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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title Machine-learned Interatomic Potentials for Alloy Modeling and Phase Diagrams
Author(s) Gus LW Hart, Conrad W Rosenbrock, Konstantin Gubaev, Alexander Shapeev, Livia B. Pártay, Noam Bernstein, Gábor Csányi
On-Site Speaker (Planned) Gus LW Hart
Abstract Scope We demonstrate the power of machine-learned potentials to model multicomponent systems. We compare two different approaches: Moment tensor potentials (MTP) and he Gaussian Approximation Potential (GAP) framework (kernel regression + the Smooth Overlap of Atomic Positions (SOAP) representation). Both types of potentials give excellent accuracy for a wide range of compositions and rival the accuracy of cluster expansion. While both models are perform well, SOAP-GAP excels at transferability as shown by sensible transformation paths between configurations, and MTP allows, due to its lower computational cost, the calculation of compositional phase diagrams. Given the fact that both methods perform as well as cluster expansion would but yield off-lattice models, we expect them to open new avenues in computational materials modeling for alloys. We show compositional phase diagrams, phonon dispersions, new superalloy phases, all predicted using these two machine-learned interatomic potentials for binary and ternary alloys.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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A Multi-GPU Implementation of a Full-field Crystal Plasticity Solver for Efficient Modeling of High-resolution Microstructures
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
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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
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Functional Uncertainty Propagation with Bayesian Ensembles in Molecular Dynamics
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Hierarchical Integration of Atomistically-derived Dislocation Mobility Laws into Discrete Dislocation Dynamics Simulations
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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
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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
Large Scale 3D Phase-field Sintering Simulations
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Machine Learning Approaches for Improving Density Functional Tight Binding Models of Reactive Materials: Application to Astrobiolgical Materials and Surface Chemistry
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