtMTD) and parallel tempering metadynamics in the well-tempered ensemble (PT-MetaD-WTE) simulations in PLUMED using GROMACS 2018.3.
DOI: 10.24435/materialscloud:16-d6
title: Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

description: Density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard corrections to treat strongly correlated electronic states. Unfortunately, the exact values of the Hubbard U and J parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largest U correction. PBE has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.
DOI: 10.24435/materialscloud:7q-yw
title: Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

description: Density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard corrections to treat strongly correlated electronic states. Unfortunately, the exact values of the Hubbard U and J parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largest U correction. PBE has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.
DOI: 10.24435/materialscloud:k1-rv
title: Machine learning for metallurgy: a neural network potential for Al-Mg-Si

description: High-strength metal alloys achieve their performance via careful control of the nucleation, growth, and kinetics of precipitation. Alloy mechanical properties are then controlled by atomic scale phenomena such as shearing of the precipitates by dislocations. Atomistic modeling to understand the operative mechanisms requires length and time scales far larger than those accessible by first-principles methods. Here, a family of Behler-Parinello neural-network potentials (NNPs) for the Al-Mg-Si system is developed to enable quantitative studies of Al-6xxx alloys. The NNP is trained on metallurgically-important quantities computed by first principles density functional theory (DFT) leading to high fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, Al stacking fault energies, antisite defect energies, and other quantities. A preliminary examination of early-stage clustering kinetics and energetics in Al-6xxx is then made, showing the formation of low-energy Mg-Si structures and the trapping of vacancies in these clusters. The Generalized Stacking Fault Energy surfaces (GSFE) for the three prevalent β" precipitate compositions in peak-aged Al-6xxx are then computed with the NNP, and are validated by DFT computations at key points. The NNP thus shows significant transferability across structures, making it a powerful approach for chemically-accurate simulations of metallurgical phenomena in Al-Mg-Si alloys.
DOI: 10.24435/materialscloud:zd-a0
title: Machine learning for metallurgy: a neural network potential for Al-Mg-Si

description: High-strength metal alloys achieve their performance via careful control of the nucleation, growth, and kinetics of precipitation. Alloy mechanical properties are then controlled by atomic scale phenomena such as shearing of the precipitates by dislocations. Atomistic modeling to understand the operative mechanisms requires length and time scales far larger than those accessible by first-principles methods. Here, a family of Behler-Parinello neural-network potentials (NNPs) for the Al-Mg-Si system is developed to enable quantitative studies of Al-6xxx alloys. The NNP is trained on metallurgically-important quantities computed by first principles density functional theory (DFT) leading to high fidelity predictions of intermetallic compounds, elastic constants, dilute solid-solution energetics, precipitate/matrix interfaces, Al stacking fault energies, antisite defect energies, and other quantities. A preliminary examination of early-stage clustering kinetics and energetics in Al-6xxx is then made, showing the formation of low-energy Mg-Si structures and the trapping of vacancies in these clusters. The Generalized Stacking Fault Energy surfaces (GSFE) for the three prevalent β" precipitate compositions in peak-aged Al-6xxx are then computed with the NNP, and are validated by DFT computations at key points. The NNP thus shows significant transferability across structures, making it a powerful approach for chemically-accurate simulations of metallurgical phenomena in Al-Mg-Si alloys.
DOI: 10.24435/materialscloud:mc-kj
title: On-surface synthesis and characterization of nitrogen-substituted undecacenes

description: In this record, we provide the data supporting our recent results on the synthesis of nitrogen-substituted undecacene analogs. Heteroatom substitution in acenes allows to tailor their remarkable electronic properties, expected to include spin-polarization and magnetism for larger members of the acene family. Here, we present a strategy for the on-surface synthesis of three undecacene analogs substituted with four nitrogen atoms on an Au(111) substrate, by employing specifically designed diethano-bridged precursors. A similarly designed precursor is used to synthesize the pristine undecacene molecule. In the publication where the results are discussed, the experimental features of scanning probe microscopy are compared with ab initio simulations, to demonstrate that the ground state of the synthesized tetraazaundecacene has considerable open-shell character on Au(111). Additionally, we demonstrate that electronegative nitrogen atoms induce a considerable shift in energy level alignment compared to the pristine undecacene, and that the introduction of hydro-aza groups causes local anti-aromaticity in the synthesized compounds. Our work provides access to the precise fabrication of nitrogen-substituted acene and its analogs, potential building-blocks of organic electronics and spintronics, and a rich playground to explore pi-electron correlation.
DOI: 10.24435/materialscloud:yt-xb
title: On-surface synthesis and characterization of nitrogen-substituted undecacenes

