[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123494-en":3,"doc-seo-123494-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},123494,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Machine-learning-driven modelling of amorphous and polycrystalline BaZrS3 - research article","Chalcogenide perovskite BaZrS3 attracts interest for emerging thin-film photovoltaics. This work demonstrates how machine-learning-driven modelling can represent both amorphous precursors and polycrystalline structures with complex grain boundaries. A bespoke machine-learned interatomic potential (MLIP) for BaZrS3 is used to analyze atomic-scale amorphous structure, quantify grain-boundary formation energies, and generate realistic-scale polycrystalline models comparable to experiment. The approach highlights the expanding role of MLIPs toward device-scale simulations for photovoltaics and photocatalysis.","Journal of  \nMaterials Chemistry A  \nPAPER  \nCite this: DOI: 10 .1039/d5ta04536c  \nReceived 4th June 2025  \nAccepted 1st September 2025 DOI: 10.1039/d5ta04536c[rsc.li/materials-a](rsc.li/materials-a)  \nMachine-learning-driven modelling of amorphous and polycrystalline BaZrS3  \nLaura-Bianca Paca,  a Yuanbin Liu,  a Andy S. Anker, ab Ludmilla Steier  a and Volker L. Deringer  *a  \nThe chalcogenide perovskite material BaZrS3 is of growing interest for emerging thin-film photovoltaics. Here we show how machine-learning-driven modelling can be used to describe the material's amorphous precursor as well as polycrystalline structures with complex grain boundaries. Using a bespoke machine-learned interatomic potential (MLIP) model for BaZrS3, we study the atomic-scale structure of the amorphous phase, quantify grain-boundary formation energies, and create realistic-scale polycrystalline structural models which can be compared to experimental data. Beyond BaZrS3, our work exemplifies the increasingly central role of MLIPs in materials chemistry and marks a step towards realistic device-scale simulations of materials that are gaining momentum in the fields of photovoltaicsand photocatalysis.  \nIntroduction  \nIn the search for new, sustainable photoabsorbers, sul􀀁debased chalcogenide perovskite materials have emerged as attractive lead-free candidates.1 However, while oxide and halide perovskites have de􀀁ned much of the progress in photovoltaics and related 􀀁elds, chalcogenide perovskites have only more recently begun to be explored. Among the latter, BaZrS3 presents optical absorption matching or even surpassing those of halide perovskites and GaAs,2 competitive charge carrier lifetimes, and improved stability to environmental factors compared to other perovskite materials.3–5 Thin 􀀁lms of BaZrS3 can be synthesised from earth-abundant and non-toxic elements: by sul􀀁dation of Ba–Zr–O precursors4,6,7 or by directly depositing sul􀀁de species using pulsed laser deposition,8 molecular beam epitaxy,9 or sputtering.3 Most methods involve the deposition of amorphous precursors that require temperatures of z 900 °C to crystallise. Their growth and subsequent crystallisation has been followed experimentally using X-ray diffraction (XRD) or X-ray spectroscopy techniques.10–12  \nGiven the rapidly growing interest in BaZrS3, computational methods are increasingly used to complement experimental studies of this material. Density-functional theory (DFT) and phonon computations were employed to map out the thermodynamic conditions under which BaZrS3 􀀁lms might form and which surface termination is expected to be the most stable.13,14  \naInorganic Chemistry Laboratory, Department of Chemistry, University of Oxford,  \nOxford OX1 3QR, UK. E-mail: [volker.deringer@chem.ox.ac.uk](volker.deringer@chem.ox.ac.uk)  \nbDepartment of Energy Conversion and Storage, Technical University of Denmark, Kgs. Lyngby 2800, Denmark  \nTo reach beyond the system-size limits of DFT-based methods, machine-learned interatomic potentials (MLIPs) have now been applied to many functional materials,15–17 including halide perovskites.18–21 The chalcogenide alternatives, viz. BaZrS3 and homologous compounds, were recently studied in a comprehensive work using ML-accelerated molecular dynamics (MD) .22 These studies have typically focused on the crystalline material20,22 and the formation of other phases, such as the binary crystals or 2D Ruddlesden–Popper structures.13 To validate MLaccelerated MD, Kayastha et al. compared simulated XRD patterns for MD-generated BaZrS3 structures with experimental XRD patterns.23 However, these simulations also were focused on ordered unit cells, corresponding to single-crystalline samples.  \nThis limitation is more generally a current research challenge in modelling perovskite solar-cell materials: experimentally synthesised materials are usually polycrystalline, and fully realistic simulations would therefore need to involve structural models representi","cbCaimGeuuJPrzLp","https://ap.wps.com/l/cbCaimGeuuJPrzLp","pdf",3427882,1,"English","en",105,"# Introduction\n## Motivation and background on BaZrS3\n## Experimental synthesis and modelling needs\n## Computational approaches and limitations\n# Method and MLIP framework\n## Development of the MLIP model based on ACE\n## Training data construction and coverage\n# Results and applications\n## Describing amorphous and polycrystalline BaZrS3\n## Quantifying grain-boundary energies\n## Toward realistic device-scale simulations","[{\"question\":\"What material and application context does the paper focus on?\",\"answer\":\"The paper focuses on BaZrS3, a chalcogenide perovskite material of growing interest for emerging thin-film photovoltaics, and it also connects to photocatalysis.\"},{\"question\":\"How does the study model amorphous and polycrystalline BaZrS3?\",\"answer\":\"It introduces a bespoke machine-learned interatomic potential (MLIP) and uses it within an ACE framework to simulate both ordered crystalline and disordered amorphous structures, including polycrystalline grain-boundary configurations.\"},{\"question\":\"What outputs are used to compare with experimental data?\",\"answer\":\"The study creates realistic-scale polycrystalline structural models intended to be compared with experimental observations, while also quantifying grain-boundary formation energies and analyzing atomic-scale amorphous structure.\"}]","Machine-learning-driven modelling of amorphous and polycrystalline BaZrS3 - 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