Progress in understanding triple ionic–electronic conduction in perovskite oxides for protonic ceramic fuel cell applications

Nanoscale, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Optimizing ORR in PCFC cathodes by balancing proton and oxygen–ion transport.

Language: Английский

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Machine Learning Models for Efficient Property Prediction of ABX₃ Materials: A High-Throughput Approach

ACS Omega, Journal Year: 2024, Volume and Issue: 9(48), P. 47519 - 47531

Published: Nov. 18, 2024

Recently, ABX3 materials have garnered significant attention due to their diverse applications in photovoltaics, catalysis, and optoelectronics as well remarkable efficiency energy conversion. However, progress has been somewhat slow the high expenses of experiment or time-consuming density functional theory (DFT) calculation. In this study, we utilized extreme gradient boosting (XGBoost) algorithm facilitate discovery characterization compounds based on vast data sets generated by DFT calculations. While XGBoost provides a powerful tool for accelerating compounds, it is crucial acknowledge that different approximation levels can significantly impact predicted band gaps, potentially introducing discrepancies when compared with experimental values. first step, predict space group 13947 oxides halides using Open Quantum Materials Database elemental features. Our analysis yields classification accuracies ranging from 82.39% 99.14% across these materials. Following this, regression algorithms are employed interrogate set, enabling predictions volume (achieving an optimal accuracy 98.41%, mean absolute error (MAE) 2.395 Å3 root-mean-square (RMSE) 4.416 Å3), formation (an 97.36%, MAE 0.075 eV/atom RMSE 0.132 eV/atom), gap 87.00%, 0.391 eV, 0.574 eV). Finally, prediction models identify possible groups each 1252 new formulas. Then, volume, energy, candidate group. Through predictive models, machine learning accelerates exploration enhanced performance functionality.

Language: Английский

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PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: April 23, 2025

Abstract The excited-state properties of molecular crystals are important for applications in organic electronic devices. G W approximation and Bethe-Salpeter equation ( +BSE) is the state-of-the-art method calculating crystalline solids with periodic boundary conditions. We present PAH101 dataset +BSE calculations 101 polycyclic aromatic hydrocarbons (PAHs) up to ~500 atoms unit cell. To best our knowledge, this first crystals. data records include quasiparticle band structure, fundamental gap, static dielectric constant, singlet exciton energy (optical gap), triplet energy, function, optical absorption spectra light polarized along three lattice vectors. can be used (i) discover materials desired electronic/optical properties, (ii) identify correlations between DFT quantities, (iii) train machine learned models help discovery efforts.

Language: Английский

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Redefining the Stability of Water Oxidation Electrocatalysts: Insights from Materials Databases and Machine Learning

ACS Materials Letters, Journal Year: 2024, Volume and Issue: 6(7), P. 2905 - 2918

Published: June 10, 2024

Research on electrochemical water splitting has experienced significant growth in interest transition metal borides, carbides, pnictides, and chalcogenides, owing to their notable catalytic performance. These materials, collectively called X-ides, are often considered promising electrocatalysts for the oxygen evolution reaction (OER). However, under strongly oxidizing conditions of OER, X-ides act as precatalysts, undergoing situ reconstruction a different, catalytically active phase. Discrepancies exist literature, with some studies claiming absence such transformations. Building upon previous efforts elucidate performance trends community, this Perspective discusses more nuanced approach X-ide research, emphasizing need reassess our understanding chemical stability significance process. By discussing role experimental computational databases, we present strategies predicting stress importance thorough validation. Moreover, highlight use machine learning extract meaningful insights from these data urge community adopt standardized, systematic reporting Finally, provide strategic guidelines directions advance ultimately enhancing future application sustainable hydrogen economy.

Language: Английский

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A Machine Learning‐Enhanced Framework for the Accelerated Development of Spinel Oxide Electrocatalysts

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: 14(39)

Published: Aug. 5, 2024

Abstract The surging demand for sustainable energy has spurred intensive research into electrochemical conversion devices such as fuel cells, water splitting, and metal‐air batteries. performance of oxygen electrocatalysts significantly impacts overall efficiency. Recently, spinel oxides (AB 2 O 4 ) have emerged promising candidates; however, the scarcity prior studies underscores need a thorough comprehensive exploration. This study presents computational framework that integrates machine learning density functional theory (DFT) calculations systematic screening 1240 oxides. data is addressed while enhancing prediction accuracy. Selected candidates are identified to outperform benchmarking perovskite oxide. Additionally, their potential mixed ionic electronic conductors with 3D network ion diffusion pathways highlighted. To further enhance understanding stability, catalytic activity, reaction mechanisms, new undemanding descriptor introduced: covalency indicator. offers design principle development high‐performance oxide electrocatalysts.

Language: Английский

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Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

Machine Learning Science and Technology, Journal Year: 2024, Volume and Issue: 5(4), P. 045051 - 045051

Published: Dec. 1, 2024

Abstract One compelling vision of the future materials discovery and design involves use machine learning (ML) models to predict properties then rapidly find tailored for specific applications. However, realizing this requires both providing detailed uncertainty quantification (model prediction errors domain applicability) making readily usable. At present, it is common practice in community assess ML model performance only terms accuracy (e.g. mean absolute error), while neglecting robust accessibility usability. Here, we demonstrate a practical method features with large set models. We develop random forest 33 spanning an array data sources (computational experimental) property types (electrical, mechanical, thermodynamic, etc). All have calibrated ensemble error bars quantify applicability guidance enabled by kernel-density-estimate-based feature distance measures. are publicly hosted on Garden-AI infrastructure, which provides easy-to-use, persistent interface dissemination that permits be invoked few lines Python code. power approach using our conduct fully ML-based exercise search new stable, highly active perovskite oxide catalyst materials.

Language: Английский

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Digital manufacturing of perovskite materials and solar cells

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124120 - 124120

Published: Sept. 17, 2024

Language: Английский

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Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization

Journal of Materiomics, Journal Year: 2024, Volume and Issue: 11(4), P. 100964 - 100964

Published: Nov. 14, 2024

Language: Английский

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Machine Learning-Driven Interface Engineering for Enhanced Microwave Absorption in MXene Films

Materials Today Physics, Journal Year: 2024, Volume and Issue: unknown, P. 101640 - 101640

Published: Dec. 1, 2024

Language: Английский

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