[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122993-en":3,"doc-seo-122993-105":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},122993,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Validation Workflow for Machine Learning Interatomic Potentials for Complex Ceramics - Key Workflow for MLIP Validation","The work addresses the growing need for robust validation of machine learning interatomic potentials (MLIPs), which increasingly approximate potential energy surfaces without the physics-based foundations of traditional potentials. A sequential three-stage workflow is introduced covering preliminary validation, static property prediction, and dynamic property prediction. The material-agnostic method is demonstrated on boron carbide (B4C), a complex ceramic that amorphizes under high-pressure loading, yielding more accurate property predictions than an available empirical potential.","Validation Workflow for Machine Learning Interatomic Potentials  \nfor Complex Ceramics  \nKimia Ghaffari1 , Salil Bavdekar2, Douglas E. Spearot 1 , Ghatu Subhash1, *  \n1 Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL  \n32611 USA  \n2 Department of Materials Science and Engineering, University of Florida, Gainesville, FL  \n32611 USA  \nAbstract  \nThe number of published Machine Learning Interatomic Potentials (MLIPs) has increased significantly in recent years. These new data-driven potential energy approximations often lack the physics-based foundations that inform many traditionally developed interatomic potentials and hence require robust validation methods for their applicability, accuracy, computational efficiency, and transferability to the intended applications. This work presents a sequential, three-stage workflow for MLIP validation: (i) preliminary validation,(ii) static property prediction, and (iii) dynamic property prediction. This material-agnostic procedure is demonstrated in a tutorial approach for the development of a robust MLIP for boron carbide (B4C), a widely employed, structurally complex ceramic that undergoes a deleterious deformation mechanism called ‘amorphization’ under high-pressure loading. It is shown that the resulting B4C MLIP offers a more accurate prediction of properties compared to the available empirical potential.  \nKEYWORDS: boron carbide; neural network; Molecular Dynamics; extreme environments;  \nshock; advanced ceramics; structural ceramics; LAMMPS; DeePMD-kit; tutorial  \n*Corresponding author: G. Subhash ([subhash@ufl.edu](subhash@ufl.edu))  \n1. Introduction  \nAtomistic simulations have unlocked access to nano-level observation and prediction of material behavior. These studies lack many of the physical and fiscal restrictions of their experimental counterparts and hence enable the study of materials before they are synthesized ina laboratory and the analysis of materials subjected to complex boundary conditions. Evaluation of material viability for extreme applications (ballistic, nuclear, aerospace, etc.) is heavily reliant on computer simulations as the necessary temperatures and pressures are difficult to achieve, if not impossible to replicate experimentally. Atomistic simulations such as molecular dynamics (MD) rely on interatomic potentials (IPs), which describe the potential energy surface (PES) of a material and hence can be used to compute interatomic forces under any deformation conditions. An ideal IP must satisfy three main requirements: (i) accuracy,(ii) transferability, and (iii) computational efficiency during runtime. The accuracy of an IP is directly responsible for the ability of a simulation to capture underlying physics, its transferability enables the investigation of more dynamic environments (beyond the trained environment and data) where diverse atomic configurations may be encountered, and its computational efficiency allows for larger and longer simulations at increased speed for observation of behaviors beyond the nanoscale. Unfortunately, traditional IP development methods are often limited in their ability to effectively satisfy the above three requirements.  \nAb initio or quantum mechanics (QM) based methods, such as Density Functional Theory (DFT), model the PES by solving for the electronic structure of a material based on atomic species and their relative positions using an approximation of Schrödinger’s equation. This method can be both accurate and transferable, as it relies on laws of physics to make predictions of material properties and behaviors. Unfortunately, this method comes with a very high  \ncomputational cost, with the largest systems being limited to 1000s of atoms for ps time durations [1] . Empirical or semi-empirical models (Lennard-Jones [2], Embedded-Atom Method [3], Stillinger-Weber [4], etc.) are constructed with a relatively simple functional form and parameters fitted so that the simulatio","cbCaim0okfBaxq2k","https://ap.wps.com/l/cbCaim0okfBaxq2k","pdf",4189356,1,40,"English","en",105,"# Abstract\n# 1. Introduction","[{\"question\":\"Why do published MLIPs require robust validation methods?\",\"answer\":\"MLIPs are data-driven approximations that may lack physics-based foundations, so validation is needed to assess applicability, accuracy, computational efficiency, and transferability to target uses.\"},{\"question\":\"What are the three stages in the proposed MLIP validation workflow?\",\"answer\":\"The workflow includes (i) preliminary validation, (ii) static property prediction, and (iii) dynamic property prediction.\"},{\"question\":\"How is the workflow demonstrated, and what is the key outcome?\",\"answer\":\"It is demonstrated through a tutorial development of a robust MLIP for boron carbide (B4C), a ceramic that amorphizes under high-pressure loading, and the resulting MLIP predicts properties more accurately than an available empirical potential.\"}]","Validation Workflow for Machine Learning Interatomic Potentials for Complex Ceramics - Key Workflow for MLIP Validation | 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do published MLIPs require robust validation methods?","Question",{"text":76,"@type":77},"MLIPs are data-driven approximations that may lack physics-based foundations, so validation is needed to assess applicability, accuracy, computational efficiency, and transferability to target uses.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What are the three stages in the proposed MLIP validation workflow?",{"text":81,"@type":77},"The workflow includes (i) preliminary validation, (ii) static property prediction, and (iii) dynamic property prediction.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the workflow demonstrated, and what is the key outcome?",{"text":85,"@type":77},"It is demonstrated through a tutorial development of a robust MLIP for boron carbide (B4C), a ceramic that amorphizes under high-pressure loading, and the resulting MLIP predicts properties more accurately than an available empirical 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