[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127352-en":3,"doc-seo-127352-105":30,"detail-sidebar-cat-0-en-105":92},{"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127352,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Accurate Machine Learning Interatomic Potentials for Polyacene Molecular Crystals: Application to Single Molecule Host-Guest Systems","Emerging machine learning interatomic potentials (MLIPs) enable large-scale, accurate material simulations, yet rigorous validation for vibrational dynamics in molecular crystals remains limited. This work develops a general MLIP using the graph neural-network MACE architecture and active-learning strategies to accurately describe vibrational dynamics across polyacene-based crystals including naphthalene, anthracene, tetracene, and pentacene. Careful error propagation demonstrates accuracy and supports analysis of anharmonic vibrational features, lifetimes, and coupling, including large-scale host-guest systems where molecular-dynamics methods quantify nuclear-motion coupling between host and guest. The study provides a framework for engineering vibrational interactions in complex molecular environments.","arXiv :2504 . 11224v1 [ cond-mat .mtrl-sci ] 15 Apr 2025  \nAccurate Machine Learning Interatomic Potentials for Polyacene Molecular Crystals: Application to Single Molecule Host-Guest Systems  \nBurak Gurlek, 1, ∗ Shubham Sharma, 1, ∗ Paolo Lazzaroni, 1 Angel Rubio, 1, 2 and Mariana Rossi 1,†  \n1 Max Planck Institute for the Structure and Dynamics of Matter and Center for Free-Electron Laser Science,  \nLuruper Chaussee 149, 22761 Hamburg, Germany  \n2 Initiative for Computational Catalysis (ICC), The Flatiron Institute,  \n162 Fifth Avenue, New York, New York 10010, USA  \nEmerging machine learning interatomic potentials (MLIPs) offer a promising solution for largescale accurate material simulations, but stringent tests related to the description of vibrational dynamics in molecular crystals remain scarce. Here, we develop a general MLIP by leveraging the graph neural network-based MACE architecture and active-learning strategies to accurately capture vibrational dynamics across a range of polyacene-based molecular crystals, namely naphthalene, anthracene, tetracene and pentacene. Through careful error propagation, we show that these potentials are accurate and enable the study of anharmonic vibrational features, vibrational lifetimes, and vibrational coupling. In particular, we investigate large-scale host-guest systems based on these molecular crystals, showing the capacity of molecular-dynamics-based techniques to explain and quantify vibrational coupling between host and guest nuclear motion. Our results establish a framework for understanding vibrational signatures in large-scale complex molecular systems and thus represent an important step for engineering vibrational interactions in molecular environments.  \nI. INTRODUCTION  \nOrganic molecular crystals, characterized by their long-range order and rich intermolecular interactions, are crucial in diverse applications, ranging from pharmaceuticals to electronics, and hold significant potential for emerging technologies, such as photovoltaics [1] and quantum information systems [2] . While these applications primarily rely on the underlying electronic properties of these systems, molecular vibrations, encompassing both inter-and intramolecular modes, are equally important due to their role in determining the crystal structure and the pronounced electron-phonon coupling which is often observed [3–7] .  \nSpecifically, molecular vibrations and their anharmonic couplings play a pivotal role in determining the thermodynamic stability of crystal polymorphs [8–12], in enhancing or hindering charge transport by modulating carrier mobility through dynamic intermolecular coupling [7, 13, 14], in facilitating rapid singlet fission to improve solar cell efficiency [1, 15, 16], and even in offering a potential usage as quantum memory elements [17] . For example, polycyclic aromatic hydrocarbons embedded in large-bandgap host materials are being explored as single-photon sources, nonlinear quantum optical elements, and nanoscale sensors [2], as they exhibit narrow optical transitions at cryogenic temperatures, allowing highly coherent light-matter interactions [18] . However, previous studies have predominantly focused on the electronic transitions, leaving the rich internal structures arising from vibrational, and spin degrees of freedom largely unexplored [19] . Despite their undeniable importance, accurately modeling vibrational dynamics that are  \n∗ These two authors contributed equally † [mariana.rossi@mpsd.mpg.de](mariana.rossi@mpsd.mpg.de)  \naffected by anharmonic mode-coupling and long-range van der Waals interactions is hampered by the computational complexity of such simulations, which makes them prohibitively expensive with traditional first-principles methods such as density-functional theory (DFT) .  \nMachine learning interatomic potentials (MLIPs) hold great promise in addressing the challenges associated with large-scale and long-time simulations of complex material systems, o","cbCaiqWOiUGeI639","https://ap.wps.com/l/cbCaiqWOiUGeI639","pdf",14259142,1,27,"English","en",105,"# Introduction\n## Motivation: vibrational effects in molecular crystals\n## Machine learning interatomic potentials and active learning\n## Gap: limited reliability for molecular-crystal vibrational dynamics\n## Scope: polyacene crystals and host-guest applications","[{\"question\":\"What limitation does this study target in machine learning interatomic potentials for molecular crystals?\",\"answer\":\"It addresses the lack of stringent tests for how MLIPs describe vibrational dynamics in molecular crystals, especially when anharmonic mode coupling and long-range van der Waals interactions matter.\"},{\"question\":\"How is the proposed MLIP constructed and validated?\",\"answer\":\"The work builds a general MLIP using the MACE graph neural network architecture and active-learning strategies, then validates predictive accuracy across multiple polyacene molecular crystals while propagating errors carefully.\"},{\"question\":\"What capabilities does the MLIP enable in the analysis of host-guest systems?\",\"answer\":\"It enables molecular-dynamics-based methods to explain and quantify vibrational coupling between host and guest nuclear motion in large-scale host-guest systems.\"}]","Accurate Machine Learning Interatomic Potentials for Polyacene Molecular Crystals: Application to Single Molecule Host-Guest Systems | 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limitation does this study target in machine learning interatomic potentials for molecular crystals?","Question",{"text":76,"@type":77},"It addresses the lack of stringent tests for how MLIPs describe vibrational dynamics in molecular crystals, especially when anharmonic mode coupling and long-range van der Waals interactions matter.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the proposed MLIP constructed and validated?",{"text":81,"@type":77},"The work builds a general MLIP using the MACE graph neural network architecture and active-learning strategies, then validates predictive accuracy across multiple polyacene molecular crystals while propagating errors carefully.",{"name":83,"@type":74,"acceptedAnswer":84},"What capabilities does the MLIP enable in the analysis of host-guest systems?",{"text":85,"@type":77},"It enables molecular-dynamics-based methods to explain and quantify vibrational coupling between host and guest nuclear motion in large-scale host-guest 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