AndreaBozzo/ceres-open-data-index huggingface dataset:
Confinement effects and acid strength in Zeolites

Chemical reactivity and sorption in zeolites are coupled to confinement and - to a lesser extent- to the acid strength of Brønsted acid sites (BAS). In presence of water the zeolite Brønsted acid sites eventually convert into hydronium ions. The gradual transition from zeolite Brønsted acid sites to hydronium ions conversion in zeolites of varying pore size is examined by ab initio molecular dynamics combined with enhanced sampling based on well-tempered metadynamics and a recently developed set of collective variables. While at low water content (1-2 water/BAS) the acidic protons prefer to be shared between zeolites and water, higher water contents (n>2) invariably lead to solvation of the protons within a localized water cluster adjacent to the BAS.  At low water loadings the standard free energy of the formed complexes is dominated by enthalpy and is associated with the acid strength of the BAS and the space around the site. Conversely, the entropy increases linearly with the concentration of waters in the pores, favors proton solvation and is independent of the pore size/shape.

Tags: acid-base, metadynamics, molecular-dynamics, zeolites
Structure-property maps with kernel principal covariates regression

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PCovR) is an underappreciated method that interpolates between principal component analysis and linear regression, and can be used to conveniently reveal structure-property relations in terms of simple-to-interpret, low-dimensional maps. Here we introduce a kernelized version of PCovR and a sparsified extension, and demonstrate the performance of this approach in revealing and predicting structure-property relations in chemistry and materials science, showing a variety of examples including elemental carbon, porous silicate frameworks, organic molecules, amino acid conformers, and molecular materials.

Tags: dimensionality-reduction, epfl, erc, kernel-methods, machine-learning, marveldd1, materials-science, max, snsf
The elphbolt ab initio solver for the coupled electron-phonon Boltzmann transport equations

elphbolt is a modern Fortran (2018 standard) code for efficiently solving the coupled electron-phonon Boltzmann transport equations from first principles. Using results from density functional and density functional perturbation theory as inputs, it can calculate the effect of the non-equilibrium phonons on the electronic transport (phonon drag) and non-equilibrium electrons on the phononic transport (electron drag) in a fully self-consistent manner and obeying the constraints mandated by thermodynamics. It can calculate the lattice, charge, and thermoelectric transport coefficients for the temperature gradient and electric fields, and the effect of the mutual electron-phonon drag on these transport properties. The code fully exploits the symmetries of the crystal and the transport-active window to allow the sampling of extremely fine electron and phonon wave vector meshes required for accurately capturing the drag phenomena. The coarray feature of modern Fortran, which offers native and convenient support for parallelization, is utilized. The code is compact, readable, well-documented, and extensible by design.

Tags: ab-initio, boltzmann-transport, electron-drag, electron-phonon-coupling, electron-transport, intersect, phonon-drag, phonon-transport, thermoelectricity
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks

Neural network (NN) force fields can predict potential energy surfaces with high accuracy and speed compared to electronic structure methods typically used to generate their training data. However, NN predictions are well-defined only for points close to the training domains, and may exhibit poor results during extrapolation. Uncertainty quantification methods can detect geometries for which predicted errors are high, but sampling regions of high uncertainty requires a thorough exploration of the phase space, often using expensive simulations. Our work uses automatic differentiation to sample atomistic configurations by balancing thermodynamic accessibility and uncertainty quantification without using molecular dynamics simulations. This dataset provides the atomistic data used to train the NN potentials for the ammonia, alanine dipeptide, and zeolite-molecule systems. For all materials, geometries, energies, and forces are provided. The ammonia and zeolite systems were computed using density functional theory calculations, while the alanine dipeptide dataset was generated using molecular dynamics simulations with the OPLS force field.

Tags: density-functional-theory, machine-learning, molecular-dynamics, zeolites
Aluminum alloy compositions and properties extracted from a corpus of scientific manuscripts and US patents

Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help accelerate the discovery of new alloys. We present two datasets: 1) chemical composition, and 2) mechanical properties for predominantly wrought aluminum alloys. The first dataset contains 13,358 entries on aluminum alloy compositions extracted from academic literature and US patents using text processing techniques, in addition to 93 wrought aluminum alloys which are already registered with the Aluminum Association. The second dataset contains 1,268 entries on mechanical properties for aluminum alloys, where each entry is associated with a particular wrought series designation, extracted from tables in academic literature.

Tags: aluminum, chemical-composition, materials-discovery, mechanical-properties, metals-and-alloys
Aluminum alloy compositions and properties extracted from a corpus of scientific manuscripts and US patents

Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help accelerate the discovery of new alloys. We present two datasets: 1) chemical composition, and 2) mechanical properties for predominantly wrought aluminum alloys. The first dataset contains 14,884 entries on aluminum alloy compositions extracted from academic literature and US patents using text processing techniques, including 550 wrought aluminum alloys which are already registered with the Aluminum Association. The second dataset contains 1,278 entries on mechanical properties for aluminum alloys, where each entry is associated with a particular wrought series designation, extracted from tables in academic literature.

