[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119012-en":3,"doc-seo-119012-105":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},119012,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids","Phonons, quantized vibrational modes in crystalline materials, strongly influence thermal and electrical properties, making their prediction central to materials science. This work proposes a deep-learning strategy for universal harmonic interatomic potentials by converting existing phonon datasets—mainly provided as interatomic force constants—into a force-displacement format for training. Using a large publicly available phonon database, the resulting model accurately reproduces full harmonic phonon spectra and computes thermodynamic properties with high precision. Restricting the model to a harmonic energy surface also enables uncertainty assessment for vibrational predictions, supporting subsequent refinement and application.","Portland State University  \nPDXScholar  \n\n| Mechanical and Materials Engineering Faculty Publications and Presentations | Mechanical and Materials Engineering |\n| --- | --- |\n| 3-4-2024\u003Cbr>Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids\u003Cbr>Huiju Lee\u003Cbr>Portland State University\u003Cbr>Yi Xia\u003Cbr>Portland State University, [yxia@pdx.edu](yxia@pdx.edu)\u003Cbr>Follow this and additional works at: [https://pdxscholar.library.pdx.edu/mengin_fac](https://pdxscholar.library.pdx.edu/mengin_fac)\u003Cbr> Part of the Mechanical Engineering Commons\u003Cbr>Let us know how access to this document benefits you. |  |\n\nCitation Details  \nPublished as: Lee, H., & Xia, Y. (2024) . Machine learning a universal harmonic interatomic potential for predicting phonons in crystalline solids. Applied Physics Letters, 124(10) .  \nThis Pre-Print is brought to you for free and open access. It has been accepted for inclusion in Mechanical and Materials Engineering Faculty Publications and Presentations by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [pdxscholar@pdx.edu](pdxscholar@pdx.edu).  \nMachine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids  \nHuiju Lee1 and Yi Xia1  \nDepartment of Mechanical and Materials Engineering, Portland State University, Portland, OR 97201,  \nUSA  \n(*Electronic mail: [yxia@pdx.edu](yxia@pdx.edu))  \n(Dated: 16 February 2024)  \nPhonons, as quantized vibrational modes in crystalline materials, play a crucial role in determining a wide range of physical properties, such as thermal and electrical conductivity, making their study a cornerstone in materials science. In this study, we present a simple yet effective strategy for deep learning harmonic phonons in crystalline solids by leveraging existing phonon databases and state-of-the-art machine learning techniques. The key of our method lies in transforming existing phonon datasets, primarily represented in interatomic force constants, into a force-displacement representation suitable for training machine learning universal interatomic potentials. By applying our approach to one of the largest phonon databases publicly available, we demonstrate that the resultant machine learning universal harmonic interatomic potential not only accurately predicts full harmonic phonon spectra but also calculates key thermodynamic properties with remarkable precision. Furthermore, the restriction to a harmonic potential energy surface in our model provides a way of assessing uncertainty in machine learning predictions of vibrational properties, essential for guiding further improvements and applications in materials science.  \nAll materials are made up of atoms, which vibrate ubiquitously, even at absolute zero temperature, due to quantum effects. When present in periodic solids, these vibrations can be quantized as quasiparticles, known as phonons. The study of phonons is an integral part of solid-state physics and materials science, as they play an essential role in many physical properties of materials, including thermodynamic stability, thermal conductivity, and electric conductivity 1. Specifically, concerning the fundamental thermodynamic properties, phonons are vital for materials' finite-temperature characteristics, such as thermal expansion, heat capacity, free energy, and lattice stability. Phonons also serve as a gene governing energy transfer in solids, manifested in lattice heat transfer 1,2 . Furthermore, when coupled with other quasiparticles, e.g., electrons, phonons can strongly modulate carrier conductivity and give rise to conventional superconductivity3. However, the properties of the materials currently contained in existing databases4–6 are limited to those obtained by relatively simple ground state calculations – formation energies, electronic band-gaps, and -structures, etc. – with no dynamical information such as phonons. The m","cbCairYBD0PubGJC","https://ap.wps.com/l/cbCairYBD0PubGJC","pdf",1338717,1,7,"English","en",105,"# Introduction\n## Phonons and their role in materials properties\n## Data limitations in existing phonon databases\n## ML approaches for phonon-related predictions","[{\"question\":\"Why are phonons important in crystalline materials?\",\"answer\":\"Phonons represent quantized lattice vibrations that determine key physical properties, including thermal and electrical conductivity and thermodynamic stability at finite temperatures.\"},{\"question\":\"What main idea does the study use for building the machine learning potential?\",\"answer\":\"The method transforms existing phonon datasets given in interatomic force constants into a force-displacement representation suitable for training universal interatomic potentials.\"},{\"question\":\"How does the harmonic restriction help the model beyond prediction accuracy?\",\"answer\":\"By limiting the potential energy surface to the harmonic regime, the framework provides a way to assess uncertainty in machine-learning predictions of vibrational properties.\"}]","Machine Learning a Universal Harmonic Interatomic Potential for Predicting Phonons in Crystalline Solids | 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