AndreaBozzo/ceres-open-data-index huggingface dataset:
High-throughput screening for solid-state Li-ion conductors combining machine learning and first-principles calculations

We present a high-throughput computational screening for fast lithium-ion conductors aimed at identifying candidate materials for application in all-solid-state electrolytes. Beginning with more than 30,000 experimentally reported Li-containing structures drawn from the Inorganic Crystal Structure Database, the Materials Platform for Data Science (Pauling file), and the Crystallography Open Database, we apply a series of automated structural and compositional filters to obtain 1500 unique crystal structures suitable for electronic-structure calculations which yields nearly 1,000 electronic insulators. We then estimate Li-ion diffusivities for these insulating candidates using molecular dynamics simulations at multiple temperatures. To make simulations computationally feasible at this scale while preserving near first-principles fidelity, we employ a foundational machine-learned interatomic potential which is carefully fine-tuned on relevant Li-chemistries. We discuss the details of the fine-tuning strategies and data-consistency considerations required to obtain a very accurate and robust model. From the MD results, we identify three particularly promising novel oxide candidates for room-temperature solid-state electrolytes, including LiSn2(AsO4)3, LiIn(IO3)4, and LiB6S4(Cl3O4)2, which all exhibit ionic conductivity greater than 1 mS/cm at room temperature with diffusion barrier between 0.20 and 0.25 eV. We provide the full screening protocol as well as a prioritised list of materials for experimental follow-up, demonstrating the value of provenance-aware, ML-driven simulations in accelerating solid-state electrolyte discovery.

Tags: dft, first-principles, high-throughput, li-ion-conductors, molecular-dynamics, solid-state-electrolytes
Multiferroicity and 180° magnetization switching in LaFeO3 via antisite defects

Materials exhibiting coexisting and coupled ferroelectric and magnetic orders are rare. Here we show, using density functional theory calculations, that introducing FeLa antisite defects into pristine, non-ferroelectric, G-type antiferromagnetic (AFM) LaFeO3 renders the material simultaneously ferroelectric and a defect-induced local ferrimagnetic phase. We assess the thermodynamic stability of FeLa antisites and identify synthesis conditions under which they are likely to form. Nudged elastic band calculations indicate an accessible barrier for ferroelectric polarization switching. Interestingly, we observe an inverse magnetoelectric coupling in which the ferroelectric polarization controls the magnetic order. We further propose two defect-mediated magnetization-control mechanisms enabled by ferroelectric switching in LaFeO3, both involving a 180° reversal: (i) switching the FeLa antisite moments while the pristine G-type AFM order remains unchanged, and (ii) of the pristine G-type AFM order while the FeLa antisite moments are pinned by a static magnetic field. In both cases, exchange coupling between the antisite and pristine Fe sublattices enables magnetization switching, highlighting a defect-engineering route to electrically controlled magnetic states for data storage.

Tags: antisite-defect, lafeo3, multiferroic
Triple-interlocked-nanotwinned bulk magnesium alloys with exceptional strength and ageing resistance

Nanoscale twin boundaries (TBs) effectively restrict the free motion of dislocations by intersecting with other TBs to realize high strength. However, the dislocations parallel to the TBs can glide along the TBs, resulting in detwinning for low strength. Herein, we present a triple-interlocked-nanotwinned (TIT) strategy for overcoming these inherent deficiencies. An Mg-9Li alloy was used as a proof-of-concept. The treated alloy had a large volume fraction of TIT interfaces (65.7%). It not only had a high yield strength of 508.6 MPa (approximately eleven times higher than that of the TIT-free sample) but also offered unprecedented ageing resistance, with a reduction in hardness of 6% after 730 days. The martensite-like phase transformation process concurrently triggers multiple-variant twins to form TIT structures. These TIT nets can effectively prohibit dislocation motion, reduce strengthening anisotropy, and disperse localized stress during the deformation process. This strategy of phase transformation twinning open a gate to fabricate other stronger metallic or ceramic nanotwinned bulk materials.

