Berlin 2024 – wissenschaftliches Programm

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CPP: Fachverband Chemische Physik und Polymerphysik

CPP 34: Emerging Topics in Chemical and Polymer Physics, New Instruments and Methods I

CPP 34.3: Vortrag

Donnerstag, 21. März 2024, 10:15–10:30, H 0106

Deciphering Electron Paramagnetic Resonance Spectra via Machine Learning — Shengchun Wang¹, Shufei Zhang², JIhu Su⁵, •Yi Luo³, and Aiwen Lei⁴ — ¹Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA — ²Shanghai Artificial Intelligence Laboratory, Shanghai, China — ³Institute of Functional Interfaces, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany — ⁴College of Chemistry and Molecular Sciences, the Institute for Advanced Studies, Wuhan University, Wuhan , China — ⁵Department of Modern Physics, University of Science and Technology of China, Hefei, China

Elucidating the properties of spin species is one of central issues in modern chemistry, material and biology science. For understanding these species, the rapid identification of isotropic and anisotropic Electron Paramagnetic Resonance (EPR) spectra is foundational. Conventional simulation methods, though detailed, often lag in efficiency. Here we present a hybrid approach integrating conventional computational method and Multi-Layer Perceptron (MLP) algorithms, leverages an extensive literature-derived EPR database, ensuring both speed and accuracy in species identification from EPR spectra. Evaluations validate its superior efficacy, attributing much to its robust database integration. This tool offers a promising bridge between academic rigor, computational efficiency, and EPR literature.

Keywords: Machine Learning; Spectrum Identification; Electron Paramagnetic Resonance