Thalis H. B. da Silva, Tiago F. T. Cerqueira, Hai-Chen Wang and Miguel A. L. Marques
{"title":"High-throughput study of kagome compounds in the AV3Sb5 family†","authors":"Thalis H. B. da Silva, Tiago F. T. Cerqueira, Hai-Chen Wang and Miguel A. L. Marques","doi":"10.1039/D5DD00123D","DOIUrl":"https://doi.org/10.1039/D5DD00123D","url":null,"abstract":"<p >The kagome lattice has emerged as a fertile ground for exotic quantum phenomena, including superconductivity, charge density wave, and topologically nontrivial states. While AV<small><sub>3</sub></small>Sb<small><sub>5</sub></small> (A = K, Rb, Cs) compounds have been extensively studied in this context, the broader AB<small><sub>3</sub></small>C<small><sub>5</sub></small> family remains largely unexplored. In this work, we employ machine-learning accelerated, high-throughput density functional theory calculations to systematically investigate the stability and electronic properties of kagome materials derived from atomic substitutions in the AV<small><sub>3</sub></small>Sb<small><sub>5</sub></small> structure. We identify 36 promising candidates that are thermodynamically stable, with many more close to the convex hull. Stable compounds are not only found with a pnictogen (Sb or Bi) as the C atom, but also with Au, Hg, Tl, and Ce. This diverse chemistry opens the way to tune the electronic properties of the compounds. In fact, many of these compounds exhibit Dirac points, Van Hove singularities, or flat bands close to the Fermi level. Our findings provide an array of compounds for experimental synthesis and further theoretical exploration of kagome superconductors beyond the already known systems.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 9","pages":" 2431-2438"},"PeriodicalIF":6.2,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00123d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
George Lyall-Brookes, Alex C. Padgham and Anna G. Slater
{"title":"Flow chemistry as a tool for high throughput experimentation","authors":"George Lyall-Brookes, Alex C. Padgham and Anna G. Slater","doi":"10.1039/D5DD00129C","DOIUrl":"https://doi.org/10.1039/D5DD00129C","url":null,"abstract":"<p >The way in which compounds and processes are discovered, screened, and optimised is changing, catalysed <em>via</em> the advancement of technology and automation. High throughput experimentation (HTE) is one of the most prevalent techniques in this area, with applications found across a broad spectrum of chemical fields. However, limitations such as challenges in handling volatile solvents mean it is not suitable for all applications, and scale-up can require extensive re-optimisation from an initial high throughput screening (HTS). These challenges can be addressed by coupling HTS with other enabling technologies, such as flow chemistry. The use of flow also widens available process windows, giving access to chemistry that is extremely challenging to carry out under batch-wise HTS. This review will highlight key contributions of flow chemistry approaches for HTS across six research areas, outlining applications, capabilities and benefits, finishing with comments on future directions for the technology.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 9","pages":" 2364-2400"},"PeriodicalIF":6.2,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00129c?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evan Xie, Xijun Wang, J. Ilja Siepmann, Haoyuan Chen and Randall Q. Snurr
{"title":"Generative AI for design of nanoporous materials: review and future prospects","authors":"Evan Xie, Xijun Wang, J. Ilja Siepmann, Haoyuan Chen and Randall Q. Snurr","doi":"10.1039/D5DD00221D","DOIUrl":"https://doi.org/10.1039/D5DD00221D","url":null,"abstract":"<p >Generative artificial intelligence (AI) is emerging as a powerful tool for advancing the design of nanoporous materials such as metal–organic frameworks, covalent–organic frameworks, and zeolites. These materials have potential application in important areas such as carbon capture, catalysis, gas storage, chemical separation, and drug delivery due to their modular, tunable structures, and their performance in these areas depends on precise control over their structure, chemical functionalities, and properties. Herein, we provide a review of generative AI algorithms that are emerging as powerful tools for the design of nanoporous materials, namely generative adversarial networks, variational autoencoders, diffusion models, genetic algorithms, reinforcement learning, and large language models. Some models are particularly good at generating diverse and high-quality designs, while others excel at exploring large design spaces or optimizing materials with desired properties. Certain algorithms also allow for efficient transitions between different designs, and some offer versatility in generating materials based on textual input. We discuss the advantages, limitations, and applications of these algorithms in porous material design and emphasize the future potential of integrating AI with experimental workflows to accelerate the development and validation of AI-generated materials.