Tags: aluminum, chemical-composition, materials-discovery, mechanical-properties, metals-and-alloys
Aluminum alloy compositions and properties extracted from a corpus of scientific manuscripts and US patents

Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help accelerate the discovery of new alloys. We present two datasets: 1) chemical composition, and 2) mechanical properties for predominantly wrought aluminum alloys. The first dataset contains 14,884 entries on aluminum alloy compositions extracted from academic literature and US patents using text processing techniques, including 550 wrought aluminum alloys which are already registered with the Aluminum Association. The second dataset contains 1,278 entries on mechanical properties for aluminum alloys, where each entry is associated with a particular wrought series designation, extracted from tables in academic literature.

Tags: aluminum, chemical-composition, experimental, materials-discovery, mechanical-properties, metals-and-alloys
The importance of reference frame for pressure at the liquid-vapour interface

This repository has the input files and a guide to recreate the data from "The importance of reference frame for pressure at the liquid-vapour interface" (https://arxiv.org/abs/2107.00499). It requires the Flowmol code to be downloaded and built from https://github.com/edwardsmith999/flowmol (it should work with the latest version but the paper was generated from commit c4a52d434053d676c0281449b0fce7112116fd54 or the persistent version linked to DOI https://doi.org/10.5281/zenodo.4639546). The included README.txt file outlines how to do this. The input files are also included on the Github repository. The summarised data is also included as a Python pickle (summary.p) with scripts to produce all plots from the paper. This data, which shows the profile going through a liquid vapour interface, can be analysed in Python.

The abstract for the article, which explains the importance of this data, is as follows: The local pressure tensor is non-unique, a fact which has generated confusion and debate in the seventy years since the seminal work by Irving Kirkwood. This non-uniqueness is normally attributed to the interaction path between molecules, especially in the interfacial-science community. In this work we reframe this discussion of non-uniqueness in terms of the location, or reference frame, used to measure the pressure. By using a general mathematical description of the liquid-vapour interface, we obtain a reference frame that moves with the interface through time, providing a new insight into the pressure. We compare this instantaneous moving reference frame with the fixed Eulerian one. Through this process, we show the requirement that normal pressure balance at the moving surface is satisfied by surface fluxes, however an additional corrective term based on surface curvature is required for the average pressure in a volume. We make the case that a focus on the path of integration is the cause of much of the confusion in the literature. Using an explicit reference frame with a more general derivation of pressure clarifies some of the issues of uniqueness in the pressure tensor and provides a pressure tensor which is defined at any instant in time and valid away from thermodynamic equilibrium.

Tags: flowmol, molecular-dynamics-simulation, python-pickle, software-input-files
Facet-dependent stability of near-surface oxygen vacancies and excess charge localization at CeO2 surfaces

To study the dependence of the relative stability of surface (VA) and subsurface (VB) oxygen vacancies with the crystal facet of CeO2, the reduced (100), (110) and (111) surfaces, with two different concentrations of vacancies, were investigated by means of density functional theory (DFT+U) calculations. The results show that the trend in the near-surface vacancy formation energies for comparable vacancy spacings, i.e. (110) < (100) < (111), does not follow that in the surface stability of the facets, i.e. (111) < (110) < (100). The results also revealed that the preference of vacancies for surface or subsurface sites, as well as the preferred location of the associated Ce3+ polarons, are facet and concentration dependent. At the higher vacancy concentration, the VA is more stable than the VB at the (110) facet whereas at the (111), it is the other way around, and at the (100) facet, both the VA and the VB have similar stability. The stability of the VA vacancies, compared to that of the VB, is accentuated as the concentration decreases. Nearest neighbor polarons to the vacant sites are only observed for the less densely packed (110) and (100) facets. These findings are rationalized in terms of the packing density of the facets, the lattice relaxation effects induced by vacancy formation and the localization of the excess charge, and the repulsive Ce3+ - Ce3+ interactions.

Tags: cerium-oxide-surfaces, excess-charge-localization, facet-dependent-stability, lattice-relaxations, oxygen-vacancies
Achieving 19% efficiency in nonfused ring electron acceptor solar cells via solubility control of donor and acceptor crystallisation

Nonfused ring electron acceptors (NFREAs) are interesting n-type near infrared (NIR) photoactive semiconductors with strong molecular absorption and easy synthetic route. However, the low backbone planarity and bulky substitution make NFREA less crystalline, which significantly retards charge transport and the formation of bicontinuous morphology in organic photovoltaic device. Donor and acceptor solubility in different solvents is studied, and the created solubility hysteresis can induce the formation of the highly crystalline donor polymer fibril to purify the NFREA phase, thus a better bicontinuous morphology with improved crystallinity. Based on these results, a general solubility hysteresis sequential condensation (SHSC) thin film fabrication methodology is established to produce highly uniform and smooth photoactive layer. The well-defined interpenetrating network morphology afforded a record efficiency of 19.02%, which is ~22% improvement comparing to conventional device fabrication. A high efficiency retention (Pr) value of 92.3% is achieved in 1 cm² device (17.28% efficiency).

