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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title A First Principles Multi-cell Monte Carlo Method for Phase Prediction
Author(s) You Rao, Changning Niu, Wolfgang Windl, Maryam Ghazisaeidi
On-Site Speaker (Planned) You Rao
Abstract Scope The importance of phase prediction lies not only in the understanding of basic thermodynamics, but also in the development of new materials. This has been a challenging problem due to the innate complexity, especially for multicomponent systems whose phase diagrams have not been established. The existing methods are either limited to binary systems or not applicable to crystalline solids at all. Here we present a new multicell Monte Carlo method based on first principles where the coexisting phases are represented by parallel supercells and free concentration search is achieved via a flip move. We first show that this method works for known miscible and immiscible systems as benchmarks and then we show that it successfully predicts the phase separation of a quaternary high entropy alloy as observed in experiments. Finally, we show that it can also work for semiconductors and intermetallics with appropriate modifiers.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A First Principles Multi-cell Monte Carlo Method for Phase Prediction
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A New Phase-field Model with Anisotropic Interface Width for the Highly Anisotropic Growth of Ice Dendrites
A Self-consistent Parametric Homogenization Framework for Fatigue in Ni-based Superalloys
Advances in a Phase Field Dislocation Dynamics Model to Account for Various Gamma-surfaces of Hexagonal Close Packed Crystallography
Advancing Methods for Atomic-scale Modeling of Heterogeneous Systems
An Active Learning Approach for the Generation of Force Fields from DFT Calculations
An Atomistic Framework to Understand Solute Grain Boundary Segregation in a Polycrystal
Applying Machine Learning to Identifying Packing Defects in Amorphous Materials
Boosting the CALPHAD Modeling of Multi-component Systems by ab initio Calculations: Selected Case Studies
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Development of an Evolutionary Deep Neural Net for Materials Research
Direct Consideration of Vacancies in CALPHAD Modelling of Zirconium Carbide
Functional Uncertainty Propagation with Bayesian Ensembles in Molecular Dynamics
Generative Deep Neural Networks for Inverse Materials Design using Backpropagation and Adaptive Learning
Hierarchical Integration of Atomistically-derived Dislocation Mobility Laws into Discrete Dislocation Dynamics Simulations
High-throughput Computational Design of Organic-inorganic Hybrid Halide Semiconductors Beyond Perovskites
Interatomic Potentials as Physically-informed Artificial Neural Networks
Inverse Solutions Based on Reduced-order Process-structure-property Linkages Using Markov Chain Monte Carlo Sampling Algorithms
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L-1 (Digital): Machine Learning and Computer Vision on Classification of Carbon Nanotube and Nanofiber Structures for TEM Dataset
L-10: PyMob: Software for Automated Assessment of Atomic Mobilities
L-11: Randomness at Scale: Properties of Bulk Nanostructured Materials from Stochastic Representative Volume Elements
L-12: Simulation of Compressive Stress-strain Curve for Additive Manufactured Ti6Al4V Cuboctahedron Cellular Structure
L-13: Three-dimensional Modeling of Growth and Motion of Dendrites under Thermosolutal Convection
L-14: Uncertainty Propagation in CalPhaD Calculations
L-2 (Invited): Multi-Scale Modelling and Defect Engineering in Boron Carbon-Nitride van der Waals Heterostructures
L-3: An Improved Collocation Method to Treat Traction-free Surfaces in Dislocation Dynamics Simulations
L-4: Classifying Atomic Environments by the Gromov-Wasserstein Distance
L-5: Coupled Light Capture and Lattice Boltzmann Model of TiO2 Micropillars Array for Water Purification
L-6: Investigation of Fe-O and Fe-N and H-O Bond Formation Process by the Molecular Dynamics Simulations
L-7: Machine Learning Driven Functionally Graded Material Designs for Mitigation of Thermally Induced Stress
L-8: Methods to Simulate Grain Boundary Diffusion in Bicrystals and Polycrystals
L-9: Numerical Simulation for Microstructural Evolution in Solidification Process using CFD-CA (Cellular Automata) Coupled Method
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Material Parameters Identification, Modeling and Experimental Verification of the New Smart Material Vacuum Packed Particles
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New Workflow for High-throughput Feature Extraction of Deforming Open Cell Foams
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