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 9","pages":" 2336-2363"},"PeriodicalIF":6.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00221d?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145028073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"af2rave: protein ensemble generation with physics-based sampling.","authors":"Da Teng, Vanessa J Meraz, Akashnathan Aranganathan, Xinyu Gu, Pratyush Tiwary","doi":"10.1039/d5dd00201j","DOIUrl":"10.1039/d5dd00201j","url":null,"abstract":"<p><p>We introduce , an open-source Python package that implements an improved and automated version of our previous AlphaFold2-RAVE protocol. AlphaFold2-RAVE integrates machine learning-based structure prediction with physics-driven sampling to generate alternative protein conformations efficiently. It has been well established that protein structures are not static but exist as ensembles of conformations, many of which are functionally relevant yet challenging to resolve experimentally. While deep learning models like AlphaFold2 can predict structural ensembles, they lack explicit physical validation. The Alphafold2-RAVE family of methods addresses this limitation by combining reduced multiple sequence alignment (MSA) AlphaFold2 predictions with biased or unbiased molecular dynamics (MD) simulations to efficiently explore local conformational space. Compared to our previous work, the current workflow significantly reduced the required amount of <i>a priori</i> knowledge about a system to allow the user to focus on the conformation diversity they would like to sample. This is achieved by a feature selection module to automatically pickup the important collective variables to monitor. The improved workflow was validated on multiple systems with the package , including <i>E. coli</i> adenosine kinase (ADK) and human DDR1 kinase, successfully identifying distinct functional states with minimal prior biological knowledge. Furthermore, we demonstrate that achieves conformational sampling efficiency comparable to long unbiased MD simulations on the SARS-CoV-2 spike protein receptor-binding domain while significantly reducing the computational cost. The package provides a streamlined workflow for researchers to generate and analyze alternative protein conformations, offering an accessible tool for drug discovery and structural biology.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":"2052-2061"},"PeriodicalIF":6.2,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144577116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alex M. Ganose, Hrushikesh Sahasrabuddhe, Mark Asta, Kevin Beck, Tathagata Biswas, Alexander Bonkowski, Joana Bustamante, Xin Chen, Yuan Chiang, Daryl C. Chrzan, Jacob Clary, Orion A. Cohen, Christina Ertural, Max C. Gallant, Janine George, Sophie Gerits, Rhys E. A. Goodall, Rishabh D. Guha, Geoffroy Hautier, Matthew Horton, T. J. Inizan, Aaron D. Kaplan, Ryan S. Kingsbury, Matthew C. Kuner, Bryant Li, Xavier Linn, Matthew J. McDermott, Rohith Srinivaas Mohanakrishnan, Aakash N. Naik, Jeffrey B. Neaton, Shehan M. Parmar, Kristin A. Persson, Guido Petretto, Thomas A. R. Purcell, Francesco Ricci, Benjamin Rich, Janosh Riebesell, Gian-Marco Rignanese, Andrew S. Rosen, Matthias Scheffler, Jonathan Schmidt, Jimmy-Xuan Shen, Andrei Sobolev, Ravishankar Sundararaman, Cooper Tezak, Victor Trinquet, Joel B. Varley, Derek Vigil-Fowler, Duo Wang, David Waroquiers, Mingjian Wen, Han Yang, Hui Zheng, Jiongzhi Zheng, Zhuoying Zhu and Anubhav Jain
{"title":"Atomate2: modular workflows for materials science","authors":"Alex M. Ganose, Hrushikesh Sahasrabuddhe, Mark Asta, Kevin Beck, Tathagata Biswas, Alexander Bonkowski, Joana Bustamante, Xin Chen, Yuan Chiang, Daryl C. Chrzan, Jacob Clary, Orion A. Cohen, Christina Ertural, Max C. Gallant, Janine George, Sophie Gerits, Rhys E. A. Goodall, Rishabh D. Guha, Geoffroy Hautier, Matthew Horton, T. J. Inizan, Aaron D. Kaplan, Ryan S. Kingsbury, Matthew C. Kuner, Bryant Li, Xavier Linn, Matthew J. McDermott, Rohith Srinivaas Mohanakrishnan, Aakash N. Naik, Jeffrey B. Neaton, Shehan M. Parmar, Kristin A. Persson, Guido Petretto, Thomas A. R. Purcell, Francesco Ricci, Benjamin Rich, Janosh Riebesell, Gian-Marco Rignanese, Andrew S. Rosen, Matthias Scheffler, Jonathan Schmidt, Jimmy-Xuan Shen, Andrei Sobolev, Ravishankar Sundararaman, Cooper Tezak, Victor Trinquet, Joel B. Varley, Derek Vigil-Fowler, Duo Wang, David Waroquiers, Mingjian Wen, Han Yang, Hui Zheng, Jiongzhi Zheng, Zhuoying Zhu and Anubhav Jain","doi":"10.1039/D5DD00019J","DOIUrl":"10.1039/D5DD00019J","url":null,"abstract":"<p >High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 7","pages":" 1944-1973"},"PeriodicalIF":6.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12210224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital discoveryPub Date : 2025-06-30eCollection Date: 2025-08-06DOI: 10.1039/d5dd00012b
