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References from Existence domains for various invariant reactions in FCC binary phase diagrams involving two phases: A quasi-chemical — regular solution approach. Local targets link to admitted publications; unresolved targets remain external evidence.
Die Schmelz-oder Erstarrungskurven bei binären Systemen, wenn die feste Phase ein Gemisch (amorphe feste Lösung oder Mischkristalle) der beiden Komponenten ist
10.1515/zpch-1908-6314 · 1908 · External reference
Melting or solidification curves in binary system
10.1515/zpch-1908-6314 · 1908 · External reference
Phase diagrams
10.1016/0079-6786(75)90004-7 · 1975 · External reference
Unresolved reference
1981 · External reference
Unresolved reference
1993 · External reference
Thermodynamics of metallic solutions
1997 · External reference
Existence domains for invariant reactions in binary regular solution phase diagrams exhibiting two phases
10.1007/bf02711187 · 2003 · External reference
Calculation of existence domains and optimized phase diagram for the Nb-Ti binary alloy system using computational methods
10.1007/s11669-020-00843-z · 2020 · External reference
A theory of cooperative phenomena
10.1103/physrev.81.988 · 1951 · External reference
The cluster/site approximation for multicomponent solutions - a practical alternative to the cluster variation method
10.1016/1359-6462(96)00198-4 · 1996 · External reference
Quasi-chemical method in the statistical theory of regular mixtures
10.1103/physrev.76.972 · 1949 · External reference
A generalization of the quasi - chemical method in the statistical theory of superlattices
10.1063/1.1724001 · 1945 · External reference
A cluster-based computational thermodynamics framework with intrinsic chemical short-range order: Part I. Configurational contribution
10.1016/j.actamat.2024.120138 · 2024 · External reference
Approximate solutions to the cluster variation free energies by the variable basis cluster expansion
10.1016/j.commatsci.2016.05.035 · 2016 · External reference
Polynomial functions for configurational correlation functions in gibbs energies of solid solutions using cluster variation method
10.1016/j.commatsci.2020.109746 · 2021 · External reference
Optimization of phase diagrams by a least squares method using simultaneously different types of data
10.1016/0364-5916(77)90002-5 · 1977 · External reference
Optimization of binary thermodynamic and phase diagram data
10.1007/bf02670872 · 1983 · External reference
Unresolved reference
1952 · External reference
Unresolved reference
2007 · External reference
Methods of approximation in the theory of regular mixtures
10.1098/rspa.1953.0005 · 1953 · External reference
Atomic ordering
2005 · External reference
Thermodynamics of binary bcc and fcc phases for exclusive second-neighbour pair interactions using cluster variation method: Analytical solutions
10.1007/s12666-021-02469-2 · 2022 · External reference
The Cu-Ir (copper-iridium) system
10.1007/bf02873198 · 1987 · External reference
SGTE data for pure elements
10.1016/0364-5916(91)90030-n · 1991 · External reference
Computational thermodynamics of Sc-Zr and Sc-Ti alloys using cluster variation method
2009 · External reference
Unresolved reference
2000 · External reference
Unresolved reference
2007 · External reference
Estimation of CE-CVM energy parameters from miscibility gap data
10.1007/bf02704237 · 2005 · External reference
Unresolved reference
2008 · External reference
ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: Application to Cu-Mg
10.1557/mrc.2019.59 · 2019 · External reference
Machine-learned interatomic potentials for alloys and alloy phase diagrams
10.1038/s41524-020-00477-2 · 2021 · External reference
Accelerating CALPHAD-based phase diagram predictions in complex alloys using universal machine learning potentials: Opportunities and challenges
10.1016/j.actamat.2025.120747 · 2025 · External reference
A data-driven approach to approximate the correlation functions in cluster variation method
10.1088/1361-651x/ac3a16 · 2022 · External reference
AI methods in materials design, discovery and manufacturing: A review
10.1016/j.commatsci.2024.112793 · 2024 · External reference
Exploring design space: Machine learning for multi-objective materials design optimization with enhanced evaluation strategies
10.1016/j.commatsci.2024.113432 · 2025 · External reference
Design of Ni-based single crystal superalloys by machine learning based on data-driven multi-task optimization
10.1016/j.commatsci.2025.113969 · 2025 · External reference
Importance of input data normalization for the application of neural networks to complex industrial problems
10.1109/23.589532 · 1997 · External reference
Modeling of thermotransport phenomenon in metal alloys using artificial neural networks
10.1016/j.apm.2012.06.018 · 2013 · External reference
An artificial neural network model to predict the thermal properties of concrete using different neurons and activation functions
10.1155/2019/3831813 · 2019 · External reference
Recent advances and applications of machine learning in solid-state materials science
10.1038/s41524-019-0221-0 · 2019 · External reference
Pytorch: An imperative style, high-performance deep learning library
2019 · External reference
Phase diagram determination using machine learning methods in materials science and engineering: a review
10.1080/14786435.2025.2558154 · 2026 · External reference
Understanding the difficulty of training deep feedforward neural networks
2010 · External reference
Activation functions in deep learning: A comprehensive survey and benchmark
10.1016/j.neucom.2022.06.111 · 2022 · External reference
A first-principles and CALPHAD-assisted phase-field model for microstructure evolution: Application to mo-v binary alloy systems
10.1016/j.matdes.2023.112443 · 2023 · External reference
An explicit integration approach for predicting the microstructures of multicomponent alloys
