We explore the range of applicability of the nuclear ensemble method (NEM) for quantitative simulations of absorption spectra and their temperature variations. We formulate a "good practice" for the NEM based on statistical theory. Special attention is paid to proper treatment of uncertainty estimation including the convergence with the number of samples, which is often neglected in the field. As a testbed, we have selected a well-known chromophore, ()-azobenzene. We measured its temperature difference UV-vis absorption spectra in methanol, which displayed two dominant features: a moderate increase in the intensity of the nπ* band and a pronounced decrease in intensity of the low-energy part of the ππ* band. We attributed both features to increasing non-Condon effects with temperature. We show that the NEM based on the path integral molecular dynamics combined with range-separated hybrid functionals provides quantitatively accurate spectra and their differences. Experimentally, the depletion of the absorption in the ππ* band showed a characteristic vibrational progression that cannot be reproduced with the NEM. We show that hundreds of thousands of samples are necessary to achieve an accuracy sufficient for the unambiguous explanation of the observed temperature effects. We provide a detailed analysis of the temperature effects on the spectrum based on the harmonic model of the system combined with the NEM. We also rationalize the vibrational structure of the spectrum using the Franck-Condon principle.
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http://dx.doi.org/10.1021/acs.jctc.0c00579 | DOI Listing |
Biomed Phys Eng Express
January 2025
School of Engineering and Computing, University of the West of Scotland, University of the West of Scotland - Paisley Campus, Paisley PA1 2BE, UK, City, Paisley, PA1 2BE, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Cancer grade classification is a challenging task identified from the cell structure of healthy and abnormal tissues. The partitioner learns about the malignant cell through the grading and plans the treatment strategy accordingly. A major portion of researchers used DL models for grade classification.
View Article and Find Full Text PDFSci Total Environ
January 2025
Center for Environmental Radioactivity (CERAD) CoE, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway; Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences (NMBU), P.O.Box 5003, NO-1432 Ås, Norway.
Numerical transport models are important tools for nuclear emergency decision makers in that they rapidly provide early predictions of dispersion of released radionuclides, which is key information to determine adequate emergency protective measures. They can also help us understand and describe environmental processes and can give a comprehensive assessment of transport and transfer of radionuclides in the environment. Transport of radionuclides in air and ocean is affected by a number of different physico-chemical processes.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark.
Background/objectives: Brown adipose tissue (BAT) plays a crucial role in energy expenditure and thermoregulation and has thus garnered interest in the context of metabolic diseases. Segmentation in medical imaging is time-consuming and prone to inter- and intra-operator variability. This study aims to develop an automated BAT segmentation method using the nnU-Net deep learning framework, integrated into the TotalSegmentator software, and to evaluate its performance in a large cohort of patients with lymphoma.
View Article and Find Full Text PDFCellular chromatin displays heterogeneous structure and dynamics, properties that control diverse nuclear processes. Models invoke phase separation of conformational ensembles of chromatin fibers as a mechanism regulating chromatin organization . Here we combine biochemistry and molecular dynamics simulations to examine, at single base-pair resolution, how nucleosome spacing controls chromatin phase separation.
View Article and Find Full Text PDFSci Rep
January 2025
Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.
Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil - nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs.
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