[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127455-en":3,"doc-seo-127455-105":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},127455,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic Potentials","Molten salts are promising for clean-energy applications where thermophysical properties and vapor pressure over operating temperatures are essential for safe engineering. Experimental evaluation is difficult and often yields large uncertainties, motivating fast, scalable molecular simulations. This work develops machine learning interatomic potentials (MLIP) for molten AlCl3 across 473–613 K and 2.7–23.4 bar, enabling direct two-phase coexistence predictions of vapor-pressure and liquid–vapor phase behavior. Two MLIP architectures are benchmarked against experimental structures and densities, showing close agreement for critical properties and improved correlations when trained with low-density configurations and PBE-D3 AIMD data.","Copyright Notices  \nNotice: This manuscript has been authored by UT-Battelle, LLC, under Contract No. DE-AC05- 00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ([http://energy.gov/downloads/doe-public-access-plan](http://energy.gov/downloads/doe-public-access-plan)).  \nNotice of Copyright: This manuscript has been authored by UT-Battelle, LLC, under contract DEAC05-00OR22725 with the US Department of Energy (DOE) . The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan ([http://energy.gov/downloads/doe-public-access-plan](http://energy.gov/downloads/doe-public-access-plan))  \nLiquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl 3) Enabled by Machine Learning Interatomic Potentials  \nRajni Chahal,1 * Luke D. Gibson,2 Santanu Roy,1 Vyacheslav S. Bryantsev1 *  \n1Chemical Science Division, Oak Ridge National Laboratory, Oak Ridge, TN-37830, United States  \n2Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA  \n[chahalr@ornl.gov](chahalr@ornl.gov), [bryantsevv@ornl.gov](bryantsevv@ornl.gov)  \nKEYWORDS: molten salts, vapor pressure, machine learning interatomic potential, two-phase direct coexistence simulations, phase diagram, thermodynamic properties.  \nABSTRACT  \nMolten salts are promising candidates in numerous clean energy applications, where knowledge of thermophysical properties and vapor pressure across their operating temperature ranges is critical for safe operations. Due to challenges in evaluating these properties using experimental methods, fast and scalable molecular simulations are essential to complement the experimental data. In this study, we developed machine learning interatomic potentials (MLIP) to study the  \nAlCl3 molten salt across varied thermodynamic conditions (T=473–613 K and P=2.7-23.4 bar), which allowed us to predict temperature-surface tension correlations and liquid-vapor phase diagram from direct simulations of two-phase coexistence in this molten salt. Two MLIP architectures, a Kernel-based potential and neural network interatomic potential (NNIP), were considered to benchmark their performance for AlCl3 molten salt using experimental structure and density values. The NNIP potential employed in two-phase equilibrium simulations yields the critical temperature and critical density of AlCl3 that are within 10 K (~3%) and 0.03 g/cm3 (~7%) of the reported experimental values. An accurate correlation between temperature and viscosities is obtained as well. In doing so, we report that the inclusion of low-density configurations in their training is critical to more accurately represent the AlCl3 system across a wide phase-space. The MLIP trained using PBE-D3 functional in the ab initio molecular dynamics (AIMD) simulations (120 atoms) also showed close agreement with experimentally-determined molten salt structure comprising Al2Cl6 dimers, as validated using Raman spectra and neutron structure factor. The PBE-D3 as well as its trained MLIP showed better liquid density and temperature correlation for AlCl3 system when compared to several other density functionals explored in this work. Overall, the demonstrated approach to predict vapor pressure and temperature correlations in this study can be employed to screen nuclear reactors-relevant compositions, helping to mitigate safety concerns.  \nINTRODUCTION  \nMolten salts due to their favorable chemical, physical, and thermal properties lend themselves to numerous hig","cbCaibb49VQXYypZ","https://ap.wps.com/l/cbCaibb49VQXYypZ","pdf",1087245,1,38,"English","en",105,"# Abstract\n# Introduction\n## Motivation: molten salts and vapor-pressure safety\n## AlCl3 as a case study\n## Computational approaches for vapor pressure and phase equilibrium\n# Method (overview from keywords)\n## MLIP architectures for AlCl3\n## Two-phase direct coexistence simulations","[{\"question\":\"Why is vapor pressure knowledge critical for molten salts like AlCl3?\",\"answer\":\"Molten salts can develop high vapor pressures under elevated temperatures, creating serious safety concerns. For volatile systems such as AlCl3, temperature changes strongly affect vapor pressure.\"},{\"question\":\"What machine learning approach is used to model AlCl3?\",\"answer\":\"The study develops machine learning interatomic potentials (MLIP), including a kernel-based potential and a neural network interatomic potential (NNIP), trained to reproduce key thermodynamic and structural behaviors.\"},{\"question\":\"How does the MLIP training improve the accuracy of phase-equilibrium predictions?\",\"answer\":\"Including low-density configurations in the training is reported as critical to better represent AlCl3 across a wide phase space. MLIPs trained with PBE-D3 AIMD data also show closer agreement with experimentally determined structure.\"}]","Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic Potentials | PDF",1785938964,96,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"liquid-vapor-phase-equilibrium-in-molten-aluminum-chloride-alcl3-enabled-by-machine-learning-interatomic-potentials","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/liquid-vapor-phase-equilibrium-in-molten-aluminum-chloride-alcl3-enabled-by-machine-learning-interatomic-potentials/127455/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is vapor pressure knowledge critical for molten salts like AlCl3?","Question",{"text":76,"@type":77},"Molten salts can develop high vapor pressures under elevated temperatures, creating serious safety concerns. For volatile systems such as AlCl3, temperature changes strongly affect vapor pressure.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What machine learning approach is used to model AlCl3?",{"text":81,"@type":77},"The study develops machine learning interatomic potentials (MLIP), including a kernel-based potential and a neural network interatomic potential (NNIP), trained to reproduce key thermodynamic and structural behaviors.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the MLIP training improve the accuracy of phase-equilibrium predictions?",{"text":85,"@type":77},"Including low-density configurations in the training is reported as critical to better represent AlCl3 across a wide phase space. MLIPs trained with PBE-D3 AIMD data also show closer agreement with experimentally determined structure.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]