Tags: ageing-resistance, magnesium-alloys, strength, triple-interlocked-nanotwin
A nanotwinned-alloy strategy enables fast sodium deposition dynamics

Sodium (Na) metal batteries are considered promising solutions for next-generation electrochemical energy storage because of their low costs and high energy densities. However, the slow Na dynamics result in unfavourable Na deposition and dendrite growth, which compromise cycling performance. Here we propose a nanotwinned alloy strategy prepared by high-pressure solid solution followed by Joule-heating treatment to address sluggish Na dynamics, achieving homogeneous Na deposition. By employing cost-effective Al-Si alloys for validation, Si solubility of 10 wt.% is extended through a high-pressure solid solution, and nanotwinned-Si particles, with a volume fraction of 82.7%, are subsequently formed through Joule-heating treatment. The sodiophilic nanotwinned-Si sites exhibit a high diffusion rate, which reduces the nondimensional electrochemical Damköhler number to far below 1, shifting the diffusion-controlled deposition behavior to reaction-controlled deposition. This transition facilitates spherical Na deposition and dendrite-free growth, allowing a symmetric cell to achieve stable Na plating/stripping over 5300 hours at 5 mA cm⁻² with a cumulative capacity of 13.25 Ah cm⁻². This strategy is also demonstrated in another CuAg system with nanotwinned Ag structures.The record contains data that support the findings described in the publication.

Tags: dynamics, nanotwinned-alloy, sodium-metal-battery
Multiband anisotropic superconductivity in 2H-NbSe2 revealed by ab-initio Kohn--Sham Bogoliubov--de Gennes calculations

This dataset contains the minimal set of AiiDA calculations supporting the results presented in the paper "Multiband Anisotropic Superconductivity in 2H-NbSe₂ Revealed by Ab-Initio Kohn--Sham Bogoliubov--de Gennes Calculations".

It includes the fully relativistic Kohn--Sham Bogoliubov--de Gennes (KS-BdG) calculations for pristine bulk 2H-NbSe₂ performed with the JuKKR code and the AiiDA-KKR plugin. The calculations use a single, physically transparent pairing strength λ_Nb applied only to the Nb sites and quantitatively reproduce the experimentally observed multigap density of states with coherence peaks at 1.85, 1.55, and 1.4 meV together with the ~30% momentum-dependent superconducting gap anisotropy. The archive provides the normal-state electronic structure and the self-consistent superconducting state, including all input parameters required for full reproducibility.

Tags: 2h-nbse2, aiida, aiida-kkr-ksbdg, bogoliubov-de-gennes, superconductivity
Neural surrogates for crystal growth dynamics with variable supersaturation: explicit vs. implicit conditioning

We develop an extensive dataset of Allen-Cahn simulations of crystal growth in 2D, including kinetic anisotropy to produce hexagonally faceted profiles, and a variable supersaturation parameter to control the growth rates and local faceting. A phase-field numerical approach based on a finite-difference explicit time integration scheme is used to generate such evolution sequences. Two Convolutional Recurrent Neural Network architectures are then exploited to train quantitative surrogate models reproducing such evolution sequences conditioned to the supersaturation value. The first infers the supersaturation parameter implicitly from an input mini-sequence of a few evolution frames while the second takes it as an explicit input along with a single initial frame. The predictive performances of the two models are evaluated by extensive testing on dedicated testsets. In particular, the analysis includes both the testing on a large dataset of the same kind of the training one, and on dedicated datasets with assigned values of the supersaturation parameter. While the architecture using the parameter as explicit input yields the best performance, with quantitative reproduction of the true phase-field solutions, the mini-sequence model produce satisfactory results only by taking comparatively larger dataset sizes. The generalization capabilities of the trained models are finally inspected by considering larger domains, longer evolution times and different initial coverages.

