cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24435/materialscloud:2020.0022/v1
title: Evidence for carbon clusters present near thermal gate oxides affecting the electronic band structure in SiC-MOSFET

description: High power SiC MOSFET technologies are critical for energy saving in, e.g., distribution of electrical power. They suffer, however, from low near-interface mobility, the origin of which has not yet been conclusively determined. Here, we present unique concerting evidence for the presence of interface defects in the form of carbon clusters at native thermally processed oxides of SiC. These clusters, with a diameter of 2–5 nm, are HF-etch resistant and possess a mixture of graphitic (sp2) and amorphous (sp3 mixed in sp2) carbon bonds different from the normal sp3 carbon present in 4H-SiC. The nucleation of such defects during thermal oxidation as well as their atomic structure is elucidated by state-of-the-art atomistic and electronic structure calculations. In addition, our property prediction techniques show the impact of the simulated carbon accumulates on the electronic structure at the interface.
DOI: 10.24435/materialscloud:2019.0060/v1
title: Electron transport through metal/MoS2 interfaces: edge- or area-dependent process?

description: In ultra-thin two-dimensional (2-D) materials, the formation of ohmic contacts with top metallic layers is a challenging task that involves different processes than in bulk-like structures. Besides the Schottky barrier height, the transfer length of electrons between metals and 2-D monolayers is a highly relevant parameter. For MoS2, both short (≤30 nm) and long (≥0.5 μm) values have been reported, corresponding to either an abrupt carrier injection at the contact edge or a more gradual transfer of electrons over a large contact area. Here we use ab initio quantum transport simulations to demonstrate that the presence of an oxide layer between a metallic contact and a MoS2 monolayer, for example TiO2 in case of titanium electrodes, favors an area-dependent process with a long transfer length, while a perfectly clean metal-semiconductor interface would lead to an edge process. These findings reconcile several theories that have been postulated about the physics of metal/MoS2 interfaces and provide a framework to design future devices with lower contact resistances.
DOI: 10.24435/materialscloud:sx-g8
title: Evidence for carbon clusters present near thermal gate oxides affecting the electronic band structure in SiC-MOSFET

description: High power SiC MOSFET technologies are critical for energy saving in, e.g., distribution of electrical power. They suffer, however, from low near-interface mobility, the origin of which has not yet been conclusively determined. Here, we present unique concerting evidence for the presence of interface defects in the form of carbon clusters at native thermally processed oxides of SiC. These clusters, with a diameter of 2–5 nm, are HF-etch resistant and possess a mixture of graphitic (sp2) and amorphous (sp3 mixed in sp2) carbon bonds different from the normal sp3 carbon present in 4H-SiC. The nucleation of such defects during thermal oxidation as well as their atomic structure is elucidated by state-of-the-art atomistic and electronic structure calculations. In addition, our property prediction techniques show the impact of the simulated carbon accumulates on the electronic structure at the interface.
DOI: 10.24435/materialscloud:2020.0023/v1
title: Influence of an external electric field on the potential energy surface of alkali decorated C60

description: We present a fully ab-initio, unbiased structure search of the configurational space of decorated C60 fullerenes in the presence of an electric field. We observed that the potential energy surface (PES) is significantly perturbed by an external electric field and that the energetic ordering of low energy isomers differs with and without electric field. We identify the energetically lowest configuration for a varying number of decorating atoms (1 ≤ n ≤ 12) for Li and (1 ≤ n ≤ 6) for K on the C60 surface at different electric field strengths. Using the correct geometric ground state in the electric field for the calculation of the dipole we obtain better agreement with the experimentally measured values than previous calculations based on the ground state in absence of an electric field. Since the lowest energy structures are typically nearly degenerate in energy, a combination of different structures is expected to be found at room temperature. The experimentally measured dipole is therefore also expected to contain significant contributions from several low energy structures.
DOI: 10.24435/materialscloud:w0-53
title: Electron transport through metal/MoS2 interfaces: edge- or area-dependent process?

description: In ultra-thin two-dimensional (2-D) materials, the formation of ohmic contacts with top metallic layers is a challenging task that involves different processes than in bulk-like structures. Besides the Schottky barrier height, the transfer length of electrons between metals and 2-D monolayers is a highly relevant parameter. For MoS2, both short (≤30 nm) and long (≥0.5 μm) values have been reported, corresponding to either an abrupt carrier injection at the contact edge or a more gradual transfer of electrons over a large contact area. Here we use ab initio quantum transport simulations to demonstrate that the presence of an oxide layer between a metallic contact and a MoS2 monolayer, for example TiO2 in case of titanium electrodes, favors an area-dependent process with a long transfer length, while a perfectly clean metal-semiconductor interface would lead to an edge process. These findings reconcile several theories that have been postulated about the physics of metal/MoS2 interfaces and provide a framework to design future devices with lower contact resistances.
DOI: 10.24435/materialscloud:qp-6j
title: Influence of an external electric field on the potential energy surface of alkali decorated C60

