Thermodynamics is a science concerning the state of a system, whether it is stable, metastable, or unstable. Its derivatives to natural variables give fundamental physico-chemical properties of the system. It is historically divided into four categories: equilibrium thermodynamics by Gibbs, statistical thermodynamics by Gibbs and Landau, irreversible thermodynamics by Onsager and Prigogine, and quantum mechanics. The development of density function theory (DFT) enabled the quantitative prediction of properties of the ground state of a system from quantum mechanics. Their integration into predictive theories will be discussed in this presentation along with future perspectives. It will be shown that the zentropy theory combines the bottom-up DFT predictions with the revised top-down statistical thermodynamics, while the theory of cross phenomena keeps the entropy production due to irreversible processes in the combine law of thermodynamics to revise the Onsager flux equations. The zentropy theory is capable of quantitatively predicting free energy landscape, singularity and emergent divergences of properties at critical point free of parameters, while the theory of cross phenomena can predict the coefficients of internal processes between non-conjugate variables.
Professor Zi-Kui Liu is the Dorothy Pate Enright Professor at the Department of Materials Science and Engineering, College of Earth and Mineral Science, The Pennsylvania State University. He obtained his BS from Central South University (China), MS from University of Science and Technology Beijing (China), PhD from Royal Institute of Technology (KTH, Sweden). He was a research associate at University of Wisconsin-Madison and a senior research scientist at Questek Innovation, LLC. He has been at the Pennsylvania State University since 1999, the Editor-in-Chief of CALPHAD journal since 2001, and the President of CALPHAD, Inc. since 2013. He co-founded the NSF Center for Computational Materials Design and served its director from 2005 to 2014. Professor Liu coined the name “Materials Genome®” in 2002 and led the incorporation of the nonprofit Materials Genome Foundation in 2018, and his company, Materials Genome, Inc., owns its trademark.
Professor Liu is a Fellow of TMS and ASM International. He served as the President of ASM International and a member of ASM International Board of Trustees and the TMS Board of Directors. He received the ASM J. Willard Gibbs Phase Equilibria Award, the TMS William Hume-Rothery Award, the ACers Spriggs Phase Equilibria Award, the Wilson Award for Excellence in Research from the Pennsylvania State University, and the Lee Hsun Award from Institute of Metals Research, Chinese Academy of Science. Professor Liu’s current research activities are centered on (1) DFT-based first-principles calculations and deep neural network machine learning for prediction and modeling of materials properties through integration of quantum, statistical, and irreversible thermodynamics, and (2) their applications for designing and tailoring materials chemistry, processing, and performances. His team developed the zentropy theory that is capable of predicting macroscopic functionalities including singularity from quantum mechanics and statistical mechanics based solely on density functional theory. Professor Liu derived the general flux equations and predicted the cross-phenomenon coefficients from the complete form of combine law of thermodynamics. He has graduated 31 PhD students and published over 600+ papers collected in Web of Science and in Google Scholar. He was the lead author of a textbook on Computational Thermodynamics of Materials published by Cambridge University Press. His research group web site is at www.phases.psu.edu.
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
