Integrated Calendar

Machine learning (ML) became a premier tool for modeling chemical processes and materials properties. For instance, ML interatomic potentials have become an efficient alternative to computationally expensive quantum chemistry simulations. In the case of reactive chemistry designing high-quality training data sets is crucial to overall model accuracy. To address this challenge, we develop a general reactive ML interatomic potential through unbiased active learning with an atomic configuration sampler inspired by nanoreactor molecular dynamics. The resulting model is then applied to study five distinct condensed-phase reactive chemistry systems: carbon solid-phase nucleation, graphene ring formation from acetylene, biofuel additives, combustion of methane and the spontaneous formation of glycine from early-earth small molecules. In all cases, the results closely match experiment and/or previous studies using traditional model chemistry methods. Altogether, explosive growth of user-friendly ML frameworks, designed for chemistry, demonstrates that the field is evolving towards physics-based models augmented by data science. I will also overview some applications of Non-adiabatic EXcited-state Molecular Dynamics (NEXMD) framework developed at several institutions. The NEXMD code is able to simulate tens of picoseconds photoinduced dynamics in large molecular systems. As an application, I will exemplify ultrafast coherent excitonic dynamics guided by intermolecular conical intersections. Here X-ray Raman signals are able to sensitively monitor the coherence evolution. The observed coherences have vibronic nature that survives multiple conical intersection passages for several hundred femtoseconds at room temperature. These spectroscopic signals are possible to measure at XFEL facilities and our modeling results allow us to understand and potentially manipulate excited state dynamics and energy transfer pathways toward optoelectronic applications.

References:
1. N. Fedik, R. Zubatyuk, N. Lubbers, J. S. Smith, B. Nebgen, R. Messerly, Y. W. Li, M. Kulichenko, A. I. Boldyrev, K. Barros, O. Isayev, and S. Tretiak “Extending machine learning beyond interatomic potentials for predicting molecular properties” Nature Rev. Chem. 6, 653 (2022).
2. G. Zhou, N. Lubbers, K. Barros, S. Tretiak, B. Nebgen, “Deep Learning of Dynamically Responsive Chemical Hamiltonians with Semi-Empirical Quantum Mechanics,” Proc. Nat. Acad. Sci. USA, 119 e2120333119 (2022)
3. S. Zhang, M. Z. Makos, R. B. Jadrich, E. Kraka, B. T. Nebgen, S. Tretiak, O. Isayev, N. Lubbers, R. A. Messerly, and J. S. Smith “Exploring the frontiers of chemistry with a general reactive machine learning potential,” (2023) https://chemrxiv.org/engage/chemrxiv/article-details/6362d132ca86b84c77ce166c
4. A. De Sio, E. Sommer, X. T. Nguyen, L. Gross, D. Popović, B. Nebgen, S. Fernandez-Alberti, S. Pittalis, C. A. Rozzi, E. Molinari, E. Mena-Osteritz, P. Bäuerle, T. Frauenheim, S. Tretiak, C. Lienau, “Intermolecular conical intersections in molecular aggregates” Nature Nanotech. 16, 63 – 68 (2021).
5. V. M. Freixas, D. Keefer, S. Tretiak, S. Fernandez-Alberti, and S. Mukamel, “Ultrafast coherent photoexcited dynamics in a trimeric dendrimer probed by X-ray stimulated-Raman signals,” Chem. Sci., 13, 6373 – 6384 (2022).