APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems
Predicting the 3D structures of atomic systems is fundamental to advancing material science and drug discovery. While flow-matching models (, FlowDPO) have recently shown promise in this domain, their performance relies heavily on alignment with ground-truth coordinates via supervised preference learning. However, obtaining experimental labels for novel crystal phases or de novo proteins is prohibitively expensive, creating a bottleneck for structural modeling in data-scarce regimes. In this wor
Record details
Published: 30 July 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment · Healthcare · Biotech
Retrieved: 31 July 2026
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ethics.ai (30 July 2026), “APO: Unsupervised Atomic Policy Optimization for 3D Structure Prediction of Atomic Systems,” evidence record 14931, https://ethics.ai/record/14931 (originally published by arXiv).
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