The mixed quantum-classical dynamical approaches have been widely used to study nonadiabatic phenomena in photochemistry and photobiology, in which the time evolutions of the electronic and nuclear subsystems are treated based on quantum and classical mechanics, respectively. The key issue is how to deal with coherence and decoherence during the propagation of the two subsystems, which has been the subject of numerous investigations for a few decades. A brief description on Ehrenfest mean-field and surface-hopping (SH) methods is first provided, and then different algorithms for treatment of quantum decoherence are reviewed in the present paper. More attentions were paid to quantum trajectory mean-field (QTMF) method under the picture of quantum measurements, which is able to overcome the overcoherence problem. Furthermore, the combined QTMF and SH algorithm is proposed in the present work, which takes advantages of the QTMF and SH methods. The potential to extend the applicability of the QTMF method was briefly discussed, such as the generalization to other type of nonadiabatic transitions, the combination with multiscale computational models, and possible improvements on its accuracy and efficiency by using machine-learning techniques.
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http://dx.doi.org/10.1021/acs.jpca.9b03480 | DOI Listing |
Crit Care
January 2025
Department of Anesthesiology and Critical Care Medicine, Yokohama City University School of Medicine, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.
Background: Both quantitative and qualitative aspects of muscle status significantly impact clinical outcomes in critically ill patients. Comprehensive monitoring of baseline muscle status and its changes is crucial for risk stratification and management optimization. However, repeatable and accessible indicators are lacking.
View Article and Find Full Text PDFComput Biol Chem
January 2025
Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
The current study focuses on the potential of second-generation antihistamines, which exhibit fewer side effects compared to first-generation drugs, to block the Histamine H receptor (HR) and mitigate allergic responses. We screened several derivatives of second-generation drugs taking Desloratadine (Deslo) and Acrivastine (Acra) as seed compounds. We performed molecular docking, drug-likeness, quantum chemical calculations, UV-visible and infrared spectroscopy, molecular electrostatic potential (MEP) mapping for understanding drug derivatives potential as efficient drugs and molecular dynamics (MD).
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
BIFOLD─Berlin Institute for the Foundations of Learning and Data, 10587 Berlin, Germany.
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modeling such as stable molecular dynamics (MD). To go beyond accuracy, we use explainable artificial intelligence (XAI) techniques to develop a general analysis framework for atomic interactions and apply it to the SchNet and PaiNN neural network models. We compare these interactions with a set of fundamental chemical principles to understand how well the models have learned the underlying physicochemical concepts from the data.
View Article and Find Full Text PDFAcc Chem Res
January 2025
Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, United Kingdom.
ConspectusPhotochemical reactions have always been the source of a great deal of mystery. While classified as a type of chemical reaction, no doubts are allowed that the general tenets of ground-state chemistry do not directly apply to photochemical reactions. For a typical chemical reaction, understanding the critical points of the ground-state potential (free) energy surface and embedding them in a thermodynamics framework is often enough to infer reaction yields or characteristic time scales.
View Article and Find Full Text PDFJ Chem Phys
January 2025
Department of Chemistry, University of Rochester, Rochester, New York 14627, USA.
We outline two general theoretical techniques to simulate polariton quantum dynamics and optical spectra under the collective coupling regimes described by a Holstein-Tavis-Cummings (HTC) model Hamiltonian. The first one takes advantage of sparsity of the HTC Hamiltonian, which allows one to reduce the cost of acting polariton Hamiltonian onto a state vector to the linear order of the number of states, instead of the quadratic order. The second one is applying the well-known Chebyshev series expansion approach for quantum dynamics propagation and to simulate the polariton dynamics in the HTC system; this approach allows us to use a much larger time step for propagation and only requires a few recursive operations of the polariton Hamiltonian acting on state vectors.
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