AndreaBozzo/ceres-open-data-index huggingface dataset:
Four- and twelve-band low-energy symmetric Hamiltonians and Hubbard parameters for twisted bilayer graphene using ab-initio input

A computationally efficient workflow for obtaining low-energy tight-binding Hamiltonians for twisted bilayer graphene, obeying both crystal and time-reversal symmetries is presented in this work. The Hamiltonians at the first magic angle are generated using the Slater-Koster approach with parameters obtained by a fit to ab-initio data at larger angles. Low-energy symmetric four-band and twelve-band Hamiltonians are constructed using the Wannier90 software. The advantage of our scheme is that the low-energy Hamiltonians are purely real and are obtained with the maximum-localization procedure to reduce the spread of the basis functions. Finally, we compute extended Hubbard parameters for both models within the constrained random phase approximation (cRPA) for screening, which again respect the symmetries. The workflow is straightforwardly transferable to other twisted multi-layer materials.

Tags: 2d-materials, crpa, erc, hubbard-parameters, magic-angle, marveldd6, slater-koster, tight-binding, twisted-bilayer-graphene, wannierization
A microscopic picture of paraelectric perovskites from structural prototypes

This work details how to determine structural prototypes for the cubic perovskite structure that are used to study the B-site displacements in the cubic, paraelectric phase. Car-Parrinello MD simulations of cubic barium titanate (BaTiO3) show the titanium displacements from the undistorted cubic structure. Using a systematic symmetry analysis we construct microscopic templates, i.e. representative structural models in the form of supercells that satisfy a desired point symmetry but are built from the combination of lower-symmetry primitive cells. Density functional theory calculations, using the microscopic templates as starting structures for a relaxation, are carried out to find structural prototypes of BaTiO3 with local polar distortions but with cubic point symmetry. The stability of these structures is studied as a function of volume and with respect to the zone-boundary phonons of pristine cubic BaTiO3. The stable distortions patterns for BaTiO3 are investigated for other titanates and for a handful of niobates and zirconates.

Tags: group-subgroup-relations, marvelosp, max, paraelectricity, perovskite
Effects of the density and composition on the properties of amorphous alumina: a high dimensional neural network potential study

Amorphous alumina (a-AlOx), which plays important roles in several technological fields, shows wide variation of the density and composition. However, their influences on the properties of a-AlOx have rarely been investigated from a theoretical perspective. In this study, high dimensional neural network (NN) potentials were constructed to generate a series of atomic structures of a-AlOx with different densities (2.6–3.3 g/cm3) and O/Al ratios (1.0–1.75). The structural, vibrational, mechanical, and thermal properties of the a-AlOx models were investigated, as well as the Li and Cu diffusion behaviour in the models. The results showed that the density and composition had different degrees of effects on the different properties. The structural and vibrational properties were strongly affected by the composition, whereas the mechanical properties were mainly determined by the density. The thermal conductivity was affected by both the density and composition of a-AlOx. However, the effects on the Li and Cu diffusion behaviour were relatively unclear.

Tags: aluminum-oxide, amorphous, neural-network-potential
Observation of a singular Weyl point surrounded by charged nodal walls in PtGa

This record contains all the raw data in the paper Nature Communications volume 12, Article number: 3994 (2021). Constrained by the Nielsen-Ninomiya no-go theorem, in all so-far experimentally determined Weyl semimetals (WSMs) the Weyl points (WPs) always appear in pairs in the momentum space with no exception. As a consequence, Fermi arcs occur on surfaces which connect the projections of the WPs with opposite chiral charges. However, this situation can be circumvented in the case of unpaired WP, without relevant surface Fermi arc connecting its surface projection, appearing singularly, while its Berry curvature field is absorbed by nontrivial charged nodal walls. Here, combining angle-resolved photoemission spectroscopy with density functional theory calculations, we show experimentally that a singular Weyl point emerges in PtGa at the center of the Brillouin zone (BZ), which is surrounded by closed Weyl nodal walls located at the BZ boundaries and there is no Fermi arc connecting its surface projection. Our results reveal that nontrivial band crossings of different dimensionalities can emerge concomitantly in condensed matter, while their coexistence ensures the net topological charge of different dimensional topological objects to be zero. Our observation extends the applicable range of the original Nielsen-Ninomiya no-go theorem which was derived from zero dimensional paired WPs with opposite chirality. The ARPES data, calculated band structure, and related documents are attached in this record.

Tags: unpaired-weyl-points, weyl-nodal-plane, weyl-nodal-surface
Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture

Data includes the the ab initio molecular dynamic simulation of Li7P3S11 that was used to measure the performance of the GNNFF. The data is divided into training and testing sets.

