{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:MRE4UZEFNJ7C4HAICW5ZKWZB7G","short_pith_number":"pith:MRE4UZEF","schema_version":"1.0","canonical_sha256":"6449ca64856a7e2e1c0815bb955b21f9946ec11c1bd0c59e814aa22d9abf91b6","source":{"kind":"arxiv","id":"2010.09943","version":1},"attestation_state":"computed","paper":{"title":"A High Accuracy Electrical Stopping Power Prediction Model based on Deep Learning Algorithm and its Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["nucl-ex","physics.comp-ph"],"primary_cat":"physics.app-ph","authors_text":"Hao Wang, Jianming Xue, Ke Jin, Shijun Zhao, Xun Guo","submitted_at":"2020-10-20T00:55:11Z","abstract_excerpt":"Energy loss of energetic ions in solid is crucial in many field, and accurate prediction of the ion stopping power is a long-time goal. Though great efforts have been made, it is still very difficult to find a universal prediction model to accurately calculate the ion stopping power in distinct target materials. Deep learning algorithm is a newly emerged method to solve multi-factors physical problems and can mine the deeply implicit relations among parameters, which make it a powerful tool in energy loss prediction. In this work, we developed an energy loss prediction model based on deep lear"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2010.09943","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.app-ph","submitted_at":"2020-10-20T00:55:11Z","cross_cats_sorted":["nucl-ex","physics.comp-ph"],"title_canon_sha256":"9b473845683808336b6fcb212268ba6f3fdee0283f744c8a29666199152b272c","abstract_canon_sha256":"29135ea8971bec10575e80ce09212248fe9946891761f8ad16fb05ba5e2b564a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:44:25.558152Z","signature_b64":"Cyza9ptIuCb0gwkQhqJBWES2qvkMOkM+yynNmAIGTtSsnBLn0r+o/CVzQx07s6tf8H432QzhhASdbMaBYVMJAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6449ca64856a7e2e1c0815bb955b21f9946ec11c1bd0c59e814aa22d9abf91b6","last_reissued_at":"2026-07-05T01:44:25.557757Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:44:25.557757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A High Accuracy Electrical Stopping Power Prediction Model based on Deep Learning Algorithm and its Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["nucl-ex","physics.comp-ph"],"primary_cat":"physics.app-ph","authors_text":"Hao Wang, Jianming Xue, Ke Jin, Shijun Zhao, Xun Guo","submitted_at":"2020-10-20T00:55:11Z","abstract_excerpt":"Energy loss of energetic ions in solid is crucial in many field, and accurate prediction of the ion stopping power is a long-time goal. Though great efforts have been made, it is still very difficult to find a universal prediction model to accurately calculate the ion stopping power in distinct target materials. Deep learning algorithm is a newly emerged method to solve multi-factors physical problems and can mine the deeply implicit relations among parameters, which make it a powerful tool in energy loss prediction. In this work, we developed an energy loss prediction model based on deep lear"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.09943","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2010.09943/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2010.09943","created_at":"2026-07-05T01:44:25.557823+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.09943v1","created_at":"2026-07-05T01:44:25.557823+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.09943","created_at":"2026-07-05T01:44:25.557823+00:00"},{"alias_kind":"pith_short_12","alias_value":"MRE4UZEFNJ7C","created_at":"2026-07-05T01:44:25.557823+00:00"},{"alias_kind":"pith_short_16","alias_value":"MRE4UZEFNJ7C4HAI","created_at":"2026-07-05T01:44:25.557823+00:00"},{"alias_kind":"pith_short_8","alias_value":"MRE4UZEF","created_at":"2026-07-05T01:44:25.557823+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MRE4UZEFNJ7C4HAICW5ZKWZB7G","json":"https://pith.science/pith/MRE4UZEFNJ7C4HAICW5ZKWZB7G.json","graph_json":"https://pith.science/api/pith-number/MRE4UZEFNJ7C4HAICW5ZKWZB7G/graph.json","events_json":"https://pith.science/api/pith-number/MRE4UZEFNJ7C4HAICW5ZKWZB7G/events.json","paper":"https://pith.science/paper/MRE4UZEF"},"agent_actions":{"view_html":"https://pith.science/pith/MRE4UZEFNJ7C4HAICW5ZKWZB7G","download_json":"https://pith.science/pith/MRE4UZEFNJ7C4HAICW5ZKWZB7G.json","view_paper":"https://pith.science/paper/MRE4UZEF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.09943&json=true","fetch_graph":"https://pith.science/api/pith-number/MRE4UZEFNJ7C4HAICW5ZKWZB7G/graph.json","fetch_events":"https://pith.science/api/pith-number/MRE4UZEFNJ7C4HAICW5ZKWZB7G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MRE4UZEFNJ7C4HAICW5ZKWZB7G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MRE4UZEFNJ7C4HAICW5ZKWZB7G/action/storage_attestation","attest_author":"https://pith.science/pith/MRE4UZEFNJ7C4HAICW5ZKWZB7G/action/author_attestation","sign_citation":"https://pith.science/pith/MRE4UZEFNJ7C4HAICW5ZKWZB7G/action/citation_signature","submit_replication":"https://pith.science/pith/MRE4UZEFNJ7C4HAICW5ZKWZB7G/action/replication_record"}},"created_at":"2026-07-05T01:44:25.557823+00:00","updated_at":"2026-07-05T01:44:25.557823+00:00"}