description: We present a fully ab-initio, unbiased structure search of the configurational space of decorated C60 fullerenes in the presence of an electric field. We observed that the potential energy surface (PES) is significantly perturbed by an external electric field and that the energetic ordering of low energy isomers differs with and without electric field. We identify the energetically lowest configuration for a varying number of decorating atoms (1 ≤ n ≤ 12) for Li and (1 ≤ n ≤ 6) for K on the C60 surface at different electric field strengths. Using the correct geometric ground state in the electric field for the calculation of the dipole we obtain better agreement with the experimentally measured values than previous calculations based on the ground state in absence of an electric field. Since the lowest energy structures are typically nearly degenerate in energy, a combination of different structures is expected to be found at room temperature. The experimentally measured dipole is therefore also expected to contain significant contributions from several low energy structures.
DOI: 10.24435/materialscloud:2019.0062/v1
title: Synthesis and Characterization of π-Extended Triangulene

description: In a recent work, we reported a combined in-solution and on-surface synthesis of π-extended triangulene, a non-Kekulé nanographene with the structural formula C33H15, consisting of ten benzene rings fused in a triangular fashion. The distinctive topology of the molecule entails the presence of three unpaired electrons that couple to form a spin quartet ground state. In this record we include experimental and computational data that support the findings of the manuscript.
DOI: 10.24435/materialscloud:2019.0061/v1
title: Mechanism and control parameters of the coupled structural and metal-insulator transition in nickelates

description: Rare-earth nickelates exhibit a remarkable metal-insulator transition accompanied by a symmetry-lowering structural distortion. Using model considerations and first-principles calculations, we present a theory of this phase transition which reveals the key role of the coupling between electronic and lattice instabilities. We show that the transition is driven by the proximity to an instability towards electronic disproportionation which couples to a specific structural distortion mode, cooperatively driving the system into the insulating state. This allows us to identify two key control parameters of the transition: the susceptibility to electronic disproportionation and the stiffness of the lattice mode. We show that our findings can be rationalized in terms of a Landau theory involving two coupled order parameters, with general implications for transition-metal oxides.
DOI: 10.24435/materialscloud:s6-j2
title: Synthesis and Characterization of π-Extended Triangulene

description: In a recent work, we reported a combined in-solution and on-surface synthesis of π-extended triangulene, a non-Kekulé nanographene with the structural formula C33H15, consisting of ten benzene rings fused in a triangular fashion. The distinctive topology of the molecule entails the presence of three unpaired electrons that couple to form a spin quartet ground state. In this record we include experimental and computational data that support the findings of the manuscript.
DOI: 10.24435/materialscloud:2019.0063/v1
title: Rethinking Metadynamics

description: Metadynamics is an enhanced sampling method of great popularity, based on the on-the-fly construction of a bias potential that is function of a selected number of collective variables. In order to improve its usability and convergence speed, we propose here a change in perspective that shifts the focus form the bias to the probability distribution reconstruction. The result is an enhanced sampling method that combine metadynamics and adaptive umbrella sampling approaches, taking the best from the two worlds. This new method has a straightforward reweighting scheme and allows for efficient importance sampling, avoiding uninteresting high free energy regions. Thanks to a compressed kernel density estimation it can handle a higher dimensional collective variable space, and does not require the prior knowledge of the boundaries of such space. The new method comes in two variants. The first aims at a quick convergence, avoiding oscillations and maximizing the quasi-static bias regime, while in the second the main focus is on a rapid exploration of the free energy landscape. We demonstrate the performance of the method in a number of representative examples.
DOI: 10.24435/materialscloud:xm-8v
title: Mechanism and control parameters of the coupled structural and metal-insulator transition in nickelates

description: Rare-earth nickelates exhibit a remarkable metal-insulator transition accompanied by a symmetry-lowering structural distortion. Using model considerations and first-principles calculations, we present a theory of this phase transition which reveals the key role of the coupling between electronic and lattice instabilities. We show that the transition is driven by the proximity to an instability towards electronic disproportionation which couples to a specific structural distortion mode, cooperatively driving the system into the insulating state. This allows us to identify two key control parameters of the transition: the susceptibility to electronic disproportionation and the stiffness of the lattice mode. We show that our findings can be rationalized in terms of a Landau theory involving two coupled order parameters, with general implications for transition-metal oxides.
DOI: 10.24435/materialscloud:2019.0071/v1
title: Electron density learning of non-covalent systems

description: Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origin of these interactions, the quantification of their magnitude and their visualization in real-space. The total electron density ρ(r) represents the simplest yet most comprehensive piece of information available for fully characterizing bonding patterns and non-covalent interactions. The charge density of a molecule can be computed by solving the Schrödinger equation, but this approach becomes rapidly demanding if the electron density has to be evaluated for thousands of different molecules or very large chemical systems, such as peptides and proteins. Here we present a transferable and scalable machine-learning model capable of predicting the total electron density directly from the atomic coordinates. The regression model is used to access qualitative and quantitative insights beyond the underlying ρ(r) in a diverse ensemble of sidechain–sidechain dimers extracted from the BioFragment database (BFDb). The transferability of the model to more complex chemical systems is demonstrated by predicting and analyzing the electron density of a collection of 8 polypeptides.
DOI: 10.24435/materialscloud:2019.0063/v2
title: Rethinking Metadynamics