Brief descirption of the work: Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

Tags: density-functional-theory, machine-learning, molecular-dynamics
Band gaps of liquid water and hexagonal ice through advanced electronic-structure calculations

The fundamental band gaps of liquid water and hexagonal ice are calculated through advanced electronic-structure methods. We compare specifically the performance of state-of-the-art GW calculations with nonempirical hybrid functionals. For the latter, we fix the free parameters either through the dielectric response of the material or through enforcing Koopmans' condition to localized states. The various approaches yield consistent band gaps, in good agreement with available experimental references. Furthermore, we discuss the critical aspects of each approach that underlie the band-gap predictions.

Tags: band-gap, epfl, first-principles, hexagonal-ice, liquid-water, nonempirical-hybrid-functionals, snsf
Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations

We study the phase equilibrium between liquid water and ice Ih modeled by the TIP4P/Ice interatomic potential using enhanced sampling molecular dynamics simulations. Our approach is based on the calculation of ice Ih-liquid free energy differences from simulations that visit reversibly both phases. The reversible interconversion is achieved by introducing a static bias potential as a function of an order parameter. The order parameter was tailored to crystallize the hexagonal diamond structure of oxygen in ice Ih. We analyze the effect of the system size on the ice Ih-liquid free energy differences, and we obtain a melting temperature of 270 K in the thermodynamic limit. This result is in agreement with estimates from thermodynamic integration (272 K) and coexistence simulations (270 K). Since the order parameter does not include information about the coordinates of the protons, the spontaneously formed solid configurations contain proton disorder as expected for ice Ih.

Tags: enhanced-sampling, free-energy, ice, proton-disorder, snsf, water
Learning on-top: regressing the on-top pair density for real-space visualization of electron correlation

The on-top pair density [Π(r)] is a local quantum chemical property, which reflects the probability of two electrons of any spin to occupy the same position in space. Simplest quantity related to the two-particles density matrix, the on-top pair density is a powerful indicator of electron correlation effects and, as such, it has been extensively used to combine density functional theory and multireference wavefunction theory. The widespread application of Π(r) is currently hindered by the need for post-Hartree-Fock or multireference computations for its accurate evaluation. In this work, we propose the construction of a machine learning model capable of predicting the CASSCF-quality on-top pair density of a molecule only from its structure and composition. Our model, trained on the GDB11-AD-3165 database, is able to predict with minimal error the on-top pair density of organic molecules bypassing completely the need for ab-initio computations. The accuracy of the regression is demonstrated using the on-top ratio as a visual metric of electron correlation effects and bond-breaking in real-space. In addition, we report the construction of a specialized basis set, built to fit the on-top pair density in a single, atom-centered expansion. This basis, cornerstone of the regression, could be potentially used also in the same spirit of the resolution-of-the-identity approximation for the electron density.

Tags: epfl, erc, machine-learning, marveldd1, on-top-pair-density, snsf, strong-electron-correlation
On-surface synthesis of oligo(indenoindene)

In this record we provide data to support our recent work on the synthesis of oligo(indenoindene). Fully conjugated ladder polymers (CLP) possess unique optical and electronic properties and are considered promising materials for applications in (opto)electronic devices. Poly(indenoindene) is a CLP consisting of an alternating array of five- and six-membered rings, which has remained elusive so far. Our results relate to on-surface synthesis of oligo(indenoindene) on Au(111). Its structure and a low electronic band gap have been elucidated by low-temperature scanning tunneling microscopy and spectroscopy and noncontact atomic force microscopy, complemented by density functional theory calculations. Achieving defect-free segments of oligo(indenoindene) offers exclusive insight into this CLP and provides the basis to further synthetic approaches.

Tags: alkyls, cscs, marveldd3, oligomers, polymers, snsf
Biomimetic high performance artificial muscle built on sacrificial coordination network and mechanical training process

Artificial muscle materials promise incredible applications in actuators, robotics and medical apparatus, yet the ability to mimic the full characteristics of skeletal muscles into synthetic materials remains a huge challenge. Herein, inspired by the dynamic sacrificial bonds in biomaterials and the self-strengthening of skeletal muscles by physical exercise, high performance artificial muscle material is prepared by rearrangement of sacrificial coordination bonds in the polyolefin elastomer via a repetitive mechanical training process. Biomass lignin is incorporated as a green reinforcer for the construction of interfacial coordination bonds. The prepared artificial muscle material exhibits high actuation strain (>40%), high actuation stress (1.5 MPa) which can lift more than 10000 times its own weight with 30% strain, characteristics of excellent self-strengthening by mechanical training, strain-adaptive stiffening, and heat/electric programmable actuation performance. In this work, we show a facile strategyfor the fabrication of intelligent materials using easily available raw materials.

