[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120130-en":3,"doc-seo-120130-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},120130,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Systematic softening in universal machine learning interatomic potentials - Fine-tuning","Supplementary material for addressing systematic softening in universal machine learning interatomic potentials through fine-tuning. The content provides benchmark and diagnostic sections including surface energy evaluation with explicit Miller indices and MPIDs, followed by defect benchmarks, phonon vibrational frequencies, ion migration barrier analysis, and discussion of softening scales. It also compares fine-tuning against training from scratch and reports additional experiments such as linear corrected CHGNet for surface calculation and the effect of model size.","Supplementary Information for overcoming systematic softening in universal machine learning interatomic potentials by  \nfine-tuning  \nCONTENTS  \nI. Surface Benchmark 2  \nII. Defect Benchmark 8  \nIII. Phonon Vibrational Frequencies 13  \nIV. Ion Migration Barriers 21  \nV. Softening scales 21  \nVI. Fine-tuning vs Training from-scratch 21  \nVII. Linear corrected CHGNet for surface calculation 24  \nVIII. Effect of model size 25  \nReferences 26  \nI. SURFACE BENCHMARK  \nThe table below presents all the compounds and surface energy calculations with corresponding Miller indices, as discussed in the surface benchmark section. The surface energy values are in units of eV/˚A2 .  \n\n| mp-id | composition | miller index | M3GNet | CHGNet | MACE | DFT |\n| --- | --- | --- | --- | --- | --- | --- |\n\nmp-126  \nmp-126  \nmp-126  \nmp-126  \nmp-126  \nmp-126  \nmp-1639  \nmp-733  \nmp-733  \nmp-733  \nmp-733  \nmp-1138  \nmp-1138  \nmp-1138  \nmp-1138  \nmp-1138  \nmp-19009  \nmp-19009  \nmp-19009  \nmp-19009  \nmp-19009  \nmp-19399  \nmp-19399  \nmp-19399  \n\n| Pt | [1, 1, 1] | 0.034337 | 0.067933 | 0.108786 |\n| --- | --- | --- | --- | --- |\n| Pt | [2, 2, 1] | 0.041738 | 0.077731 | 0.120055 |\n| Pt | [2, 1, 0] | 0.055588 | 0.087461 | 0.132217 |\n| Pt | [1, 1, 0] | 0.049405 | 0.084198 | 0.129928 |\n| Pt | [2, 1, 1] | 0.044232 | 0.079317 | 0.122664 |\n| Pt | [1, 0, 0] | 0.045627 | 0.080184 | 0.129639 |\n| BN | [1, 1, 0] | 0.093675 | 0.081441 | 0.127251 |\n| GeO2 | [1, 0, 0] | 0.073282 | 0.081616 | 0.079633 |\n| GeO2 | [0, 0, 1] | 0.087457 | 0.018861 | 0.080309 |\n| GeO2 | [1, 0, 1] | 0.066760 | 0.064233 | 0.062573 |\n| GeO2 | [2, 0, 1] | 0.073349 | 0.076060 | 0.083530 |\n| LiF | [2, 1, 0] | 0.030142 | 0.025535 | 0.028741 |\n| LiF | [2, 1, 1] | 0.057645 | 0.047527 | 0.056996 |\n| LiF | [2, 2, 1] | 0.055239 | 0.043816 | 0.052588 |\n| LiF | [1, 1, 0] | 0.040302 | 0.034503 | 0.040202 |\n| LiF | [1, 0, 0] | 0.017387 | 0.013148 | 0.015115 |\n| NiO | [1, 0, 0] | 0.041740 | 0.034537 | 0.078835 |\n| NiO | [1, 1, 0] | 0.083462 | 0.069599 | 0.112546 |\n| NiO | [2, 1, 0] | 0.068462 | 0.054417 | 0.102001 |\n| NiO | [2, 1, 1] | 0.104637 | 0.087699 | 0.126782 |\n| NiO | [2, 2, 1] | 0.108441 | 