Xu Chen, Dazhou Yu, Liang Zhao, Fang Liu
{"title":"ACES-GNN: can graph neural network learn to explain activity cliffs?","authors":"Xu Chen, Dazhou Yu, Liang Zhao, Fang Liu","doi":"10.1039/d5dd00012b","DOIUrl":"10.1039/d5dd00012b","url":null,"abstract":"<p><p>Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug discovery. This study introduces the Activity-Cliff-Explanation-Supervised GNN (ACES-GNN) framework, designed to simultaneously improve predictive accuracy and interpretability by integrating explanation supervision for activity cliffs (ACs) into GNN training. ACs, defined by structurally similar molecules with significant potency differences, pose challenges for traditional models due to their reliance on shared structural features. By aligning model attributions with chemist-friendly interpretations, the ACES-GNN framework bridges the gap between prediction and explanation. Validated across 30 pharmacological targets, ACES-GNN consistently enhances both predictive accuracy and attribution quality for ACs compared to unsupervised GNNs. Our results demonstrate a positive correlation between improved predictions and accurate explanations, offering a robust and adaptable framework to better understand and interpret ACs. This work underscores the potential of explanation-guided learning to advance interpretable artificial intelligence in molecular modeling and drug discovery.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":"2062-2074"},"PeriodicalIF":6.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144577115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital discoveryPub Date : 2025-06-30eCollection Date: 2025-08-06DOI: 10.1039/d5dd00031a
Jacob Lynge Elholm, Paulius Baronas, Paul A Gueben, Victoria Gneiting, Helen Hölzel, Kasper Moth-Poulsen
{"title":"An automated photo-isomerisation and kinetics characterisation system for molecular photoswitches.","authors":"Jacob Lynge Elholm, Paulius Baronas, Paul A Gueben, Victoria Gneiting, Helen Hölzel, Kasper Moth-Poulsen","doi":"10.1039/d5dd00031a","DOIUrl":"10.1039/d5dd00031a","url":null,"abstract":"<p><p>Physical chemistry parameters such as absorbance, photoconversion quantum yield, and thermal half-lives are crucial for the characterisation of new molecular photoswitch systems. In a traditional workflow, these parameters are challenging and time-consuming to measure. In this paper, a high-throughput flow-based photoswitch characterisation platform with a built-in broad-spectrum LED array and thermal back-conversion capabilities is developed with UV-Vis spectroscopic analysis tools to reduce materials consumption, limit laborous workflows, and improve experimental reproducibility. Following the experiments, an in-house developed Python program is used for easy and fast data analysis. The program is designed to be able to analyse different types of photoswitches depending on the molecular properties. The specific components and configurations are detailed, enabling reproducibility and adaptation to various experimental needs. This system demonstrates the potential for efficient, high-throughput analysis in spectroscopic studies. Wide applicability is underlined by showing the results and comparison of three different photoswitch types, norbornadienes, bicyclooctadienes, and azobenzenes. The results we obtain are in good agreement with reported values in the literature.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":"2045-2051"},"PeriodicalIF":6.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12232385/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144593079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital discoveryPub Date : 2025-06-25eCollection Date: 2025-08-06DOI: 10.1039/d5dd00174a
Lucas Foppa, Matthias Scheffler
