Optimization of active learning strategies for infrared spectra prediction in catalysis

<jats:p>Infrared (IR) spectroscopy is a key tool for probing catalytic processes, but interpreting experimental spectra can be challenging and often requires guidance from theoretical simulations. In our recent work, we introduced PALIRS [Bhatia et al., npj Comput. Mater., 2025, 11, 324], a Python-based Active Learning Code for Infrared Spectroscopy, employing machine-learned interatomic potentials (MLIPs) to predict IR spectra. PALIRS achieved accuracy comparable to advanced quantum mechanical approaches like ab initio molecular dynamics (AIMD) at a fraction of the computational cost. In this work, we extend PALIRS by systematically comparing MLIP training strategies within the active learning loop: conventional training from-scratch and transfer learning with naive and multihead fine-tuning. While naive fine-tuning fails to improve MLIP performance, multihead fine-tuning achieves an accuracy comparable to that of from-scratch training at substantially lower computational cost. Beyond the comparison of training strategies, PALIRS is extended to predict IR spectra of organic molecules containing H, C, N, and O atoms with up to five carbon atoms, demonstrating its versatility across chemically diverse systems.</jats:p>