Title: Adaptive Statistical Thermodynamics of Complex Materials
Year: 2026
Type: preprint
Abstract: This paper presents a novel approach to modeling and predicting the phase transitions and structural stability of complex materials through the application of adaptive statistical thermodynamics. We introduce a dynamic, self-organized disorder field, parameterized by a machine learning algorithm, that evolves to promote stability and facilitates accurate modeling of complex phenomena. The core mechanism centers on the concept of a "Statistical Entropy Field," which dynamically adjusts the statistical properties of the material based on local disorder, ensuring convergence towards desired structural states. This framework integrates advanced machine learning techniques to learn and optimize this field dynamics, offering a robust and adaptable method for predicting phase transitions and structural stability. The development of this method represents a significant advancement in our understanding of complex material behavior and has the potential to significantly enhance computational modeling capabilities within the field of materials science.
Author: Zhang, Jincheng
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7211916690