description: In this record, we provide the data supporting our recent results on the synthesis of nitrogen-substituted undecacene analogs. Heteroatom substitution in acenes allows to tailor their remarkable electronic properties, expected to include spin-polarization and magnetism for larger members of the acene family. Here, we present a strategy for the on-surface synthesis of three undecacene analogs substituted with four nitrogen atoms on an Au(111) substrate, by employing specifically designed diethano-bridged precursors. A similarly designed precursor is used to synthesize the pristine undecacene molecule. In the publication where the results are discussed, the experimental features of scanning probe microscopy are compared with ab initio simulations, to demonstrate that the ground state of the synthesized tetraazaundecacene has considerable open-shell character on Au(111). Additionally, we demonstrate that electronegative nitrogen atoms induce a considerable shift in energy level alignment compared to the pristine undecacene, and that the introduction of hydro-aza groups causes local anti-aromaticity in the synthesized compounds. Our work provides access to the precise fabrication of nitrogen-substituted acene and its analogs, potential building-blocks of organic electronics and spintronics, and a rich playground to explore pi-electron correlation.
DOI: 10.24435/materialscloud:3y-gr
title: Understanding the diversity of the metal-organic framework ecosystem

description: By combining metal nodes and organic linkers one can make millions of different metal-organic frameworks (MOFs). At present over 90,000 MOFs have been synthesized and there are databases with over 500,000 predicted structures. This raises the question of whether a new experimental or predicted structure adds new information. For MOF-chemists the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we show how machine learning can be used to quantify similarities of MOFs. This quantification allows us to use techniques from ecology to analyse the chemical diversity of these materials in terms of diversity metrics. In particular, we show that this diversity analysis can identify biases in the databases, and how such bias can lead to incorrect conclusions. This formalism provides us with a simple and powerful practical guideline to see whether a set of new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.
DOI: 10.24435/materialscloud:e0-qn
title: On-surface synthesis of unsaturated carbon nanostructures with regularly fused pentagon-heptagon pairs

description: In this record we provide data to support our recent findings for the fabrication of Unsaturated Carbon Nanostructures with Regularly Fused Pentagon–Heptagon Pairs. Multiple fused pentagon–heptagon pairs are frequently found as defects at the grain boundaries of the hexagonal graphene lattice and are suggested to have a fundamental influence on graphene-related materials. However, the construction of sp2-carbon skeletons with multiple regularly fused pentagon–heptagon pairs is challenging. In this work, we found that the pentagon–heptagon skeleton of azulene was rearranged during the thermal reaction of an azulene-incorporated organometallic polymer on Au(111). The resulting sp2-carbon frameworks were characterized by high-resolution scanning probe microscopy techniques and feature novel polycyclic architectures composed of multiple regularly fused pentagon–heptagon pairs. Moreover, the calculated analysis of its aromaticity revealed a peculiar polar electronic structure.
DOI: 10.24435/materialscloud:mb-a3
title: Understanding the diversity of the metal-organic framework ecosystem

description: By combining metal nodes and organic linkers one can make millions of different metal-organic frameworks (MOFs). At present over 90,000 MOFs have been synthesized and there are databases with over 500,000 predicted structures. This raises the question of whether a new experimental or predicted structure adds new information. For MOF-chemists the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we show how machine learning can be used to quantify similarities of MOFs. This quantification allows us to use techniques from ecology to analyse the chemical diversity of these materials in terms of diversity metrics. In particular, we show that this diversity analysis can identify biases in the databases, and how such bias can lead to incorrect conclusions. This formalism provides us with a simple and powerful practical guideline to see whether a set of new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.
DOI: 10.24435/materialscloud:vg-0e
title: On-surface synthesis of unsaturated carbon nanostructures with regularly fused pentagon-heptagon pairs

description: In this record we provide data to support our recent findings for the fabrication of Unsaturated Carbon Nanostructures with Regularly Fused Pentagon–Heptagon Pairs. Multiple fused pentagon–heptagon pairs are frequently found as defects at the grain boundaries of the hexagonal graphene lattice and are suggested to have a fundamental influence on graphene-related materials. However, the construction of sp2-carbon skeletons with multiple regularly fused pentagon–heptagon pairs is challenging. In this work, we found that the pentagon–heptagon skeleton of azulene was rearranged during the thermal reaction of an azulene-incorporated organometallic polymer on Au(111). The resulting sp2-carbon frameworks were characterized by high-resolution scanning probe microscopy techniques and feature novel polycyclic architectures composed of multiple regularly fused pentagon–heptagon pairs. Moreover, the calculated analysis of its aromaticity revealed a peculiar polar electronic structure.
DOI: 10.24435/materialscloud:60-01
title: Correlation between electronic and structural orders in 1T-TiSe2