Tags: experimental, large-area-device, nonfused-ring-electron-acceptor, organic-solar-cells, record-efficiency
turboMagnon - A code for the simulation of spin-wave spectra using Liouville-Lanczos approach to time-dependent density-functional perturbation theory

We introduce turboMagnon, an implementation of the Liouville-Lanczos approach to linearized time-dependent density-functional theory, designed to simulate spin-wave spectra in solid-state materials. The code is based on the noncollinear spin-polarized framework and the self-consistent inclusion of spin-orbit coupling that allow to model complex magnetic excitations. The spin susceptibility matrix is computed using the Lanczos recursion algorithm that is implemented in two flavors - the non-Hermitian and the pseudo-Hermitian one. turboMagnon is open-source software distributed under the terms of the GPL as a component of QE. As with other components, turboMagnon is optimized to run on massively parallel architectures using native mathematical libraries (LAPACK and FFTW) and a hierarchy of custom parallelization layers built on top of MPI. The effectiveness of the code is showcased by computing magnon dispersions for the CrI₃ monolayer, and the importance of the spin-orbit coupling is discussed.

Tags: cineca, inelastic-neutron-scattering, linear-response, liouville-lanczos-approach, magnons, marvel, max, quantum-espresso, spin-orbit-coupling, spin-wave-spectra, time-dependent-density-functional-perturbation-the
eQM7: a dataset for small molecules with Foster-Boys centers

The electron QM7 (eQM7) dataset is created with the purpose of training and validating polarizable (machine learning) force fields on non-equilibrium configurations of small molecules. It contains 6868 molecules with hydrogen, carbon, nitrogen and oxygen. For each molecule, 500 perturbations are constructed using normal mode sampling, torsion sampling, dimer sampling and homogeneous electric fields. Energies, forces and Foster-Boys centers are computed using density functional theory (DFT) with the PBE0 functional, Aug-cc-pVTZ basis set in the ab-initio quantum chemistry code Psi4.

Tags: density-functional-theory, foster-boys-centers, machine-learning
Superconductivity in antiperovskites

We present a comprehensive theoretical study of conventional superconductivity in cubic antiperovskites materials with composition XYZ₃ where X and Z are metals and Y is  H, B, C, N, O, and P. Our starting point are electron-phonon calculations for 384 materials performed with density-functional perturbation theory. While 40% of the materials were dynamically unstable as they exhibited imaginary frequencies, we discovered 16 compounds with Tc higher than 5 K including antiperovskites with Y=H, N, C and O. We used these results to train interpretable machine learning models to understand and further explore this family of compounds. This lead us to predict a further 44 materials with superconducting transition temperatures above 5 K, reaching a maximum of 17.8 K for PtHBe₃. Furthermore, the models give us an understanding of the mechanism of superconductivity in anti-perovskites and highlight the importance of the density of states at the Fermi level and of the mass of the Y-atom for the strength of the phonon coupling. Finally, we study in detail a few systems, uncovering some issues with previously published theoretical data. The combination of traditional approaches with interpretable machine learning turns out to be a very efficient methodology to study and systematize whole classes of materials, and is easily extendable to other families of compounds or physical properties.

Tags: anti-perovskites, electron-phonon-coupling, superconductivity
Efficient and accurate defect level modelling in monolayer MoS₂ via GW+DFT with open boundary conditions

Within the framework of many-body perturbation theory integrated with density functional theory (DFT), a novel defect-subspace projection GW method, the so-called p-GW, is proposed. By avoiding the periodic defect interference through open boundary self-energies, we show that the p-GW can efficiently and accurately describe quasi-particle correlated defect levels in two-dimensional (2D) monolayer MoS₂. By comparing two different defect states originating from sulfur vacancy and adatom to existing theoretical and experimental works, we show that our GW correction to the DFT defect levels is precisely modelled. Based on these findings, we expect that our method can provide genuine trap states for various 2D transition-metal dichalcogenide (TMD) monolayers, thus enabling the study of defect-induced effects on the device characteristics of these materials via realistic simulations.

