Quantitative predictive theories through integrating quantum, statistical, equilibrium, and nonequilibrium thermodynamics.

J Phys Condens Matter

Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA 16802, United States of America.

Published: May 2024

Today's thermodynamics is largely based on the combined law for equilibrium systems and statistical mechanics derived by Gibbs in 1873 and 1901, respectively, while irreversible thermodynamics for nonequilibrium systems resides essentially on the Onsager Theorem as a separate branch of thermodynamics developed in 1930s. Between them, quantum mechanics was invented and quantitatively solved in terms of density functional theory (DFT) in 1960s. These three scientific domains operate based on different principles and are very much separated from each other. In analogy to the parable of the blind men and the elephant articulated by Perdew, they individually represent different portions of a complex system and thus are incomplete by themselves alone, resulting in the lack of quantitative agreement between their predictions and experimental observations. Over the last two decades, the author's group has developed a multiscale entropy approach (recently termed as zentropy theory) that integrates DFT-based quantum mechanics and Gibbs statistical mechanics and is capable of accurately predicting entropy and free energy of complex systems. Furthermore, in combination with the combined law for nonequilibrium systems presented by Hillert, the author developed the theory of cross phenomena beyond the phenomenological Onsager Theorem. The zentropy theory and theory of cross phenomena jointly provide quantitative predictive theories for systems from electronic to any observable scales as reviewed in the present work.

Download full-text PDF

Source
http://dx.doi.org/10.1088/1361-648X/ad4762DOI Listing

Publication Analysis

Top Keywords

quantitative predictive
8
predictive theories
8
combined law
8
statistical mechanics
8
nonequilibrium systems
8
onsager theorem
8
quantum mechanics
8
zentropy theory
8
theory cross
8
cross phenomena
8

Similar Publications

Background: Benign and malignant breast tumors differ in their microvasculature morphology and distribution. Histologic biomarkers of malignant breast tumors are also correlated with the microvasculature. There is a lack of imaging technology for evaluating the microvasculature.

View Article and Find Full Text PDF

This study aimed to develop a Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression (LR) model using quantitative imaging features from Shear Wave Elastography (SWE) and Contrast-Enhanced Ultrasound (CEUS) to assess the malignancy risk of BI-RADS 4 breast lesions (BLs). The features predictive of malignancy in the LASSO analysis were used to construct a nomogram. Female patients (n = 111) with BI-RADS 4 BLs detected via routine ultrasound at Ma'anshan People's Hospital underwent SWE, CEUS, and histopathological examinations were enrolled in this study.

View Article and Find Full Text PDF

Topological indices (TIs) of chemical graphs of drugs hold the potential to compute important properties and biological activities leading to more thoughtful drug design. Here, we considered certain drugs treating eye-related disorders, including cataract, glaucoma, diabetic retinopathy, and macular degeneration. By combining modeling and decision-makings approaches, this study presents a cost-effective way to comprehend the behavior of molecules.

View Article and Find Full Text PDF

The q-RASPR approach for predicting the property and fate of persistent organic pollutants.

Sci Rep

January 2025

Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, via Mario Negri 2, Milano, 20156, Italy.

This study presents a quantitative read-across structure-property relationship (q-RASPR) approach that integrates the chemical similarity information used in read-across with traditional quantitative structure-property relationship (QSPR) models. This novel framework is applied to predict the physicochemical properties and environmental behaviors of persistent organic pollutants, specifically polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs). By utilizing a curated dataset and incorporating similarity-based descriptors, the q-RASPR approach improves the accuracy of predictions, particularly for compounds with limited experimental data.

View Article and Find Full Text PDF

Biopsy location and tumor-associated macrophages in predicting malignant glioma recurrence using an in-silico model.

NPJ Syst Biol Appl

January 2025

Center for Interdisciplinary Digital Sciences (CIDS), Department Information Services and High-Performance Computing (ZIH), Dresden University of Technology, 01062, Dresden, Germany.

Predicting the biological behavior and time to recurrence (TTR) of high-grade diffuse gliomas (HGG) after maximum safe neurosurgical resection and combined radiation and chemotherapy plays a pivotal role in planning clinical follow-up, selecting potentially necessary second-line treatment and improving the quality of life for patients diagnosed with a malignant brain tumor. The current standard-of-care (SoC) for HGG includes follow-up neuroradiological imaging to detect recurrence as early as possible and relies on several clinical, neuropathological, and radiological prognostic factors, which have limited accuracy in predicting TTR. In this study, using an in-silico analysis, we aim to improve predictive power for TTR by considering the role of (i) prognostically relevant information available through diagnostics used in the current SoC, (ii) advanced image-based information not currently part of the standard diagnostic workup, such as tumor-normal tissue interface (edge) features and quantitative data specific to biopsy positions within the tumor, and (iii) information on tumor-associated macrophages.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!