AndreaBozzo/ceres-open-data-index huggingface dataset:
The JuHemd (Jülich-Heusler-magnetic-database) of the Monte Carlo simulated critical temperatures of the magnetic phase transition for experimentally reported Heusler and Heusler-like materials

The JuHemd (Jülich-Heusler-magnetic-database) is a collection of the magnetic phase transition types and transition temperatures (Tc) for experimentally documented Heusler and Heusler-like materials, as found by density functional calculations augmented by the Monte Carlo method, and as reported by experiment in the literature.
The database contains results on 400 compounds, many of them with different setups of the chemical order/disorder, totaling 776 systems. The ground state electronic structure was obtained by density functional theory calculations with the JuKKR code. Two exchange-correlation functionals were employed: the local density approximation (LDA) and the generalized gradient approximation (GGA). The chemical disorder was treated within the coherent-potential approximation. For 306 materials (627 systems) with sizable magnetization, the Heisenberg exchange parameters were evaluated using the method of infinitesimal rotations and the Tc was determined by our in-house Monte Carlo program. The simulated data for all the compounds in the database are accompanied by experimental data published in the literature: the crystal structure, the lattice constant, the Tc and the character of the magnetic phase transition. The correlation between the experimental reports and the results for LDA and GGA functionals and the statistical analysis of these results will be published elsewhere.

Tags: critical-temperature, density-functional-theory, erc, heusler-alloy, jlvmd, juhemd, kkr, magnetic-phase-transition, magnetic-structure, max, monte-carlo
2‐D materials for ultrascaled field-effect transistors: one hundred candidates under the ab initio microscope

Due to their remarkable properties, single-layer 2-D materials appear as excellent candidates to extend Moore's scaling law beyond the currently manufactured silicon FinFETs. However, the known 2-D semiconducting components, essentially transition metal dichalcogenides, are still far from delivering the expected performance. Based on a recent theoretical study that predicts the existence of more than 1800 exfoliable 2-D materials, we investigate here the 100 most promising contenders for logic applications.

Tags: 2d-materials, ab-initio-device-simulation, cscs, marveldd3, next-generation-field-effect-transistors, performance-comparison, prace, snsf
How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

In the past decades many density-functional theory methods and codes adopting periodic boundary conditions have been developed and are now extensively used in condensed matter physics and materials science research. Only in 2016, however, their precision (i.e., to which extent properties computed with different codes agree among each other) was systematically assessed on elemental crystals: a first crucial step to evaluate the reliability of such computations. We discuss here general recommendations for verification studies aiming at further testing precision and transferability of density-functional-theory computational approaches and codes. We illustrate such recommendations using a greatly expanded protocol covering the whole periodic table from Z=1 to 96 and characterizing 10 prototypical cubic compounds for each element: 4 unaries and 6 oxides, spanning a wide range of coordination numbers and oxidation states. The primary outcome is a reference dataset of 960 equations of state cross-checked between two all-electron codes, then used to verify and improve nine pseudopotential-based approaches. Such effort is facilitated by deploying AiiDA common workflows that perform automatic input parameter selection, provide identical input/output interfaces across codes, and ensure full reproducibility. Finally, we discuss the extent to which the current results for total energies can be reused for different goals (e.g., obtaining formation energies). This data entry contains all data to reproduce the results, as well as the resulting curated all-electron dataset and the scripts to generate the figures of the paper.

Tags: automation, dft, equation-of-state, marvelp3, pseudopotentials, verification
Entanglement between a muon spin and I>1/2 nuclear spins

We report on the first example of quantum coherence between the spins of muons and quadrupolar nuclei. We observe this effect in vanadium intermetallic compounds which adopt the A15 crystal structure, and whose members include all technologically dominant superconductors. The entangled states are extremely sensitive to the local structural and electronic environments through the electric field gradient at the quadrupolar nuclei. This case-study demonstrates that positive muons can be used as a quantum sensing tool to probe also structural and charge related phenomena in materials, even in the absence of magnetic order. The data here contained can be used to reproduce all results and graphs shown in the article.

Tags: density-functional-theory, muon-spin-rotation-relaxation-spectroscopy, quantum-coherence
Bias free multiobjective active learning for materials design and discovery

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. 
In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. 
We apply our algorithm to de novo polymer design with a prohibitively large search space.
Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search.
This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.

Tags: active-learning, erc, machine-learning, marvel, multiobjective, polymers, snsf
Revealing the structure and oxygen transport at interface in complex oxide heterostructures via 17O NMR spectroscopy