Tags: dft, gw, marvel, negf
Crossover from Boltzmann to Wigner thermal transport in thermoelectric skutterudites

Skutterudites are crystals with a cagelike structure that can be augmented with filler atoms ("rattlers"), usually leading to a reduction in thermal conductivity that can be exploited for thermoelectric applications. Here, we leverage the recently introduced Wigner formulation of thermal transport to elucidate the microscopic physics underlying heat conduction in skutterudites, showing that filler atoms can drive a crossover from the Boltzmann to the Wigner regimes of thermal transport, i.e., from particlelike conduction to wavelike tunneling. At temperatures where the thermoelectric efficiency of skutterudites is largest, wavelike tunneling can become comparable to particlelike propagation. We define a Boltzmann deviation descriptor able to differentiate the two regimes and relate the competition between the two mechanisms to the materials' chemistry, providing a design strategy to select rattlers and identify optimal compositions for thermoelectric applications.

Tags: marvel, phonons, thermal-conductivity, thermoelectrics
Achieving 19% efficiency in nonfused ring electron acceptor solar cells via solubility hysteresis sequential condensation strategy

Nonfused ring electron acceptors (NFREAs) are interesting n-type near infrared (NIR) photoactive semiconductors with strong molecular absorption and easy synthetic route. However, the low backbone planarity and bulky substitution make NFREA less crystalline, which significantly retards charge transport and the formation of bicontinuous morphology in organic photovoltaic device. Donor and acceptor solubility in different solvents is studied, and the created solubility hysteresis can induce the formation of the highly crystalline donor polymer fibril to purify the NFREA phase, thus a better bicontinuous morphology with improved crystallinity. Based on these results, a general solubility hysteresis sequential condensation (SHSC) thin film fabrication methodology is established to produce highly uniform and smooth photoactive layer. The well-defined interpenetrating network morphology afforded a record efficiency of 19.02%, which is ~22% improvement comparing to conventional device fabrication. A high efficiency retention (Pr) value of 92.3% is achieved in 1 cm² device (17.28% efficiency).

Tags: experimental, large-area-device, nonfused-ring-electron-acceptor, organic-solar-cells, record-efficiency
Unified theory of atom-centered representations and message-passing machine-learning schemes

Data-driven schemes that associate molecular and crystal structures with their microscopic properties share the need for a concise, effective description of the arrangement of their atomic constituents. Many types of models rely on descriptions of atom-centered environments, that are associated with an atomic property or with an atomic contribution to an extensive macroscopic quantity. Frameworks in this class can be understood in terms of atom-centered density correlations (ACDC), that are used as a basis for a body-ordered, symmetry-adapted expansion of the targets. Several other schemes, that gather information on the relationship between neighboring atoms using "message-passing" ideas, cannot be directly mapped to correlations centered around a single atom. We generalize the ACDC framework to include multi-centered information, generating representations that provide a complete linear basis to regress symmetric functions of atomic coordinates, and provides a coherent foundation to systematize our understanding of both atom-centered and message-passing, invariant and equivariant machine-learning schemes.

This record contains the data and code required to reproduce the results from the corresponding paper, computing message-passing inspired machine learning features built on top of density correlation. The data used in this article is a subset of other existing datasets, which can be found online:

- methane dataset: https://archive.materialscloud.org/record/2020.105
- NaCl dataset: https://github.com/dilkins/TENSOAP/tree/ea671154b3642b4ec879a4292a4dd4399ddbdea6/example/random_nacl
- QM7 and QM9 with dipole moments: https://archive.materialscloud.org/record/2020.56

Tags: machine-learning, marveldd2, message-passing, pasc, reproducibility
Steering on-surface reactions through molecular steric hindrance and molecule-substrate van der Waals interactions

On-surface synthesis is a rapidly developing field involving chemical reactions on well-defined solid surfaces to access the synthesis of low-dimensional organic nanostructures which cannot be achieved via traditional solution chemistry. On-surface reactions critically depend on a high degree of chemoselectivity in order to achieve an optimum balance between the target structure and possible side products. In this record we provide data for the calculations that support a work that we recently published. In the published manuscript, we demonstrate the synthesis of graphene nanoribbons with a large unit cell based on steric hindrance-induced complete chemoselectivity as revealed by scanning probe microscopy measurements and density functional theory calculations. Our results disclose that combined molecule-substrate van der Waals interactions and intermolecular steric hindrance promote a selective aryl-aryl coupling, giving rise to high-quality uniform graphene nanostructures. The established coupling strategy has been used to synthesize two types of graphene nanoribbons with different edge topologies inducing a pronounced variation of the electronic energy gaps. The demonstrated chemoselectivity is representative of n-anthryl precursor molecules and may be further exploited to synthesize graphene nanoribbons with novel electronic, topological and magnetic properties with implications for electronic and spintronic applications.

Tags: aiidalab, dft, graphene-nanoribbons, marvel, on-surface-synthesis, scanning-probe-microscopy, snsf