High Magnetic Field Detunes Vibronic Resonances in Photosynthetic Light Harvesting

Direct Probe of Conical Intersection Photochemistry by Time-Resolved X-ray Magnetic Circular Dichroism

The direct probing of photochemical dynamics by detecting the electronic coherence generated during passage through conical intersections is an intriguing challenge. The weak coherence signal and the difficulty in preparing purely excited wave packets that exclude coherence from other sources make it experimentally challenging. We propose to use time-resolved X-ray magnetic circular dichroism to probe the wave packet dynamics around the conical intersection. The magnetic field amplifies the relative strength of the electronic coherence signal compared to populations through the magnetic field response anisotropy. More importantly, since the excited state relaxation through conical intersections involves a change of parity, the magnetic coupling matches the symmetry of the response function with the electronic coherence, making the coherence signal only sensitive to the conical intersection induced coherence and excludes the pump pulse induced coherence between the ground state and excited state. In this theoretical study, we apply this technique to the photodissociation dynamics of a pyrrole molecule and demonstrate its capability of probing electronic coherence at a conical intersection as well as population transfer. We demonstrate that a magnetic field can be effectively used to extract novel information about electron and nuclear molecular dynamics.

Infrared Signatures of Phycobilins within the Phycocyanin 645 Complex

Aquatic photosynthetic organisms evolved to use a variety of light frequencies to perform photosynthesis. Phycobiliprotein phycocyanin 645 (PC645) is a light-harvesting complex in cryptophyte algae able to transfer the absorbed green solar light to other antennas with over 99% efficiency. The infrared signatures of the phycobilin pigments embedded in PC645 are difficult to access and could provide useful information to understand the mechanism behind the high efficiency of energy transfer in PC645. We use visible-pump IR-probe and two-dimensional electronic vibrational spectroscopy to study the dynamical evolution and assign the fingerprint mid-infrared signatures to each pigment in PC645. Here, we report the pigment-specific vibrational markers that enable us to track the spatial flow of excitation energy between the phycobilin pigment pairs. We speculate that two high-frequency modes (1588 and 1596 cm^-1) are involved in the vibronic coupling leading to fast (<ps) and direct energy transfer from the highest to lowest exciton, bypassing the intermediate excitons.

Harnessing deep reinforcement learning to construct time-dependent optimal fields for quantum control dynamics

We present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in dynamical chemical systems. Our DRL approach gives impressive performance in constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in quantum dynamical chemical systems.

Role of the Exchange Interactions in the Stability of the Cellulose

The problem of the origin of biochirality and the related problem of the initial monomers' selection are still under discussion, and the main point here is not the mechanics of...

Coherent Speedup of Excitation Energy Transfer in PC645

The fundamental principle governing the robust excitation energy transfer (EET) in the light-harvesting complexes remained elusive. Recent spectroscopic observations of the EET attract attention toward the role of the quantum effects in the biological systems. In this study, along with the time evolution, the distance between the quantum states of the pigments is also considered for a detailed illustration of the effect of the quantum coherence on the speed of the excitation transfer. The comparative analysis of the coherently delocalized EET with the incoherent discrete hopping reveals that the coherent superposition speedup the excitation transfer process, for instance, with the initially localized excitation, coherence provides a ∼4-fold enhancement to the excitation transfer rate.

Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields

Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, E(t), that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches: (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of E(t)), and (2) a cross-correlation neural network approach for directly predicting E(t) in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models. From these results, we show that machine learning, particularly deep neural networks, can be employed as cost-effective statistical approaches for designing electromagnetic fields to enable desired transitions in these quantum dynamical systems.

Ring currents modulate optoelectronic properties of aromatic chromophores at 25 T

The properties of organic molecules can be influenced by magnetic fields, and these magnetic field effects are diverse. They range from inducing nuclear Zeeman splitting for structural determination in NMR spectroscopy to polaron Zeeman splitting organic spintronics and organic magnetoresistance. A pervasive magnetic field effect on an aromatic molecule is the aromatic ring current, which can be thought of as an induction of a circular current of π-electrons upon the application of a magnetic field perpendicular to the π-system of the molecule. While in NMR spectroscopy the effects of ring currents on the chemical shifts of nearby protons are relatively well understood, and even predictable, the consequences of these modified electronic states on the spectroscopy of molecules has remained unknown. In this work, we find that photophysical properties of model phthalocyanine compounds and their aggregates display clear magnetic field dependences up to 25 T, with the aggregates showing more drastic magnetic field sensitivities depending on the intermolecular interactions with the amplification of ring currents in stacked aggregates. These observations are consistent with ring currents measured in NMR spectroscopy and simulated in time-dependent density functional theory calculations of magnetic field-dependent phthalocyanine monomer and dimer absorption spectra. We propose that ring currents in organic semiconductors, which commonly comprise aromatic moieties, may present new opportunities for the understanding and exploitation of combined optical, electronic, and magnetic properties.

Heterogeneous CPU+GPU-Enabled Simulations for DFTB Molecular Dynamics of Large Chemical and Biological Systems

We introduce a new heterogeneous CPU+GPU-enhanced DFTB approach for the routine and efficient simulation of large chemical and biological systems. Compared to homogeneous computing with conventional CPUs, heterogeneous computing approaches exhibit substantial performance with only a modest increase in power consumption, both of which are essential to upcoming exascale computing initiatives. We show that DFTB-based molecular dynamics is a natural candidate for heterogeneous computing, since the computational bottleneck in these simulations is the diagonalization of the Hamiltonian matrix, which is performed several times during a single molecular dynamics trajectory. To thoroughly test and understand the performance of our heterogeneous CPU+GPU approach, we examine a variety of algorithmic implementations, benchmarks of different hardware configurations, and applications of this methodology on several large chemical and biological systems. Finally, to demonstrate the capability of our implementation, we conclude with a large-scale DFTB MD simulation of explicitly solvated HIV protease (3974 atoms total) as a proof-of-concept example of an extremely large/complex system which, to the best of our knowledge, is the first time that an entire explicitly solvated protein has been treated at a quantum-based MD level of detail.