AndreaBozzo/ceres-open-data-index huggingface dataset:
Understanding long-lived metastable phases in ultrafast optical experiments

Experiments involving resonant optical excitation of infrared-active phonons in crystals have emerged as a powerful new way to tune materials properties. A puzzling and so far unexplained aspect of some so-called nonlinear phononics experiments is that the observed lifetimes of the optically created metastable phases are sometimes orders of magnitude longer than expected based on the nonlinear phononics mechanism assumed in most works. We use a combination of phenomenological theory and first-principles calculations to demonstrate that strong coupling between different lattice degrees of freedom (strains and Raman-active phonons) can give rise to a long-lived metastable phase recently observed in experiments on perovskite LaAlO3 [Hortensius et al. npj Quantum Mater. 5 95 (2020)]. We show that the long-timescale oscillatory response in the experimental optical reflectivity data is not due solely to shear strains, as originally suggested, but arises from a “hybrid” mode involving displacements of Raman-active phonons of the same symmetry. Our work suggests that strong coupling between different order parameters can provide a mechanism for long-lived optically created metastable phases and points towards strategies, such as strain engineering, for modifying or increasing the lifetime of light-induced phases in ultrafast optical experiments.

This archive contains the data files used to produce Figures 2-4 of the described paper.

Tags: complex-oxides, first-principles, ultrafast-optical-experiments-and-theory
Neutral supercells for charged impurities by explicit acceptor/donor compensation - defects in diamond

To investigate charged defects in gapped materials by first-principles calculations, they must be charged by either adding/removing electrons or compensating donors/acceptors. The former approach is more common, but it is not without drawbacks. We tested the latter method for a collection of model systems consisting of charge-compensated point defects in diamond (NV/SiV-centers, substitutional nitrogen/phosphorous/oxygen/sulfur donors, and substitutional boron/beryllium acceptors), comparing the geometrical and electronic properties of the compensated defect pairs with those of the individual defects in charged supercells. We find that the charging by explicit donors/acceptors works well and can be advantageous if properly designed, although interpretation of the results can be challenging. In this archive, we share the final optimized geometries of all studied structural models (in CIF format).

Tags: compensating-defect, jellium-counter-charge, nv-center, siv-center
Database of scalable training of neural network potentials for complex interfaces through data augmentation

This database contains the reference data used for direct force training of Artificial Neural Network (ANN) interatomic potentials using the atomic energy network (ænet) and ænet-PyTorch packages (https://github.com/atomisticnet/aenet-PyTorch). It also includes the GPR-augmented data used for indirect force training via Gaussian Process Regression (GPR) surrogate models using the ænet-GPR package (https://github.com/atomisticnet/aenet-gpr).
Each data file contains atomic structures, energies, and atomic forces in XCrySDen Structure Format (XSF). The dataset includes all reference training/test data and corresponding GPR-augmented data used in the four benchmark examples presented in the reference paper, "Scalable Training of Neural Network Potentials for Complex Interfaces Through Data Augmentation".
A hierarchy of the dataset is described in the README.txt file, and an overview of the dataset is also summarized in supplementary Table S1 of the reference paper.

Tags: aenet, ann, first-principles, gpr, li-metal-battery, machine-learning, potential-energy-surface
Reconstructions and dynamics of 𝛽 -lithium thiophosphate surfaces

Lithium thiophosphate (LPS) has demonstrated promising properties for use as a solid electrolyte for the next-generation of lithium ion batteries. However, the high reactivity of LPS with common contaminants such as atmospheric water hinders commercialization of the cells. We employ a machine learning interatomic potential to gain fundamental, atomistic understanding on the mechanical, chemical and electronic properties of an LPS surface. Our focus lies first on the identification of relevant surface complexions formed by surface reconstructions, which differ greatly in properties from the bulk and define the form of the reactive sites. Then, we reveal the complex reactivity of the LPS surface with water and identify the surface moieties that play a relevant role in the material’s degradation. Thus, we uncover the impact of dynamical changes of the surfaces and their electronic structure on the material’s reactivity.This dataset includes all files necessary to reproduce the data reported in Reconstructions and dynamics of 𝛽 -lithium thiophosphate surfaces.This includes the MLIP model and dataset, generated surface structures with certain Miller indices, Wulff constructions, as well the input and result files of LAMMPS and DFT calculations.