{"title":"Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups.","authors":"Lucas Foppa, Matthias Scheffler","doi":"10.1039/d5dd00174a","DOIUrl":"10.1039/d5dd00174a","url":null,"abstract":"<p><p>Useful materials are often statistically exceptional and they might be overlooked by artificial intelligence (AI) models that attempt to describe all materials simultaneously. These global models perform well for the majority of materials, but they do not necessarily capture the useful ones. Subgroup discovery (SGD) identifies descriptions of subsets of materials associated with exceptional values of a chosen property. Thus, SGD can better capture exceptional materials compared to widely used AI techniques. Previous studies focused on the SG that maximizes an objective function establishing a tradeoff between the SG size and the exceptionality of the distribution of property values within the SG. However, this optimization does not give a unique solution, but many SGs typically have similar objective-function values. Here, we identify a \"Pareto region\" of SGD solutions presenting a multitude of size-exceptionality tradeoffs. The approach is demonstrated by learning descriptions of perovskites with a high bulk modulus.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" ","pages":"2175-2187"},"PeriodicalIF":6.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12257468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Setting new benchmarks in AI-driven infrared structure elucidation†","authors":"Marvin Alberts, Federico Zipoli and Teodoro Laino","doi":"10.1039/D5DD00131E","DOIUrl":"10.1039/D5DD00131E","url":null,"abstract":"<p >Automated structure elucidation from infrared (IR) spectra represents a significant breakthrough in analytical chemistry, having recently gained momentum through the application of Transformer-based language models. In this work, we improve our original Transformer architecture, refine spectral data representations, and implement novel augmentation and decoding strategies to significantly increase performance. We report a Top-1 accuracy of 63.79% and a Top-10 accuracy of 83.95% compared to the current performance of state-of-the-art models of 53.56% and 80.36%, respectively. Our findings not only set a new performance benchmark but also strengthen confidence in the promising future of AI-driven IR spectroscopy as a practical and powerful tool for structure elucidation. To facilitate broad adoption among chemical laboratories and domain experts, we openly share our models and code.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 7","pages":" 1936-1943"},"PeriodicalIF":6.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203481/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jonathan D. Schultz, Kelsey A. Parker, Bashir Sbaiti and David N. Beratan
{"title":"Using machine learning to map simulated noisy and laser-limited multidimensional spectra to molecular electronic couplings†","authors":"Jonathan D. Schultz, Kelsey A. Parker, Bashir Sbaiti and David N. Beratan","doi":"10.1039/D5DD00125K","DOIUrl":"https://doi.org/10.1039/D5DD00125K","url":null,"abstract":"<p >Two-dimensional electronic spectroscopy (2DES) has enabled significant discoveries in both biological and synthetic energy-transducing systems. Although deriving chemical information from 2DES is a complex task, machine learning (ML) offers exciting opportunities to translate complicated spectroscopic data into physical insight. Recent studies have found that neural networks (NNs) can map simulated multidimensional spectra to molecular-scale properties with high accuracy. However, simulations often do not capture experimental factors that influence real spectra, including noise and suboptimal pulse resonance conditions, bringing into question the experimental utility of NNs trained on simulated data. Here, we show how factors associated with experimental 2D spectral data influence the ability of NNs to map simulated 2DES spectra onto underlying intermolecular electronic couplings. By systematically introducing multisourced noise into a library of 356 000 simulated 2D spectra, we show that noise does not hamper NN performance for spectra exceeding threshold signal-to-noise ratios (SNR) of <em>ca.</em> 12.4, 2.5, and 5.1 if uncorrelated additive, correlated additive, or intensity-dependent noise sources dominate, respectively. In stark contrast to human-based analyses of 2DES data, we find that the NN accuracy improves significantly (<em>ca.</em> 84% → 96%) when the data are constrained by the bandwidth and center frequency of the pump pulses. This result is consistent with the NN learning the optical trends described by Kasha's theory of molecular excitons. Our findings convey positive prospects for adapting simulation-trained NNs to extract molecular properties from inherently imperfect experimental 2DES data. More broadly, we propose that machine-learned perspectives of nonlinear spectroscopic data may produce unique and perhaps counterintuitive guidelines for experimental design.</p>","PeriodicalId":72816,"journal":{"name":"Digital discovery","volume":" 7","pages":" 1912-1924"},"PeriodicalIF":6.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2025/dd/d5dd00125k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144589490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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