description: Metadynamics is an enhanced sampling method of great popularity, based on the on-the-fly construction of a bias potential that is a function of a selected number of collective variables. We propose here a change in perspective that shifts the focus from the bias to the probability distribution reconstruction while retaining some of the key characteristics of metadynamics, such as flexible on-the-fly adjustments to the free energy estimate. The result is an enhanced sampling method that presents a drastic improvement in convergence speed, especially when dealing with suboptimal and/or multidimensional sets of collective variables. The method is especially robust and easy to use and in fact requires only a few simple parameters to be set, and it has a straightforward reweighting scheme to recover the statistics of the unbiased ensemble. Furthermore, it gives more control of the desired exploration of the phase space since the deposited bias is not allowed to grow indefinitely and it does not push the simulation to uninteresting high free energy regions. We demonstrate the performance of the method in a number of representative examples.
DOI: 10.24435/materialscloud:v4-xd
title: Electron density learning of non-covalent systems

description: Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origin of these interactions, the quantification of their magnitude and their visualization in real-space. The total electron density ρ(r) represents the simplest yet most comprehensive piece of information available for fully characterizing bonding patterns and non-covalent interactions. The charge density of a molecule can be computed by solving the Schrödinger equation, but this approach becomes rapidly demanding if the electron density has to be evaluated for thousands of different molecules or very large chemical systems, such as peptides and proteins. Here we present a transferable and scalable machine-learning model capable of predicting the total electron density directly from the atomic coordinates. The regression model is used to access qualitative and quantitative insights beyond the underlying ρ(r) in a diverse ensemble of sidechain–sidechain dimers extracted from the BioFragment database (BFDb). The transferability of the model to more complex chemical systems is demonstrated by predicting and analyzing the electron density of a collection of 8 polypeptides.
DOI: 10.24435/materialscloud:2019.0064/v1
title: On-Surface Synthesis of a Non-Planar Porous Nanographene

description: In a recent publication we demonstrated the on surface synthesis of a porous nanographene and we characterized its electronic properties combining spectroscopy experiments to ab-initio simulations. In this record we provide experimental and computational data that support the results discussed in the manuscript.
DOI: 10.24435/materialscloud:ek-3s
title: Rethinking Metadynamics

description: Metadynamics is an enhanced sampling method of great popularity, based on the on-the-fly construction of a bias potential that is a function of a selected number of collective variables. We propose here a change in perspective that shifts the focus from the bias to the probability distribution reconstruction while retaining some of the key characteristics of metadynamics, such as flexible on-the-fly adjustments to the free energy estimate. The result is an enhanced sampling method that presents a drastic improvement in convergence speed, especially when dealing with suboptimal and/or multidimensional sets of collective variables. The method is especially robust and easy to use and in fact requires only a few simple parameters to be set, and it has a straightforward reweighting scheme to recover the statistics of the unbiased ensemble. Furthermore, it gives more control of the desired exploration of the phase space since the deposited bias is not allowed to grow indefinitely and it does not push the simulation to uninteresting high free energy regions. We demonstrate the performance of the method in a number of representative examples.
DOI: 10.24435/materialscloud:zt-zq
title: On-Surface Synthesis of a Non-Planar Porous Nanographene

description: In a recent publication we demonstrated the on surface synthesis of a porous nanographene and we characterized its electronic properties combining spectroscopy experiments to ab-initio simulations. In this record we provide experimental and computational data that support the results discussed in the manuscript.
DOI: 10.24435/materialscloud:2019.0065/v1
title: Neural networks-based variationally enhanced sampling

description: Sampling complex free-energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here, we propose to use machine-learning techniques in conjunction with the recent variationally enhanced sampling method [O. Valsson, M. Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free-energy surfaces, removes boundary effects artifacts, and allows several collective variables to be handled.
DOI: 10.24435/materialscloud:90-vd
title: Assessing the persistence of chalcogen bonds in solution with neural network potentials

description: Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on Density Functional Theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level.  Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what are the most prevalent non-covalent interactions occurring in a solute-Cl-THF mixture. The simulations in explicit solvent highlight the clear competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP, and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non--covalent interaction interplay occurring in solution.
DOI: 10.24435/materialscloud:pc-6d
title: Neural networks-based variationally enhanced sampling

description: Sampling complex free-energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here, we propose to use machine-learning techniques in conjunction with the recent variationally enhanced sampling method [O. Valsson, M. Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free-energy surfaces, removes boundary effects artifacts, and allows several collective variables to be handled.
DOI: 10.24435/materialscloud:m1-ka
title: Assessing the persistence of chalcogen bonds in solution with neural network potentials

description: Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on Density Functional Theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level.  Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what are the most prevalent non-covalent interactions occurring in a solute-Cl-THF mixture. The simulations in explicit solvent highlight the clear competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP, and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non--covalent interaction interplay occurring in solution.