ProgramMaster Logo
Conference Tools for MS&T23: Materials Science & Technology
Register as a New User
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Abstract
Meeting MS&T23: Materials Science & Technology
Symposium Computational Discovery, Understanding, and Design of Multi-principal Element Materials
Presentation Title Charge-Density based Convolutional Neural Networks for Property Prediction in High Entropy Alloys
Author(s) Jacob Fisher, Serveh Kamrava, Pejman Tahmasebi, Dilpuneet S. Aidhy
On-Site Speaker (Planned) Dilpuneet S. Aidhy
Abstract Scope A descriptor-less machine learning (ML) model based only on charge density extracted from density functional theory (DFT) is developed to predict properties in concentrated alloys. Often, in most ML models, textbook physical descriptors such as atomic radius, valence charge and electronegativity are used which have limitations because these properties change in concentrated alloys when multiple elements are mixed to form a solid solution. We illustrate that, within the scope of DFT, the search for descriptors can be circumvented by charge density, which is the backbone of the Kohn-Sham DFT and describes the system completely. The model is based on convolutional neural networks (CNNs) as one of the promising ML techniques. The performance of our model is evaluated by predicting stacking fault energy (SFE) of concentrated alloys with an RMSE and R2 of 6.18 mJ/m2 and 0.87, respectively, validating the accuracy of the proposed approach.


A New Modified Embedded Atom Method Potential to Understand Plasticity in VNbTaTiZr High Entropy Alloy
Ab-Initio Investigation of Jahn-Teller Distortions within High Entropy Oxide Systems Using Recently Developed Meta-GGA Functionals
Charge-Density based Convolutional Neural Networks for Property Prediction in High Entropy Alloys
Computational Microstructural Design for Multi-phase Multi-principal Element Alloys
Computational Studies of Deformation Twinning in BCC Complex Concentrated Alloys
Critical Shear Stress Distribution and Average Dislocation Mobility in FeNiCrCoCu High Entropy Alloys Computed via Atomistic Simulations
Effect of Elasticity in Microstructural Evolution of Multi-component, Multi-phase System
Effects of Chemical Short-range Order in Medium Entropy Alloy CoCrNi
First-principles Study for Discovery of High-entropy MXenes
Hybrid Machine Learning Approach for Designing Refractory High Entropy Alloys
Microstructural Engineering via Heat Treatments in Multi-principal Element Alloy Systems with Miscibility Gaps
Modelling and Simulation on Mechanical Behavior of High-entropy Alloys
Phase Field Simulation of AgCuNi Ternary Alloy: Exploring Ag-CuNi Precipitation and Immiscibility
Predicting Ideal Shear Strength of Dilute Multicomponent Ni-based Alloys by an Integrated First-principles, CALPAHD, and Correlation Analysis
The Elastic Properties and Stacking Fault Energy of FeNiMoW
Yield Strength-Plasticity Trade-off and Uncertainty Quantification in ML-based Design of Refractory High-entropy Alloys

Questions about ProgramMaster? Contact