Tags: allen-cahn-equation, convolutional-recurrent-neural-network, crystal-growth, surrogate-model
From symmetry to stability: structural and electronic transformation in Cs₂KInI₆

Cs2KInI6 is a promising lead-free halide double perovskite with a calculated direct band gap of 1.94 eV, ideal for solar cell applications. Our first-principles calculations reveal that its cubic phase (Fm3̅m) is dynamically unstable. Using an accelerated machine learning approach, we identify 42 dynamically stable structures and further validate these findings using first-principles calculations on 11 of these. The most stable phase has Cmc21 symmetry with 20 atoms/unit cell. It lies 13 meV/atom above the convex hull but lacks octahedral cation coordination. The most stable perovskite-like structure has P3̅ symmetry with 10 atoms/unit cell and low octahedral connectivity. Structure-property trade-offs are highlighted, with calculated distortions generally widening the band gap, shifting it from direct to indirect, and flattening the band edges. This work showcases the synergy of genetic algorithms, machine-learned potentials, and first principles validation for discovering stable, complex materials. This archive contains all our input, output files, the data and scripts.

Tags: first-principles, halide-double-perovskite, machine-learned-interatomic-potentials, phonons, polymorphs
Solvent reverse osmosis beyond size exclusion in 2D nanochannel membranes

Solvent reverse osmosis is a pressure-driven, liquid-phase process for separating solvent mixtures, offering a potential low-energy alternative to thermal operations. Graphene oxide (GO) laminates provide tunable nanochannels to probe confined solvent transport, yet solvent–solvent separations remain underexplored due to solvation-induced structural instabilities and the small molecular sizes. Here we construct solvent-stable, supported GO nanochannel membranes that preserves integrity under pressurized solvents, and tune interlayer confinement and surface polarity via controlled chemical reduction. Across 51 solvent systems and 5 distinct nanochannels, we demonstrate that separation is governed by coupled nanoconfinement and solvent affinity, where selective interfacial association can surpass simple size-exclusion expectations. Maximum permselectivity arises from balancing channel size with retained polarity, indicating that channel shrinking alone does not optimize performance. These findings identify channel surface chemistry as a key design factor for polarity-rich solvent systems and provide a framework for rationally tailoring nanochannels for complex solvent separations.

Tags: graphene, reverse-osmosis, solventsolvent-separation
OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent

The FAIR principles have transformed how computational materials data and workflows are shared, yet existing repositories can only serve pre-computed entries — their coverage is perpetually incomplete and cannot adapt to new questions on demand. We built OptiMat Alloys, a large-language-model-powered conversational agent for multi-principal element alloy exploration, on three pillars: a living database that stores every calculation with provenance, low-barrier accessibility through a web interface that requires zero programming expertise, and built-in uncertainty quantification via cross-potential and cross-configuration validation. By coupling foundational machine-learning interatomic potentials that span nearly the entire periodic table with natural-language interaction, the agent enables targeted, on-demand computation guided by the user's own domain knowledge — extending FAIR from pre-computed repositories to on-demand knowledge generation, and making computational alloy screening accessible to any materials scientist.

Tags: fair-principle, llm-agent, materials-informatics, multi-principal-element-alloys, universal-machine-learning-potentials
Discovery of correlated electron molecular orbital materials using graph representations

Correlated electron molecular orbital (CEMO) materials host emergent electronic states built from molecular orbitals localized over clusters of transition metal ions yet have historically been discovered sporadically and generally been treated as isolated case studies. Here we establish CEMO materials as a systematically discoverable class and introduce a graph-based framework to identify, classify, and organize transition-metal cluster motifs in inorganic solids. Starting from crystal structures in the Materials Project, we construct transition metal connectivity graphs, extract cluster motifs using a bond-cutting algorithm, and determine cluster point groups, effective cluster sublattice dimensionality, and translational symmetry. Applying this approach in a high-throughput screen of 34,548 compounds yields 5,306 cluster-containing materials, including 2,627 stable or metastable compounds with isolated clusters and 984 materials featuring mixed-metal clusters. The resulting dataset reveals symmetry and element dependent trends in cluster formation. By integrating cluster classification with flat band lattice topology and battery-relevant information, we provide further relevant information to multiple scientific communities. The accompanying open dataset, Cluster Finder software, and interactive web platform enable systematic exploration of cluster driven electronic phenomena and establish a general pathway for discovering correlated quantum materials and functional materials with cluster-based or extended metal-metal bonding in inorganic solids.

