[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291056-105":59,"doc-detail-291056-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","accelerating-ab-initio-melting-property-calculations-with-machine-learning-application-to-the-high-entropy-alloy-tavcrw-291056","Accelerating ab initio melting property calculations with machine learning: application to the high entropy alloy TaVCrW","","Melting properties are essential for designing advanced refractory materials, yet high-temperature experiments are difficult and often yield sparse, scattered data. Ab initio free-energy calculations within DFT are accurate but computationally prohibitive because they require expensive thermodynamic integration driven by ab initio molecular dynamics. A DFT-based method is proposed using a tailored machine-learning potential to reproduce ab initio phase-space, enabling substitution of thermodynamic integration with efficient free-energy perturbation calculations. Applied to high-entropy alloy TaVCrW, the method predicts melting temperature, entropy and enthalpy of fusion, volume change at melting, and solid/liquid heat capacities, showing reasonable agreement with CALPHAD extrapolations.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/accelerating-ab-initio-melting-property-calculations-with-machine-learning-application-to-the-high-entropy-alloy-tavcrw-291056/291056/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/accelerating-ab-initio-melting-property-calculations-with-machine-learning-application-to-the-high-entropy-alloy-tavcrw-291056/291056.png","ImageObject",300,407,{"name":92,"@type":93},"Genevieve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why are melting properties hard to measure experimentally?","Question",{"text":113,"@type":114},"Melting temperatures are extremely high for refractory materials, making experiments very challenging and often producing very scattered data or even none.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What bottleneck makes ab initio free-energy calculations expensive?",{"text":118,"@type":114},"They generally require costly thermodynamic integration driven by ab initio molecular dynamics simulations, which makes high-throughput calculations infeasible.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the proposed machine-learning-assisted approach reduce computational cost?",{"text":122,"@type":114},"A specially designed machine-learning potential reproduces the ab initio phase-space distribution, allowing thermodynamic integration to be fully substituted by more efficient free-energy perturbation calculations.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291056,1789643331,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},1374391974585,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","npj | computational materials Article  \nPublished in partnership with the Shanghai Institute of Ceramics of the Chinese Academy of Sciences  \n[https://doi.org/10.1038/s41524-024-01464-7](https://doi.org/10.1038/s41524-024-01464-7)  \nAccelerating ab initio melting property calculations with machine learning: application to the high entropy  \nalloy TaVCrW  \n Check for updates  \n\n| Li-Fang Zhu 1,2 , Fritz Körmann1,2,3, Qing Chen  Blazej Grabowski 2 | 4,5, Malin Selleby |  | 5, Jörg Neugebauer1 & |\n| --- | --- | --- | --- |\n| Melting properties are critical for designing novel materials, especially for discovering highperformance, high-melting refractory materials. Experimental measurements of these properties are extremely challenging due to their high melting temperatures. Complementary theoretical predictions are, therefore, indispensable. One of the most accurate approaches for this purpose is the ab initio free-energy approach based on density functional theory (DFT) . However, it generally involves expensive thermodynamic integration using ab initio molecular dynamic simulations. The high computational cost makes high-throughput calculations infeasible. Here, we propose a highly efficient DFT-based method aided by a specially designed machine learning potential. Asthe machine learning potential can closely reproduce the ab initio phase-space distribution, even for multi-component alloys, the costly thermodynamic integration can be fully substituted with more efficient free energy perturbation calculations. The method achieves overall savings of computational resources by 80% compared to current alternatives. We apply the method to the high-entropy alloy TaVCrW and calculate its melting properties, includingthe melting temperature, entropy and enthalpy of fusion, and volume change at the melting point. Additionally, the heat capacities of solid and liquid TaVCrW are calculated. The results agree reasonably with the CALPHAD extrapolated values. |  |  |  |\n| Discovering novel high entropy alloys (HEAs) with exceptional performance has ushered in a new era for materials design1–3. The melting temperatureisa crucial parameter in the search for such materials. For instance, a correlation between a high melting point and elevated temperature strength has been identified in refractory complex concentrated alloys4. Besides the melting temperature, other melting properties, such as enthalpy and entropy of fusion, and volume change at the melting point, are also crucial for constructing phase diagrams and developing novel materials. However, experimental measurements on these properties, even for unary refractory materials, face severe challenges due to high melting points, often resulting in very scattered experimental data, if available at all. Additionally, the vast compositional space of HEAs makes systematic experimental screening of promising candidates impractical. |  | Several computational methods for melting point predictions have been developed using empirical potentials, machine learning potentials, and density functional theory (DFT)5–7. Calculations on other melting properties, such as entropy and enthalpy of fusion, volume expansion from solid to liquid at the melting point, and thermodynamic properties of the liquid phase (especially the liquid heat capacity), are, however, limited. They require access to the free energy surface of both solid and liquid, including all relevant physical contributions, such as vibrational entropy, including the anharmonic contribution, and electronic entropy, including the electron-vibration coupling. These physical contributions significantly impact the thermodynamic properties of both the solid and liquid phases, thereby affecting the predicted melting properties8–10. |  |\n\n1Department for Computational Materials Design, Max Planck Institute for Sustainable Materials, Max-Planck-str. 1, 40237 Düsseldorf, Germany. 2Institute for Materials Science, University of Stuttgart, Pfaffenwaldring 55,","cbCaiheQPKbZ8qMo","https://ap.wps.com/l/cbCaiheQPKbZ8qMo","pdf",1910722,11,"English","# Introduction\n## Motivation for melting-property prediction\n## Limits of experiments for HEAs\n## Computational approaches and gaps\n# Proposed method\n## DFT-based ab initio free energy challenge\n## Machine-learning potential to avoid ab initio MD\n## Free-energy perturbation substitution\n# Application to TaVCrW","[{\"question\":\"Why are melting properties hard to measure experimentally?\",\"answer\":\"Melting temperatures are extremely high for refractory materials, making experiments very challenging and often producing very scattered data or even none.\"},{\"question\":\"What bottleneck makes ab initio free-energy calculations expensive?\",\"answer\":\"They generally require costly thermodynamic integration driven by ab initio molecular dynamics simulations, which makes high-throughput calculations infeasible.\"},{\"question\":\"How does the proposed machine-learning-assisted approach reduce computational cost?\",\"answer\":\"A specially designed machine-learning potential reproduces the ab initio phase-space distribution, allowing thermodynamic integration to be fully substituted by more efficient free-energy perturbation calculations.\"}]","Accelerating ab initio melting property calculations with machine learning: application to the high entropy alloy TaVCrW | PDF",28]