Tags: deutsche-forschungsgemeinschaft, lps, machine-learning, machine-learning-interatomic-potentials, marvel, marvelp2, molecular-dynamics, smoothsoap, solid-electrolyte, surface-complexion
Machine learning-driven analytical models for threshold displacement energy prediction in materials

Understanding the behavior of materials under irradiation is crucial for the design and safety of nuclear reactors, spacecraft, and other radiation environments. The threshold displacement energy (Ed) is a critical parameter for understanding radiation damage in materials, yet its determination often relies on costly experiments or simulations.This work presents a compilation of threshold displacement energies (Ed) and fundamental material parameters (e.g., density, atomic mass, melting temperature) designed to enable the application of the machine learning-based Sure Independence Screening and Sparsifying Operator (SISSO) method. The goal is to develop accurate, analytical models for predicting Ed based on intrinsic material properties. The models outperform traditional approaches for monoatomic materials, capturing key trends with high accuracy. While predictions for polyatomic materials highlight challenges due to dataset complexity, they reveal opportunities for improvement with expanded data. This study identifies cohesive energy and melting temperature as key factors influencing Ed, offering a robust framework for efficient, data-driven predictions of radiation damage in diverse materials.

Tags: materials, radiation-damage, threshold-displacement-energy
Design of high-mobility p-type GaN via the piezomobility tensor

Gallium nitride (GaN) is a wide-bandgap semiconductor of significant interest for applications in solid-state lighting, power electronics, and radio-frequency amplifiers. An important limitation of this semiconductor is its low intrinsic hole mobility, which hinders the development of p-channel devices and the large-scale integration of GaN CMOS in next-generation electronics. Prior research has explored the use of strain to improve the hole mobility of GaN, but a systematic analysis of all possible strain conditions and their impact on the mobility is lacking. In this study, we introduce a piezomobility tensor notation to characterize the relationship between applied strain and hole mobility in GaN. To map the strain-dependence of the hole mobility, we solve the ab initio Boltzmann transport equation, accounting for electron-phonon scattering and GW quasiparticle energy corrections.

Tags: electron-phonon-coupling, hole-mobility, nitrides
Strained alloy microstructure dataset for ML property prediction and time-evolution simulation

Spinodal decomposition is a phenomenon involving spontaneous phase separation in a solid alloy. It is often considered as the prototype of second order phase transitions and is known for the formation of non-trivial patterns. In the presence of a lattice mismatch f between the components, even more complex, qualitatively different patterns emerge, depending on the specific f values. Despite being interesting in practical and theoretical settings, modeling this class of phenomena may be hindered by computational costs. Lately, applications of Machine Learning (ML) promise to mitigate these issues. The availability of suitable datasets is therefore of primary importance for the development of said models. We here provide a collection of phase field simulations of spinodal decomposition involving elastic effects under different mismatch conditions f. These may be used in training a ML model both for the forward problem of predicting the evolution given an initial condition and for the inverse problem of extracting the misfit parameter from a sequence. The dataset is conveniently already divided into training, validation and test sets. The data also support the ML framework, based on a convolutional recurrent scheme, discussed in the related publication in refrences and allows for the full reproduction of the reported findings. Once trained, the neural network is able to accurately reproduce ground-truth evolution even in critical regions of the parameter space (e.g., near the onset of metastability) and predict the misfit parameters to a high degree of accuracy (~0.01% absolute strain).

Tags: alloy-microstructures, convolutional-neural-network, elastic-field, lattice-mismatch, machine-learning, phase-field-simulations, recurrent-neural-network, spinodal-decomposition, surrogate-modeling
Thermal transport in amorphous carbon nanotubes

Thermal transport in low-dimensional materials is of great fundamental and applied interest due to their unusual and widely tunable properties, as demonstrated by numerous studies of two- and one-dimensional (1D) crystals. In grim contrast, low-dimensional amorphous materials remain largely unexplored, despite their potentially unique characteristics and applications. Here, we theoretically explore thermal transport in quasi-1D single-walled amorphous carbon nanotubes (a-CNTs) by combining homogeneous nonequilibrium molecular dynamics and lattice dynamics, based on a custom-trained high-accuracy machine-learned potential. For both zigzag and armchair a-CNTs of different diameters, the quantum-corrected thermal conductivity (κ) consistently drops by about an order of magnitude as the degree of disorder increases from 0.01 to 0.08, which is quantified by the concentration of Stone-Wales defects. Spectral analysis reveals the predominant role of the low-frequency (<3 THz) vibrational modes, the mean free paths (MFPs) of which are long but finite, thus leading to converged κ as the tube length reaches several micrometers. Further, lattice dynamics calculations yield much larger MFPs for the longitudinal acoustic modes compared to the transverse acoustic modes, which is primarily attributed to the distinctly higher group velocity of the former. Moreover, the κ suppression appears to be dictated by the structural disorder while anharmonicity barely contributes. Our work expands the understanding of thermal transport in low-dimensional amorphous materials and may help promote their eventual application. This record contains data and scripts that support the findings of this article.