Tags: battery-materials, correlated-electron-materials, flat-band, materials-informatics
Characteristic data of activation of crystalline and amorphous OER catalysts with bulk and surface Fe incorporation.

This electrochemical, X-ray diffraction, and operando X-ray abosorption spectroscopy data characterize and decouple the effects of bulk vs. electrolyte Fe incorporation in crystalline, defective crystalline, and amorphous catalysts derived from the same CoSn(OH)₆ precursor. Electrolyte Fe incorporation equalizes the OER activity across all three structural variants, demonstrating that surface Fe chemistry can dominate initial structural effects on the reconstructed catalyst interface.

Tags: catalyst, cobalt, diffraction, electrocatalysis, iron, local-structure, operando, oxygen-evolution, surface-reconstruction, x-ray-apbsorption-spectroscopy
On the origin of in-gap states in amorphous Ge2Sb2Te5

The localized states in the band gap of amorphous phase change alloys like Ge2Sb2Te5 control the electrical conduction via the Poole-Frenkel mechanism. Understanding the origin of in-gap states and their evolution in time during aging of the glass is therefore important for the control of the resistance drift in phase change memory devices. Here, we use a machine learning interatomic potential to generate several models of Ge2Sb2Te5 whose electronic structure is then analyzed within density functional theory with a hybrid functional. A detailed statistical analysis of the structural motifs on which the in-gap states are localized, reveals that the vast majority of in-gap states involve wrong bonds (homopolar or Ge-Sb bonds) often accompanied by Ge in tetrahedral configurations or overcoordinated Ge and Sb atoms. Metadynamics simulations mimicking glass aging support the picture that structural relaxations lead to the depletion of in-gap states and then to an increase of resistance. The simulations thus provide important insights for the mitigation of the resistance drift in phase change memory devices.

Tags: electronic-memories, electronic-structure, machine-learning, molecular-dynamics, phase-change-materials
AMaRaNTA: automated first-principles exchange parameters in 2D magnets [Results]

AMaRaNTA stands for Automating Magnetic paRAmeters iN a Tensorial ApproachThe aim of the AMaRaNTA package is to implement and automatize the widely used energy-mapping method, namely the 4-state method using density functional theory by removing the laborious, multi-step manual procedures. Based on the AiiDA framework, it extracts full spin-orbit coupling included exchange and anisotropy parameters for 2D magnets. In its current implementation (full workchain publicly available at https://github.com/AMaRaNTA-code), AMaRaNTA returns the nearest-neighbour exchange tensor, complemented by scalar parameters for second- and third-nearest-neighbour exchange interactions as well as single-ion anisotropy.Present repository is to make available the results obtained using this workchain run on a set of 29 materials, a representative subset of the Materials Cloud 2D Structure database.

Tags: 2d-magnets, 4state-method, aiida, dft, magnetic-exchanges
Micro-environment of the Eu interstitial in the β-Si6-zAlzOzN8-z:Eu2+ green phosphor

The precise atomic-scale structure around Eu2+ activators in the β-Si6-zAlzOzN8-z:Eu2+ commercial green phosphor remains elusive. We use the first-principles ΔSCF excited-state method, embedding of the interatomic force constants for supercells up to 3501 atoms, and Huang-Rhys theory to clarify this issue. Monte Carlo exploration is used to identify representative low-energy structural models spanning different levels of Al/O concentration z.  For the lowest-energy structure at low z, our computed photoluminescence spectrum reproduces the experimental vibronic peaks at 6K with excellent agreement in peak positions and intensities, validating the Eu-N9 coordination model with Al, O, and Eu confined to the same crystallographic plane. Analysis of the low-energy structures reveals that the electron-phonon coupling is weak (S ≈ 2.15) with a robust characteristic phonon signature across different Al/O arrangements, explaining the surprising persistence of resolved phonon replicas with increasing z. We explain the experimentally observed red-shift of emission with increasing z through systematic trends in zero-phonon line energies, modest increases in Huang-Rhys factors, and larger configurational diversity at higher compositions.

