Thermodynamics and its prediction and CALPHAD modeling: Review, state of the art, and perspectives

ZK Liu - Calphad, 2023 - Elsevier
Thermodynamics is a science concerning the state of a system, whether it is stable,
metastable, or unstable, when interacting with its surroundings. The combined law of …

Uncertainty reduction and quantification in computational thermodynamics

R Otis - Computational Materials Science, 2022 - Elsevier
Uncertainty quantification is an important part of materials science and serves a role not only
in assessing the accuracy of a given model, but also in the rational reduction of uncertainty …

Ab initio simulations on the pure Cr lattice stability at 0K: Verification with the Fe-Cr and Ni-Cr binary systems

S Yang, Y Wang, ZK Liu, Y Zhong - Calphad, 2021 - Elsevier
Significant discrepancies have been observed and discussed on the lattice stability of Cr
between the predictions from the ab initio calculations and the CALPHAD approach. In the …

Automated assessment of a kinetic database for fcc Co–Cr–Fe–Mn–Ni high entropy alloys

K Abrahams, S Zomorodpoosh… - … and Simulation in …, 2021 - iopscience.iop.org
The development of accurate kinetic databases by parametrizing the composition and
temperature dependence of elemental atomic mobilities, is essential for correct …

Generalized method of sensitivity analysis for uncertainty quantification in Calphad calculations

N Ury, R Otis, V Ravi - Calphad, 2022 - Elsevier
Abstract Calculation of Phase Diagrams (Calphad) is a method of using thermodynamic
models obtained from experimental data to perform thermodynamic calculations. The next …

Phase-field approach to simulate BCC-B2 phase separation in the AlnCrFe2Ni2 medium-entropy alloy

YA Coutinho, A Kunwar, N Moelans - Journal of Materials Science, 2022 - Springer
Phase separation is a relevant mode of transformation for microstructure development in
multicomponent alloys. Its occurrence can drastically alter the composition landscape and …

Efficient thermodynamic model optimization and uncertainty quantification via integration of combinatorial materials chip and Bayesian approach

H Zhang, B Wu, C Xia, L Zhang, H Wang - Scripta Materialia, 2024 - Elsevier
Combinatorial materials chips (CMC) are composition spread thin-films deposited on a
substrate that can rapidly determine the compositional-temperature information of ternary …

Analytically differentiable metrics for phase stability

C Kunselman, B Bocklund, A van de Walle, R Otis… - Calphad, 2024 - Elsevier
In this work, a long-established but sparsely documented method of obtaining semi-analytic
derivatives of thermodynamic properties with respect to equilibrium conditions is briefly …

[图书][B] Artificial Intelligence-aided Materials Design: AI-algorithms and Case Studies on Alloys and Metallurgical Processes

R Jha, BK Jha - 2022 - taylorfrancis.com
This book describes the application of artificial intelligence (AI)/machine learning (ML)
concepts to develop predictive models that can be used to design alloy materials, including …

[图书][B] Computational design of additively manufactured functionally graded materials by thermodynamic modeling with uncertainty quantification

B Bocklund - 2021 - search.proquest.com
Thermodynamics is language for describing the equilibrium and non-equilibrium states of
the microscopic and macroscopic systems that make up our universe. In materials science …