aceInterpretable AI reveals hidden structure in nuclear masses
Abstract Predicting nuclear masses with high precision remains a central challenge in nuclear physics, with implications ranging from stellar nucleosynthesis to determining the boundaries of the nuclear landscape. Approaches using artificial intelligence (AI) have recently achieved remarkable accuracy on this task, but their usefulness is limited by their black-box nature and by the difficulty of assessing their reliability when extrapolating beyond measured nuclei. Here, we present an interpretable AI model tha…
An interpretable AI model matched the best existing nuclear mass prediction precision while revealing physical structure in binding energies, including a rediscovered principle called localized Jaffe factorization.
The source notes that black-box AI models are hard to assess for reliability when extrapolating beyond measured nuclei, which is a general unresolved concern in this field.
Evidence
- Peer-reviewedCommunications AI & Computing2026-10-21
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Truvace Impact Record TRV-2026-2180, v1: “Interpretable AI reveals hidden structure in nuclear masses.” Truvace, 2026-10-22. /record/TRV-2026-2180 (accessed at citation time). sha256 45fe73f42c52d27e…
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