Tags: dft, electron-phonon, phosphors, vibronic
Predicting challenging phase transitions with Bayesian active learning

Materials underpin modern technologies, from energy harvesting, storage, and conversion to information and communication technologies. Their functionality is often governed by the interplay between competing phases, as thermodynamic behavior shapes microscopic properties and ultimately determines technological performance; for instance, the light absorption of inorganic metal-halide perovskites in solar cells. Accurately predicting crystal thermodynamics, however, remains a major challenge for computational approaches because strong anharmonic effects require extensive sampling of the potential energy surface. Here, we present an on-the-fly Bayesian framework, combined with the stochastic self-consistent harmonic approximation, for learning first-principles interatomic potentials. This approach enables the prediction of thermodynamic properties over a broad temperature range with first-principles accuracy while requiring training on only a few tens to a few hundreds of atomic configurations. To demonstrate its power, we investigate the thermodynamic and dynamical properties of Li2O, α-CsPbI3, and δ-CsPbI3, requiring only 44, 256, and 50 total-energy calculations, respectively. Notably, we show that this framework accurately captures the phase diagram of CsPbI3, which explains its spontaneous degradation into the non-absorbing yellow phase, predicting the transition temperature with remarkable accuracy and efficiency. More broadly, the method presented opens a novel route toward accelerated materials engineering under realistic conditions for a wide range of technologically relevant applications, including solid-state batteries, optoelectronic devices, and memristors.

Tags: active-learning, anharmonicity, machine-learning, phase-transitions, phonons, thermodynamics
data-efficient and fast machine learning molecular dynamics through integrated active learning and knowledge distillation

This entry provides the training and test datasets, final datasets, trained models, and simulation results associated with four machine learning interatomic potentials (MLIPs) of liquid water: DeePMD and MACE models trained from scratch via active learning, a fine-tuned MACE foundation model, and a DeePMD potential obtained by knowledge distillation. All input scripts for the active learning and AIMD simulations have been included. Source data for reproducing the figures in the manuscript are provided. Detailed explanations for each subdirectory are provided inside the folder.

Tags: active-learning, deepmd, knowledge-distillation, mace, machine-learning-interatomic-potential
Data availability for "ChemFit: a framework for automated high-dimensional model parameter optimization"

This repository accompanies the manuscript “ChemFit: A framework for automated high-dimensional model parameter optimization” and contains the source code, input structures, and simulation outputs required to reproduce the examples presented in the study. ChemFit is a Python framework for the definition and massively concurrent evaluation of simulation-based objective functions for use with gradient-free and black-box optimization algorithms.The archive includes snapshots of the source code used in the study together with the corresponding simulation metadata, logfiles, reference structures, and initial configuration files necessary to reproduce the reported optimization workflows and results.

Tags: biocondensates, chemfit, coarse-grained-model, computational-physics, lennard-jones, parameter-optimization, water-model
Performance improvement of deorbitalized exchange-correlation functionals

Deorbitalization of a conventional meta-generalized-gradient exchange-correlation approximation replaces its dependence upon the Kohn-Sham kinetic energy density with a dependence on the density gradient and Laplacian. In principle, that simplification should provide improved computational performance relative to the original meta-GGA form because of the shift from an orbital-dependent generalized Kohn-Sham potential to a true KS local potential. Often that prospective gain is lost because of problematic roughness in the density caused by the density Laplacian and consequent roughness in the exchange-correlation potential from the resulting higher-order spatial derivatives of the density in it. We address the problem by constructing a deorbitalizer based on the RPP deorbitalizer [Phys. Rev. Mater. 6, 083803 (2022)] with comparative smoothness of the potential along with retention of constraint satisfaction as design goals. Applied to the r2SCAN exchange-correlation functional [J. Phys. Chem. Lett. 11, 8208 (2020)], we find substantial timing improvements for solid-state calculations over both r2SCAN and its earlier deorbitalization for high precision calculations of structural properties, while improving upon the accuracy of RPP deorbitalization for both solids and molecules.

Data archive includes data needed to reproduce the figures and tables of this work, and input scripts for test set calculations performed using VASP and NWChem electronic structure codes.

Tags: computation, density-functional-theory, deorbitalization, metagga