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Raman spectroscopy is an important tool in the study of vibrational properties and composition of molecules, peptides and even proteins. Raman spectra can be simulated based on the change of the electronic polarizability with vibrations, which can nowadays be efficiently obtained via machine learning models trained on first-principles data. However, the transferability of the models trained on small molecules to larger structures is unclear and direct training on large structures in prohibitively expensive. In this work, we first train two machine learning models to predict polarizabilities of all 20 amino acids. Both models are carefully benchmarked and compared to DFT calculations, with neural network method found to offer better transferability. By combining machine learning models with classical force field molecular dynamics, Raman spectra of all amino acids are also obtained and investigated, showing good agreement with experiments. The models are further extended to small peptides. We find that adding structures containing peptide bonds to the training set greatly improves predictions even for peptides not included in training sets.

Discusses various issues related to gay and lesbian students and art education. These include the harassment, intimidation, and alienation experienced by gay and lesbian students. Briefly covers the creation of gay/lesbian teacher and student organizations and their attempts to address these problems. (MJP)

Contends that using art textbooks is a relatively new trend in art education. Reports on a survey about art textbook use among 64 art teachers involved in an in-service teacher education program. Concludes it is important that the art education field encourages, promotes, and supports research on the use of art textbooks. (CFR)