Tags: artificial-muscle-materials, lignin, mechanical-training, programmable-actuation, sacrificial-coordination-bonds, self-strengthening, strain-adaptive-stiffening
Efficient Training of ANN Potentials by Including Atomic Forces via Taylor Expansion and Application to Water and a Transition-Metal Oxide

This data set contains atomic structures of water clusters, bulk water and rock-salt Li8Mo2Ni7Ti7O32 in the XCrySDen [1] structure format (XSF), and total energies are included as additional meta information. The extended XSF format is compatible with the atomic energy network (aenet) package [2,3] for artificial neural network potential construction and application.  The structures were generated using ab initio molecular dynamics (AIMD) simulations performed with the Vienna Ab Initio Simulation Package (VASP) [4,5] and projector-augmented wave (PAW) [6] pseudopontentials.

For the bulk water system the revised Perdew-Burke-Ernzerhof density functional [7] with the Grimme D3 van-der-Waals correction [8] (revPBE+D3) was used. The AIMD simulations of the Li-Mo-Ni-Ti-O system employed the strongly constrained and appropriately normed (SCAN) semilocal density functional [9].  For both periodic systems, the plane-wave cutoff was 400 eV, and Gamma-point only k-point meshes were employed.  A time step of 1 fs was used for the integration of the equation of motion, and a Nosé-Hoover thermostat [10,11] was used to maintain the temperature at 400 K.

The energies and interatomic forces of the water cluster structures were calculated using the BLYP density functional [12,13] with additional Grimme D3 correction as implemented in the Turbomole software [14].

Further details can be found in the associated research article.

[1] A. Kokalj, J. Mol. Graphics Modell. 17, 176–179 (1999).
[2] N. Artrith, A. Urban, Comput. Mater. Sci. 114, 135–150 (2016).
[3] N. Artrith, A. Urban, G. Ceder, Phys. Rev. B 96, 014112 (2017).
[4] G. Kresse, J. Furthmüller, Phys. Rev. B 54, 11169–11186 (1996).
[5] Kresse, J. Furthmüller, Comput. Mater. Sci. 6, 15–50 (1996).
[6] P. E. Blöchl, Phys. Rev. B 50, 17953–17979 (1994).
[7] Y. Zhang, W. Yang, Phys. Rev. Lett. 80, 890–890 (1998).
[8] S. Grimme, J. Antony, S. Ehrlich, H. Krieg, J. Chem. Phys. 132, 154104 (2010).
[9] J. Sun, A. Ruzsinszky, J. Perdew, Phys. Rev. Lett. 115, 036402 (2015).
[10] S. Nosé, J. Chem. Phys. 81, 511–519 (1984).
[11] W. G. Hoover, Phys. Rev. A 31, 1695–1697 (1985).
[12] A. D. Becke, Phys. Rev. A 38, 3098–3100 (1988).
[13] C. Lee, W. Yang, R. G. Parr, Phys. Rev. B 37, 785–789 (1988).
[14] F. Furche, R. Ahlrichs, C. Hättig, W. Klopper, M. Sierka, F. Weigend, WIREs Comput Mol Sci 4, 91–100 (2014).

Tags: aenet, aimd, lithium-transition-metal-oxide, vasp, water
Structure determination of an amorphous drug through large-scale NMR predictions

Knowledge of the structure of amorphous solids can direct, for example, the optimization of pharmaceutical formulations, but atomic-level structure determination in amorphous molecular solids has so far not been possible. Solid-state NMR is among the most popular methods to characterize amorphous materials, and Molecular Dynamics (MD) simulations can help describe the structure of disordered materials. However, directly relating MD to NMR experiments in molecular solids has been out of reach until now because of the large size of these simulations. Here, using a machine learning model of chemical shifts, we determine the atomic-level structure of the hydrated amorphous drug AZD5718 by combining dynamic nuclear polarization-enhanced solid-state NMR experiments with predicted chemical shifts for MD simulations of large systems. From these amorphous structures we then identify H-bonding motifs and relate them to local intermolecular complex formation energies.

Tags: experimental, machine-learning, marveldd1, molecular-dynamics, nmr, snsf
On-surface activation of benzylic C-H bonds for the synthesis of pentagon-fused graphene nanoribbons

