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arXiv Machine Learning By Yi Jiang, Letian Chen, Runhan Shi, Lea Bae, Zheng Ling, Dong Ha Kim, Tong Zhu, Yang Yang

Parity-equivariant prediction of conformer-resolved electronic circular dichroism spectra accelerates absolute configuration assignment

Read the original on arXiv Machine Learning →

The paper introduces PhysECD, a parity-aware E(3)-equivariant machine learning framework that predicts electronic circular dichroism (ECD) spectra from conformer geometries in milliseconds. It builds spectra from underlying quantum‑chemical quantities to enforce correct chiroptical symmetry. The authors also present SCECD, a large conformer‑resolved dataset of 578,104 computed spectra, and demonstrate that PhysECD achieves state‑of‑the‑art accuracy on benchmarks and matches experimental spectra, offering a fast alternative to TDDFT for absolute configuration assignment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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