Beyond "Mega": Origin of the "Giga" Stokes Shift for Triazolopyridiniums.

Over the past decade, fluorophores that exhibit "mega" Stokes shifts, defined to be Stokes shifts of greater than 100 nm, have gained considerable attention due to their potential technological applications. A subset of these fluorophores have Stokes shifts of at least 10,000 cm-1, for whom we suggest the moniker "giga" Stokes shift. The majority of "giga" Stokes shifts reported in the literature arise from the twisted intramolecular charge transfer mechanism, but this mechanism does not fit empirical characterization of triazolopyridinium (TOP). This observation inspired a density functional theory (DFT) and time-dependent DFT study of TOP, and several related fluorophores, to elucidate the novel photophysical origin for the "giga" Stokes shift of TOP. The resulting computational models revealed that photoexcitation of TOP yields a zwitterionic excited state that undergoes significant structural relaxation prior to emission. Most notably, TOP has two orthogonal moieties in the ground state that adopt a coplanar geometry in the excited state. According to Hückel's rule, both the heterocycle and phenyl moieties of TOP should be aromatic in an orthogonal ground state. However, according to Baird's rule, these individual moieties should be anti-aromatic in the excited state. By relaxing to a coplanar conformation in the excited state, TOP likely forms a single aromatic system consisting of both the heterocycle and phenyl moieties.

Machine Learning in a Molecular Modeling Course for Chemistry, Biochemistry, and Biophysics Students.

Recent advances in computer hardware and software, particularly the availability of machine learning libraries, allow the introduction of data-based topics such as machine learning into the Biophysical curriculum for undergraduate and/or graduate levels. However, there are many practical challenges of teaching machine learning to advanced-level students in the biophysics majors, who often do not have a rich computational background. Aiming to overcome such challenges, we present an educational study, including the design of course topics, pedagogical tools, and assessments of student learning, to develop the new methodology to incorporate the basis of machine learning in an existing Biophysical elective course, and engage students in exercises to solve problems in an interdisciplinary field. In general, we observed that students had ample curiosity to learn and apply machine learning algorithms to predict molecular properties. Notably, feedback from the students suggests that care must be taken to ensure student preparations for understanding the data-driven concepts and fundamental coding aspects required for using machine learning algorithms. This work establishes a framework for future teaching approaches that unite machine learning and any existing course in the biophysical curriculum, while also pinpointing the critical challenges that educators and students will likely face.