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It is a long-standing open question in quantum complexity theory whether the definition of non-deterministic quantum computation requires quantum witnesses (

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).

Recent text-to-image diffusion models are able to generate convincing results of unprecedented quality. However, it is nearly impossible to control the shapes of different regions/objects or their layout in a fine-grained fashion. Previous attempts to provide such controls were hindered by their reliance on a fixed set of labels. To this end, we present SpaText - a new method for text-to-image generation using open-vocabulary scene control. In addition to a global text prompt that describes the entire scene, the user provides a segmentation map where each region of interest is annotated by a free-form natural language description. Due to lack of large-scale datasets that have a detailed textual description for each region in the image, we choose to leverage the current large-scale text-to-image datasets and base our approach on a novel CLIP-based spatio-textual representation, and show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-based. In addition, we show how to extend the classifier-free guidance method in diffusion models to the multi-conditional case and present an alternative accelerated inference algorithm. Finally, we offer several automatic evaluation metrics and use them, in addition to FID scores and a user study, to evaluate our method and show that it achieves state-of-the-art results on image generation with free-form textual scene control.

The global oceanic overturning circulation and the transport of heat and dissolved gases are strongly controlled by upper ocean turbulent mixing that is driven by the breaking of internal gravity waves (IWs). Understanding the life cycle of oceanic IWs, from generation to dissipation, is therefore crucial for improving the representation of ocean mixing in climate models, which do not resolve the IW field.  Oceanic IWs are observed to have a continuous energy distribution across spatial and temporal scales – an internal wave continuum – despite being forced primarily at near-inertial and tidal frequencies at large scales. The formation of the IW continuum and the associated energy transfer to dissipative scales have been traditionally attributed to wave-wave interactions and to Doppler shifting of wave frequencies by currents. Here, we provide evidence from realistic numerical simulations that oceanic eddies rapidly diffuse storm-forced wave energy across spatiotemporal scales, thereby playing a dominant role in the formation of the IW continuum and the corresponding spatiotemporal distribution of energy dissipation. We further demonstrate that winds can play an important role in damping oceanic IWs through current feedback.  This results in a substantial reduction in wind power input at near inertial frequencies and a net energy sink for internal tides.

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Introduction: Bianca Jones Marlin, Ph.D. is a neuroscientist and Herbert and Florence Irving Assistant Professor of Cell Research at the Zuckerman Institute at Columbia University in New York City. Her research investigates how organisms unlock innate behaviors at appropriate times, and how learned information is passed to subsequent generations via transgenerational epigenetic inheritance. Dr. Marlin combines neural imaging, behavior, and molecular genetics to uncover how learned behavior in the parent can become innate behavior in the offspring— work that promises to make a profound impact on societal brain health, mental well-being, and parenting. For more information about Dr. Marlin, visit www.biancajonesmarlin.com