Vertically aligned nanocomposite (VAN) films, comprising nanopillars of one phase embedded in a matrix of another, have shown great promise for a range of applications due to their high interfacial areas oriented perpendicular to the substrate. In particular, oxide VANs show enhanced oxide-ion conductivity in directions that are orthogonal to those found in more conventional thin-film heterostructures, however the structure of the interfaces and its influence on conductivity remain unclear. In this work 17O NMR spectroscopy is used to study CeO2–SrTiO3 VAN thin films: selective isotopic enrichment is combined with a lift-off technique to remove the substrate, facilitating detection of the 17O NMR signal from single atomic layer interfaces. By performing the isotopic enrichment at variable temperatures, the superior oxide-ion conductivity of the VAN films compared to the bulk materials is shown to arise from enhanced oxygen mobility at this interface; oxygen motion at the interface is further identified from 17O relaxometry experiments. The structure of this interface is solved by calculating the NMR parameters using density functional theory combined with random structure searching, allowing the chemistry underpinning the enhanced oxide-ion transport to be proposed. Finally, a comparison is made with 1% Gd-doped CeO2–SrTiO3 VAN films, for which greater NMR signal can be obtained due to paramagnetic relaxation enhancement, while the relative oxide-ion conductivities of the phases remain similar. These results highlight the information that can be obtained on interfacial structure and dynamics with solid-state NMR spectroscopy, in this and other nanostructured systems, our methodology being generally applicable to overcome sensitivity limitations in thin-film studies.
The dataset contains the Experimental data, first-principles calculations and random structures searching results.

Tags: erc, interface, oxide, solid-state-nmr, thin-films
Ultrafast electron localization in the EuNi2(Si0.21Ge0.79)2 correlated metal

Ultrafast electron delocalization induced by a femtosecond laser pulse is a well-known process in which electrons are ejected from the ions within the laser pulse duration. However, very little is known about the speed of electron localization out of an electron gas in correlated metals, i.e., the capture of an electron by an ion. Here, we demonstrate by means of pump-probe x-ray techniques across the Eu L3 absorption edge that an electron localization process in the EuNi2(Si0.21Ge0.79)2 intermetallic material occurs within a few hundred femtoseconds after the optical excitation. Spectroscopy and diffraction data collected simultaneously at low temperature and for various laser fluences show that the localization dynamics process is much faster than the thermal expansion of the unit cell along the c direction which occurs within picoseconds. Nevertheless, this latter process is still much slower than pure electronic effects, such as screening, and the subpicosecond timescale indicates an optical phonon drive origin. In addition, comparing the laser fluence dependence of the electronic response with that found in other intermediate 4 f valence materials, we suggest that the electron localization process observed in this Eu-based correlated metal is mainly related to changes in the 4 f hybridization. The observed ultrafast electron localization process sparks fundamental questions for our understanding of electron correlations and their coupling to the lattice.

Tags: experimental, pump-and-probe-x-ray-techniques, ultrafast-electron-delocalization, valence-transition
Assessing the persistence of chalcogen bonds in solution with neural network potentials

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.

Tags: chalcogen-bonding-in-solution, epfl, erc, machine-learning, molecular-dynamics, neural-network-potentials, non-covalent-interactions, snsf
Neural networks-based variationally enhanced sampling

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.

Tags: biomolecules, crystallization, deep-learning, enhanced-sampling, marvel, marveldd1, plumed, rare-events, silicon
Rethinking Metadynamics

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.

Tags: enhanced-sampling, erc, free-energy, marveldd1, umbrella-sampling
Electron density learning of non-covalent systems

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.

Tags: electron-density, machine-learning, marveldd1, non-covalent-systems
Rethinking Metadynamics

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.

Tags: enhanced-sampling, free-energy, marveldd1, umbrella-sampling
Mechanism and control parameters of the coupled structural and metal-insulator transition in nickelates

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.

Tags: dftdmft, electronic-structure, nickelates
Synthesis and Characterization of π-Extended Triangulene

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.

Tags: marveldd3, nanographenes, non-kekule, on-surface-synthesis, triangulene
Influence of an external electric field on the potential energy surface of alkali decorated C60

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.

Tags: alkali-atom, c60, electric-field, free-energy, marveldd1, snsf
Electron transport through metal/MoS2 interfaces: edge- or area-dependent process?

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.

Tags: 2-d-materials, ab-initio-device-simulations, contact-physics, fermi-level-pinning, marveldd3, metal-semiconductor-interfaces, transfer-length
Evidence for carbon clusters present near thermal gate oxides affecting the electronic band structure in SiC-MOSFET

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.

Tags: 4h-sic, marveldd1, microfabrication, mosfet, snsf
Ab initio simulation of band-to-band tunneling FETs with single- and few-layer 2-D materials as channels

Full-band atomistic quantum transport simulations based on first principles are employed to assess the potential of band-to-band tunneling field-effect-transistors (TFETs) with a 2-D channel material as future electronic circuit components. We demonstrate that single layer transition metal dichalcogenides (TMDs) are not well-suited for TFET applications. There might, however, exist a great variety of 2-D semiconductors that have not even been exfoliated yet: this work pinpoints some of the most promising candidates among them to realize highly efficient TFETs. Single-layer SnTe, As, TiNBr, and Bi are all found to ideally deliver ON-currents larger than 100 μA/μm at 0.5 V supply voltage and 0.1 nA/μm OFF current value. We show that going from single to multiple layers can boost the TFET performance as long as the gain from a narrowing band gap exceeds the loss from the deteriorating gate control. Finally, a 2-D van der Waals heterojunction TFET is revealed to perform almost as well as the best single-layer homojunction, paving the way for research in optimal 2-D material combinations.

Tags: 2-d-materials, ab-initio, device-simulation, marveldd3, quantum-transport, tfets