This study explores advanced neural network methodologies for representing multiphase Equations of State (EoS), a fundamental framework in modeling thermodynamic relationships. Researchers developed two innovative approaches—Additive Graph Correction (AGC) and Symplectic Correction (SC)—that enhance the accuracy and consistency of EoS, particularly in capturing phase transitions and adhering to conservation laws. The AGC adjusts energy functions locally, while SC modifies the EoS globally using symplectic geometry principles. Both methods address challenges in traditional analytical models by offering flexibility in handling multiscale systems and phase boundaries. Computational examples, including hydrodynamic simulations, demonstrate the efficacy of these models in improving data fidelity and supporting downstream applications like material science and astrophysics. This work lays a foundation for leveraging machine learning to refine EoS frameworks and optimize their integration into scientific modeling.
For more details, please continue reading the full article under the following link:
https://www.nature.com/articles/s41598-024-81445-4
In general, if you enjoy reading this kind of scientific news articles, I would also be keen to connect with fellow researchers based on common research interests in materials science, including the possibility to discuss about any potential interest in the Materials Square cloud-based online platform ( www.matsq.com ), designed for streamlining the execution of materials and molecular atomistic simulations!
Best regards,
Dr. Gabriele Mogni
Technical Consultant and EU Representative
Virtual Lab Inc., the parent company of the Materials Square platform
Website: Home | Virtual Lab Inc.
Email: gabriele@simulation.re.kr
#materials #materialsscience #materialsengineering #computationalchemistry #modelling #chemistry #researchanddevelopment #research #MaterialsSquare #ComputationalChemistry #Tutorial #DFT #simulationsoftware #simulation