Tags: amorphous-carbon-nanotube, lattice-dynamics, machine-learned-potential, molecular-dynamics, thermal-transport
How salt solvation slows water dynamics while blue-shifting its dielectric spectrum

Water inherently contains trace amounts of various salts, yet the microscopic processes by which salts influence some of its physical properties remain elusive. Notably, the mechanisms that reduce the dielectric constant of water upon salt addition are still debated. The primary absorption peak for electromagnetic radiation-commonly used in microwave heating-shifts towards higher frequencies in saline solutions, suggesting faster water molecular dynamics. This observation, however, contrasts with the simultaneous increase in viscosity and experimental reports that ionic solutes would slow down water molecular motion. In this work, we use molecular dynamics (MD) simulations with deep-neural-network models trained on high-quality quantum mechanical data to mimic interatomic forces and molecular dipoles, to compute the dielectric spectra of perchlorate water saline solution, which may be relevant to the recent discovery of liquid water beneath the thick ice crust at Mars’s south pole. Our results reveal that both the reduction in the dielectric constant and the absorption peak shift can be attributed to ion-induced changes in the orientational ordering of water molecules. Additionally, we demonstrate that the self-part of the molecular dipole-dipole correlation function reveals clear signatures of the slowing dynamics within the first cationic solvation shell, consistent with the experimentally observed increase in viscosity.

This record contains the DeepMD neural network potential and the underlying training data as well as the neural network for prediciting Wannier centroids and the underlying training data.

Tags: deepmd, max, molecular-dynamics-simulation, neural-network-potential, salt, water
GapExtractor v2.0

GapExtractor v2.0© is a free software designed to determine the band gap energy of non-translucent samples by analyzing their diffuse reflectance spectra. The analysis is based on the Tauc-method and the Kubelka and Munk formalism. The algorithm is coded in the Python 3.11.5 programming language. By coping, running or distributing this software, you indicate your acceptance of the license terms.

Steps to reproduce: Unzip GapExtractor v2.0 file; run gapextractor_v2.0.py; check the required phyton libraries; install if necessary.

Tags: band-gap-energy, diffuse-reflectance-spectroscopy, polyphasic-samples, tauc-method
HTA - An open-source software for assigning heads and tails to SMILES in polymerization reactions

Polymers are versatile materials with a wide range of applications. The improvement of polymer properties rises the importance on the way that the repeating units are connected (head-to-tail,head-to-head,tail-to-tail) to build the polymer structure since it directly influences the morphology, chain topology and consequently its properties. Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and machine learning (ML) tools are being used in the efficient development, design and discovery of polymer. One key enabling factor for the essential foundations for Polymer Informatics is the machine-readable polymer representation. Polymer have been represented in a string format with special characters used to tag the head and tail positions indicating where the linking bond happens between repeat units. Available tools to assign the head and tail position limits its applicability in a broad sense. In this work we show a new tool to assign the head and tail atoms for a given monomer. From a database of 206 polymer precursors curated from the literature, our algorithm correctly predicted the class of 201 data points, which represents 97.6% of accuracy and regarding the the head and tail assignment, correctly assigned the positions for 188 data points, which translates to 91.3% of accuracy.

Tags: head-and-tail-atom-position, polymer, repeat-unit
Fluctuations and correlations of local topological order parameters in disordered two-dimensional topological insulators

Two-dimensional topological insulators are characterized by an insulating bulk and conductive edge states protected by the nontrivial topology of the bulk electronic structure. They remain robust against moderate disorder until Anderson localization occurs and destroys the topological phase. Interestingly, disorder can also induce a topological phase—known as a topological Anderson insulator—starting from an otherwise pristine trivial phase. While topological invariants are generally regarded as global quantities, we argue that space-resolved topological markers can act as local order parameters, revealing the role of fluctuations and correlations in the local topology under Anderson disorder and vacancies. With this perspective, we perform numerical simulations of disorder-driven topological phase transitions in the Haldane and Kane-Mele models, using supercells with both open and periodic boundary conditions. We find that short-scale fluctuations of topological markers vanish upon coarse-graining, except at the topological phase transition, where their correlation length peaks and large-scale fluctuations remain. Notably, such a topological correlation function is characterized by critical exponents that appear universal across disorder types, yet they can resolve different topological phase transitions.