In the record we provide the inputs and outputs for the calculations that support our recent results in the synthesis of pentagon-fused graphene nanoribbons (GNRs). GNRs have potential for applications in electronic devices. A key issue, thereby, is the fine-tuning of their electronic characteristics, which can be achieved through subtle structural modifications. These are not limited to the conventional armchair, zigzag, and cove edges, but also possible through incorporation of non-hexagonal rings. On-surface synthesis enables the fabrication and visualization of GNRs with atomically precise chemical structures, but strategies for the incorporation of non-hexagonal rings have been underexplored. In the manuscript, we describe the on-surface synthesis of armchair-edged GNRs with incorporated five-membered rings through the C-H activation and cyclization of benzylic methyl groups. ortho-Tolyl-substituted dibromobianthryl was employed as the precursor monomer, and visualization of the resulting structures after annealing at 300 °C on a gold surface by high-resolution noncontact atomic force microscopy clearly revealed the formation of methylene-bridged pentagons at the GNR edges. These persisted after annealing at 340 °C, along with a few fully conjugated pentagons having singly-hydrogenated apexes. The benzylic methyl groups could also migrate or cleave-off, resulting in defects lacking the five-membered rings. Moreover, unexpected and unique structural rearrangements, including the formation of embedded heptagons, were observed, providing novel insights into the on-surface reactions en route to functional carbon nanomaterials.

Tags: ab-initio-calculations, carbon-based-nanomaterials, graphene-flagship, graphene-nanoribbons, marveldd3, on-surface-synthesis
The new perspective for the superconductor

The superconductor theory based on the electron pair is reviewed and several viewpoints are proposed. A demonstrated case reveals the speed of each electron in the electron pair at Fermi level about 1.82x10^6 m/s in Pb. However, the fastest longitudinal and transverse speeds of crystal waves in Pb at 0 K are 2.18x10^3 m/s and 1.29x10^3 m/s in [100] direction, respectively. It seems to be very hard even impossible that the mediated phonon can real-time transfer momentum and energy between two so high-speed and antiparallel-momentum electrons in the superconducting state. In this research, we focus on single electron based on the experiments of Transmission Electron Microscopy. The new fitting temperature-dependent model for the London penetration depth is proposed. This model is much better than the one- and two-gap models and matches three experimental data much well. Then it further gives the temperature-dependent effective electron mass for the Nb superconductor film. Finally, the expression for the resistivity is deduced which can explain why the resistance is almost zero in the superconductor. All these new results are obtained by using the concept of single electron.

Tags: effective-electron-mass, electron-pair, london-penetration-depth, mediated-phonon, resistivity, superconductor, temperature-dependent
Carrier lifetimes and polaronic mass enhancement in the hybrid halide perovskite CH₃NH₃PbI₃ from multiphonon Fröhlich coupling

We elucidate the nature of the electron-phonon interaction in the archetypal hybrid perovskite CH₃NH₃PbI₃ using ab initio many-body calculations and an exactly solvable model. We demonstrate that electrons and holes near the band edges primarily interact with three distinct groups of longitudinal-optical vibrations, in order of importance: the stretching of the Pb-I bond, the bending of the Pb-I-Pb bonds, and the libration of the organic cations. These polar phonons induce ultrafast intraband carrier relaxation over timescales of 6–30 fs and yield polaron effective masses 28% heavier than the bare band masses. These findings allow us to rationalize previous experimental observations and provide a key to understanding carrier dynamics in halide perovskites.

Tags: ch3nh3pbi3, electron-phonon-coupling, first-principles-calculations, perovskite, prace, semiconductors
A data-driven perspective on the colours of metal-organic frameworks

Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is used to assess the success of a synthesis. 
However, predicting the colour of a material based on its structure is challenging.
In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale, using chemically meaningful reasoning.
In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. 
For example, we show that the majority of colour assignments are subject to perceptive spread that would not comply with common printing standards. 
To remedy this, we suggest and implement an alternative way of reporting colour that is more suitable for a data-driven approach to materials science.

Tags: color, epfl, erc, lsmo, marvel, ml
Using metadynamics to build neural network potentials for reactive events: the case of urea decomposition in water

The study of chemical reactions in aqueous media is very important for its implications in several fields of science, from biology to industrial processes. However, modeling these reactions is difficult when water directly participates in the reaction, since it requires a fully quantum mechanical description of the system. Ab-initio molecular dynamics is the ideal candidate to shed light on these processes. However, its scope is limited by a high computational cost. A popular alternative is to perform molecular dynamics simulations powered by machine learning potentials, trained on an extensive set of quantum mechanical calculations. Doing so reliably for reactive processes is difficult because it requires including very many intermediate and transition state configurations. In this study we used an active learning procedure accelerated by enhanced sampling to harvest such structures and to build a neural-network potential to study the urea decomposition process in water. This allowed us to obtain the free energy profiles of this important reaction in a wide range of temperatures, to discover several novel metastable states, and improve the accuracy of the kinetic rates calculations. Furthermore, we found that the formation of the zwitterionic intermediate has the same probability of occurring via an acidic or a basic pathway, which could be the cause of the insensitivity of reaction rates to the solution pH.

Tags: erc, free-energy-surface, kinetic-rates, marveldd1, metadynamics, neural-network-potentials, urea-decomposition