10.1038/s41467-025-61246-7 · 2025 · External reference
The synergy of machine learning and CALPHAD: Revitalizing traditional approaches
10.1016/j.commatsci.2025.113970 · 2025 · External reference
GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation
10.1016/j.commatsci.2022.111927 · 2023 · External reference
Physics-informed GCN-LSTM framework for long-term forecasting of 2D and 3D microstructure evolution
10.1038/s41524-026-01999-x · 2026 · External reference
Optimization of binary thermodynamic and phase diagram data
10.1007/bf02670872 · ExternalCitation · doi-reference
Estimation of CE-CVM energy parameters from miscibility gap data
10.1007/bf02704237 · ExternalCitation · doi-reference
Existence domains for invariant reactions in binary regular solution phase diagrams exhibiting two phases
10.1007/bf02711187 · ExternalCitation · doi-reference
The Cu-Ir (copper-iridium) system
10.1007/bf02873198 · ExternalCitation · doi-reference
Calculation of existence domains and optimized phase diagram for the Nb-Ti binary alloy system using computational methods
10.1007/s11669-020-00843-z · ExternalCitation · doi-reference
Thermodynamics of binary bcc and fcc phases for exclusive second-neighbour pair interactions using cluster variation method: Analytical solutions
10.1007/s12666-021-02469-2 · ExternalCitation · doi-reference
Phase diagrams
10.1016/0079-6786(75)90004-7 · ExternalCitation · doi-reference
Optimization of phase diagrams by a least squares method using simultaneously different types of data
10.1016/0364-5916(77)90002-5 · ExternalCitation · doi-reference
SGTE data for pure elements
10.1016/0364-5916(91)90030-n · ExternalCitation · doi-reference
The cluster/site approximation for multicomponent solutions - a practical alternative to the cluster variation method
10.1016/1359-6462(96)00198-4 · ExternalCitation · doi-reference
A cluster-based computational thermodynamics framework with intrinsic chemical short-range order: Part I. Configurational contribution
10.1016/j.actamat.2024.120138 · ExternalCitation · doi-reference
Accelerating CALPHAD-based phase diagram predictions in complex alloys using universal machine learning potentials: Opportunities and challenges
10.1016/j.actamat.2025.120747 · ExternalCitation · doi-reference
Modeling of thermotransport phenomenon in metal alloys using artificial neural networks
10.1016/j.apm.2012.06.018 · ExternalCitation · doi-reference
Approximate solutions to the cluster variation free energies by the variable basis cluster expansion
10.1016/j.commatsci.2016.05.035 · ExternalCitation · doi-reference
Polynomial functions for configurational correlation functions in gibbs energies of solid solutions using cluster variation method
10.1016/j.commatsci.2020.109746 · ExternalCitation · doi-reference
GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation
10.1016/j.commatsci.2022.111927 · ExternalCitation · doi-reference
AI methods in materials design, discovery and manufacturing: A review
10.1016/j.commatsci.2024.112793 · ExternalCitation · doi-reference
Exploring design space: Machine learning for multi-objective materials design optimization with enhanced evaluation strategies
10.1016/j.commatsci.2024.113432 · ExternalCitation · doi-reference
Design of Ni-based single crystal superalloys by machine learning based on data-driven multi-task optimization
10.1016/j.commatsci.2025.113969 · ExternalCitation · doi-reference
The synergy of machine learning and CALPHAD: Revitalizing traditional approaches
10.1016/j.commatsci.2025.113970 · ExternalCitation · doi-reference
A first-principles and CALPHAD-assisted phase-field model for microstructure evolution: Application to mo-v binary alloy systems
10.1016/j.matdes.2023.112443 · ExternalCitation · doi-reference
Activation functions in deep learning: A comprehensive survey and benchmark
10.1016/j.neucom.2022.06.111 · ExternalCitation · doi-reference
An explicit integration approach for predicting the microstructures of multicomponent alloys
10.1038/s41467-025-61246-7 · ExternalCitation · doi-reference
Recent advances and applications of machine learning in solid-state materials science
10.1038/s41524-019-0221-0 · ExternalCitation · doi-reference
Machine-learned interatomic potentials for alloys and alloy phase diagrams
10.1038/s41524-020-00477-2 · ExternalCitation · doi-reference
Physics-informed GCN-LSTM framework for long-term forecasting of 2D and 3D microstructure evolution
10.1038/s41524-026-01999-x · ExternalCitation · doi-reference
A generalization of the quasi - chemical method in the statistical theory of superlattices
10.1063/1.1724001 · ExternalCitation · doi-reference
Phase diagram determination using machine learning methods in materials science and engineering: a review
10.1080/14786435.2025.2558154 · ExternalCitation · doi-reference
A data-driven approach to approximate the correlation functions in cluster variation method
10.1088/1361-651x/ac3a16 · ExternalCitation · doi-reference
Methods of approximation in the theory of regular mixtures
10.1098/rspa.1953.0005 · ExternalCitation · doi-reference
Quasi-chemical method in the statistical theory of regular mixtures
10.1103/physrev.76.972 · ExternalCitation · doi-reference
A theory of cooperative phenomena
10.1103/physrev.81.988 · ExternalCitation · doi-reference
Importance of input data normalization for the application of neural networks to complex industrial problems
10.1109/23.589532 · ExternalCitation · doi-reference
An artificial neural network model to predict the thermal properties of concrete using different neurons and activation functions
10.1155/2019/3831813 · ExternalCitation · doi-reference
Melting or solidification curves in binary system
10.1515/zpch-1908-6314 · ExternalCitation · doi-reference
ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: Application to Cu-Mg
10.1557/mrc.2019.59 · ExternalCitation · doi-reference