Tags: anderson-disorder, critical-exponents, fluctuations-and-correlations, haldane-model, kane-mele-model, local-topology, topological-order-parameters, vacancies
Engineering PdAu/CeO2 alloy/oxide interfaces for selective methane-to-methanol conversion with water

The direct conversion of methane-to-methanol remains a critical challenge in methane valorization. In this study, we unveil the crucial role of PdAu/CeO2 catalysts in enabling selective methane transformation under mild conditions, using only water as the sole oxidant. Through a combination of experimental techniques, including XPS and catalytic testing, alongside density functional theory (DFT) calculations, we demonstrate that a Pd0.3Au0.7/CeO2 catalyst, which predominantly exposes isolated Pd atoms, achieves remarkable methanol selectivity (∼80%) at 500 K with a 1:1 methaneto-water ratio. While Pd/CeO2 efficiently activates methane, its tendency for overreaction leads to complete methanol decomposition, thereby limiting selectivity. Alloying Pd withAu on ceria mitigates this over-reactivity, preventing methanol degradation while maintaining sufficient catalytic activity. The PdAu/CeO2 composite exhibits a synergistic effect: Pd in contact with the ceria support facilitates methane activation and water dissociation, while Au fine-tunes reactivity to promote methanol formation. DFT calculations confirm that isolated Pd sites at the PdAu/CeO2 interface play a key role in balancing activity and selectivity. This work underscores the importance of alloy/oxide interfaces in controlling selective methane conversion with water and offers valuable insights for designing highly efficient catalysts for methanol synthesis.

Tags: alloy, bimetallic, ceo2, ceria, ch3oh, ch4, computational-chemistry, dft, gofee, gold, heterogeneous-catalysis, methane-to-methanol, nanoparticle, palladium
Robust Wannierization including magnetization and spin-orbit coupling via projectability disentanglement

Maximally-localized Wannier functions (MLWFs) are widely employed as an essential tool for calculating the physical properties of materials due to their localized nature and computational efficiency. Projectability-disentangled Wannier functions (PDWFs) have recently emerged as a reliable and efficient approach for automatically constructing MLWFs that span both occupied and lowest unoccupied bands. Here, we extend the applicability of PDWFs to magnetic systems and/or those including spin-orbit coupling, and implement such extensions in automated workflows. Furthermore, we enhance the robustness and reliability of constructing PDWFs by defining an extended protocol that automatically expands the projectors manifold, when required, by introducing additional appropriate hydrogenic atomic orbitals. We benchmark our extended protocol on a set of 200 chemically diverse materials, as well as on the 40 systems with the largest band distance obtained with the standard PDWF approach, showing that on our test set the present approach delivers a 100% success rate in obtaining accurate Wannier-function interpolations, i.e., an average band distance below 15meV between the DFT and Wannier-interpolated bands, up to 2 eV above the Fermi level.

Tags: aiida, automation, marvel, projectability-disentanglement, snsf, wannier-functions, wannierization
Variational first-principles approach to self-trapped polarons

The behavior of charge carriers in polar materials is governed by electron-phonon interactions, which affect their mobilities via phonon scattering and may localize carriers into self-induced deformation fields, forming self-trapped polarons. We present a first-principles study of self-trapped polaron formation in paradigmatic polar semiconductors and insulators using the variational polaron equations framework and self-consistent gradient optimization. Our method incorporates long-range corrections to the electron-phonon interaction, essential for finite-size systems. We demonstrate how the variational approach enables the identification of multiple polaronic states and supports the analysis of polarons with arbitrarily large spatial extent via energy filtering. The potential energy surfaces of the resulting polarons exhibit multiple local minima, reflecting distinct, symmetry-broken polaronic configurations in systems with degenerate band edges. Our findings align with previous theoretical studies and establish a robust foundation for future ab initio studies of polarons, especially those employing variational methods.This archive contains the data necessary to reproduce the results of the "Variational first-principles approach to self-trapped polarons" article by Vasilii Vasilchenko, Matteo Giantomassi, Samuel Poncé, and Xavier Gonze.

Tags: electron-phonon-coupling, first-principles-calculations, polarons, variational-wave-functional-methods
A salt-free medium facilitating electrode prelithiation towards fast-charging, high-energy lithium-ion batteries

The substantial consumption of lithium (Li) ions and sluggish reaction kinetics at the anode detrimentally impact the deliverable energy and fast-charging capability of lithium-ion batteries (LIBs) with silicon-based anodes. The prevailing contact prelithiation method using an electrolyte medium can replenish the active Li, but it may cause materials/electrode instability and bring barrier for Li-ion transport due to the nonuniform reaction. Here we explore a contact prelithiation methodology employing cyclic carbonate mediums that can enable spatially and temporally uniform prelithiation reaction. These mediums enable a delicate equilibrium between a Li-ion diffusion and the intrinsic prelithiation reaction rate of the material throughout the electrode depth. To gain an in-depth insight into the mechanism for producing Li-ions under the contact between Li metal and cyclic carbonate solvents, we undertook molecular dynamics simulations. Our calculation results of the self-produced Li alkyl carbonates reveal that while the long-chain Li alkyl carbonates are capable of forming an extensive polymer network in FEC and participating in the formation of a stable SEI on the Li metal surface, short-chain Li alkyl carbonates can dissolve into the solvent to produce Li-ions.

Tags: battery, lithium, molecular-dynamics