description: The correlation between electronic and crystal structures of 1T -TiSe2 in the charge-density wave (CDW) state is studied by x-ray diffraction in order to clarify basic properties in the CDW state, transport properties, and chirality. Three families of reflections are used to probe atomic displacements and the orbital asymmetry in Se. Two distinct onset temperatures are found: TCDW and a lower T∗ indicative for an onset of Se out-of-plane atomic displacements. T∗ coincides with a DC resistivity maximum and the onset of the proposed gyrotropic (chiral) electronic structure. However, no indication for chirality is found. The relation between the atomic displacements and the transport properties is discussed in terms of Ti 3d and Se 4p states that only weakly couple to the CDW order.
DOI: 10.24435/materialscloud:gr-tj
title: Effect of charge self-consistency in DFT+DMFT calculations for complex transition metal oxides

description: We investigate the effect of charge self-consistency (CSC) in density-functional theory plus dynamical mean-field theory calculations compared to simpler "one-shot" calculations for materials where interaction effects lead to a strong redistribution of electronic charges between different orbitals or between different sites. We focus on two systems close to a metal-insulator transition (MIT), for which the importance of CSC is currently not well understood. Specifically, we analyze the strain-related orbital polarization in the correlated metal CaVO3 and the spontaneous electronic charge disproportionation in the rare-earth nickelate LuNiO3. In both cases, we find that the CSC treatment reduces the charge redistribution compared to cheaper one-shot calculations. However, while the MIT in CaVO3 is only slightly shifted due to the reduced orbital polarization, the effect of the site polarization on the MIT in LuNiO3 is more subtle. Furthermore, we highlight the role of the double-counting correction in CSC calculations containing different inequivalent sites.
DOI: 10.24435/materialscloud:mk-yd
title: Correlation between electronic and structural orders in 1T-TiSe2

description: The correlation between electronic and crystal structures of 1T -TiSe2 in the charge-density wave (CDW) state is studied by x-ray diffraction in order to clarify basic properties in the CDW state, transport properties, and chirality. Three families of reflections are used to probe atomic displacements and the orbital asymmetry in Se. Two distinct onset temperatures are found: TCDW and a lower T∗ indicative for an onset of Se out-of-plane atomic displacements. T∗ coincides with a DC resistivity maximum and the onset of the proposed gyrotropic (chiral) electronic structure. However, no indication for chirality is found. The relation between the atomic displacements and the transport properties is discussed in terms of Ti 3d and Se 4p states that only weakly couple to the CDW order.
DOI: 10.24435/materialscloud:tx-8e
title: Effect of charge self-consistency in DFT+DMFT calculations for complex transition metal oxides

description: We investigate the effect of charge self-consistency (CSC) in density-functional theory plus dynamical mean-field theory calculations compared to simpler "one-shot" calculations for materials where interaction effects lead to a strong redistribution of electronic charges between different orbitals or between different sites. We focus on two systems close to a metal-insulator transition (MIT), for which the importance of CSC is currently not well understood. Specifically, we analyze the strain-related orbital polarization in the correlated metal CaVO3 and the spontaneous electronic charge disproportionation in the rare-earth nickelate LuNiO3. In both cases, we find that the CSC treatment reduces the charge redistribution compared to cheaper one-shot calculations. However, while the MIT in CaVO3 is only slightly shifted due to the reduced orbital polarization, the effect of the site polarization on the MIT in LuNiO3 is more subtle. Furthermore, we highlight the role of the double-counting correction in CSC calculations containing different inequivalent sites.
DOI: 10.24435/materialscloud:p3-1x
title: The role of water in host-guest interaction

description: One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.
DOI: 10.15468/dl.pk2tfk
title: Occurrence Download

description: A dataset containing 335 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-79.37079 43.64967,-79.36979 43.6473,-79.3614 43.64937,-79.36272 43.6516,-79.37079 43.64967))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 335 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067084-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.24435/materialscloud:44-xp
title: The role of water in host-guest interaction

description: One of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system's degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.