0.088199 | 0.129272 |\n| Cr2O3 | [2, 1, 1] | 0.088672 | 0.087783 | 0.103788 |\n| Cr2O3 | [0, 0, 1] | 0.074112 | 0.070402 | 0.085364 |\n| Cr2O3 | [1, 0, 0] | 0.091601 | 0.090690 | 0.099994 |\n\n0.094540  \n0.111288  \n0.124666  \n0.124951  \n0.110808  \n0.129515  \n0.173043  \n0.089101  \n0.029946  \n0.079778  \n0.094763  \n0.033885  \n0.067411  \n0.058832  \n0.047889  \n0.019274  \n0.075694  \n0.146961  \n0.116266  \n0.179743  \n0.176103  \n0.137950  \n0.103951  \n0.135110  \nmp-19399  \nmp-19399  \nmp-19399  \nmp-124  \nmp-124  \nmp-124  \nmp-124  \nmp-124  \nmp-124  \nmp-841  \nmp-841  \nmp-841  \nmp-841  \nmp-841  \nmp-1143  \nmp-1143  \nmp-1143  \nmp-1143  \nmp-1143  \nmp-1143  \nmp-30  \nmp-30  \nmp-30  \nmp-30  \nmp-30  \nmp-30  \nmp-1894  \nmp-1894  \nmp-13  \nmp-13  \n\n| Cr2O3 | [2, 1, -2] | 0.100278 | 0.108988 | 0.115123 |\n| --- | --- | --- | --- | --- |\n| Cr2O3 | [2, -1, 2] | 0.100240 | 0.099631 | 0.113920 |\n| Cr2O3 | [2, 0, -1] | 0.083573 | 0.083330 | 0.095162 |\n| Ag | [2, 1, 0] | 0.023318 | 0.041564 | 0.040281 |\n| Ag | [2, 1, 1] | 0.019012 | 0.036769 | 0.035553 |\n| Ag | [1, 1, 1] | 0.014562 | 0.030217 | 0.028861 |\n| Ag | [1, 1, 0] | 0.021264 | 0.039141 | 0.037516 |\n| Ag | [1, 0, 0] | 0.019419 | 0.034914 | 0.034974 |\n| Ag | [2, 2, 1] | 0.018113 | 0.036006 | 0.034438 |\n| Li2O2 | [1, 0, 2] | 0.046675 | 0.029191 | 0.039260 |\n| Li2O2 | [2, 1, 2] | 0.051622 | 0.032755 | 0.033052 |\n| Li2O2 | [1, 0, 0] | 0.086174 | 0.063395 | 0.065709 |\n| Li2O2 | [2, 1, 0] | 0.059645 | 0.038821 | 0.060778 |\n| Li2O2 | [1, 1, 0] | 0.047108 | 0.030317 | 0.044707 |\n| Al2O3 | [1, 0, -2] | 0.086088 | 0.080818 | 0.103740 |\n| Al2O3 | [2, 1, -2] | 0.164204 | 0.146616 | 0.166867 |\n| Al2O3 | [2, 0, -1] | 0.106417 | 0.097795 | 0.116902 |\n| Al2O3 | [1, 0, 0] | 0.111818 | 0.102915 | 0.119333 |\n| Al2O3 | [2, 1, 2] | 0.116533 | 0.104785 | 0.130883 |\n| Al2O3 | [2, -1, 2] | 0.128921 | 0.124779 | 0.133044","cbCaijW3VndZMnbb","https://ap.wps.com/l/cbCaijW3VndZMnbb","pdf",9984052,1,27,"English","en",105,"# Surface Benchmark\n## Defect Benchmark\n## Phonon Vibrational Frequencies\n## Ion Migration Barriers\n## Softening scales\n## Fine-tuning vs Training from-scratch\n## Linear corrected CHGNet for surface calculation\n## Effect of model size\n# References","[{\"question\":\"What problem does the supplementary material focus on?\",\"answer\":\"It focuses on overcoming systematic softening in universal machine learning interatomic potentials using fine-tuning.\"},{\"question\":\"What does the Surface Benchmark section contain?\",\"answer\":\"It presents compounds and surface energy calculations, reporting values in eV/Ų together with Miller indices across multiple models and DFT.\"},{\"question\":\"How does fine-tuning compare to training from scratch in the document?\",\"answer\":\"A dedicated section contrasts fine-tuning with training from scratch to evaluate differences in performance and softening behavior.\"}]","Systematic softening in universal machine learning interatomic potentials - 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