Conference Tools for 2021 TMS Annual Meeting & Exhibition

Help

About this Abstract
Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title	Accuracy, Uncertainty, Inspectability: The Benefits of Compositionally-restricted Attention-based Networks
Author(s)	Taylor D. Sparks, Steven K. Kauwe, Ryan J. Murdock, Anthony Yu-Tung Wang
On-Site Speaker (Planned)	Taylor D. Sparks
Abstract Scope	We describe a new model architecture, the Compositionally-Restricted Attention-Based Network (CrabNet). CrabNet generates high-fidelity predictions based on the self-attention mechanism, a fundamental component of the transformer architecture which revolutionized natural language processing. The transformer encoder uses self-attention to encode the context-dependent behavior for the components within a system. In physical environments, elements contribute differently to a material's property based on the materials system itself. For example, boron behaving as an electrical dopant in one system while behaving as a mechanical strengthening bond modification in another. CrabNet's ability to potentially capture this type of context-dependent behavior allows for highly accurate model predictions. Importantly, CrabNet generates simple and inspectable self-attention maps. These attention maps govern the learned material property by representing element importance and interactions. The visualization and analysis of these attention maps are available during training and inference periods.
Proceedings Inclusion?	Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Bayesian Optimization Framework for Exploring the Grain Boundary Manifold

A Machine Learning Investigation of Crystallographic Parameters for Abnormal Grain Growth

A Probabilistic Approach with Built-in Uncertainty Quantification for the Calibration of a Superelastic Constitutive Model from Full-field Strain Data

A Sensitivity Analysis of Microstructure-Based Model for U-10Mo Hot Rolling and Annealing

Accelerating High Throughput Materials Simulation Studies Using Machine Learning Based Application Programming Interface (API)

Accelerating Phase-field Predictions via Machine Learning Trained Surrogate Models

Accelerating the Discovery of Self-Reporting Redox-active Materials Using Quantum Chemistry Guided Machine Learning

Accuracy, Uncertainty, Inspectability: The Benefits of Compositionally-restricted Attention-based Networks

AI Guided Discovery of Self-assembly Peptide Sequences using Monte Carlo Tree Search and Coarse-grained Simulations

AI Guided High-throughput Exploration of Potential Energy Surfaces

Are We Making Progress on ML Algorithms for Structure-property Relationships? Using MatBench as a Test Bed

Bayesian Inference and Uncertainty Quantification of Grain Boundary Properties

Building a Better Database to Learn From; Application to Interatomic Potentials

Coupling Machine Learning and Global Structure Optimization in GASP 2.0

Data Science Approaches to Develop Predictive Models for Energy-relevant Materials

Decision Trees in Continuous Action Space for High-throughput Exploration of Potential Energy Surfaces

Discovery and Classification of Double Spinel Chemical Space

Exploring Metastability and Mapping Metastable Phase Diagrams Using Machine Learning

Fast Crystal Structure Reconstruction and Prediction Method: Based on X-ray Diffraction Dataset and Neural Network

Finding and Sharing Atomistic Materials Data and Software with the NIST Materials Resource Registry

Harnessing Materials Data and Simulation Capabilities for the Accelerated Discovery of Photocathode Materials

Inverse Design of Energy Storage Materials via Active Learning

Machine Learning Approach of Molecular Dynamics Simulations for Body-Centered Cubic Zirconium

Machine Learning for Predicting Grain Boundary Properties

Machine Learning Guided Discovery of Novel Oxide Perovskites for Scintillator Applications

Machine Learning Prediction of Defect Formation Energies

Microstructure-driven Parameter Calibration for Mesoscale Simulation

Mining Structure-property Linkages in Nonporous Materials Using Interpretative Deep Learning Approach

Model Comparison and Uncertainty Prediction for ML Models of Crystalline Solids Material Properties

Multi-fidelity Machine-learning with Uncertainty Quantification and Bayesian Optimization for Materials Design: Application to Random Alloys

Neural Network Reactive Force Field for C, H, N, O Systems

Parsimonious Neural Networks Learn Classical Mechanics and an Accurate Time Integrator

Predicting Adsorption Energies and Surface Pourbaix Diagram of Metal NPs by GCNN Method

Quantifying RAMPAGE Interatomic Potentials for Metal Alloys

Simultaneous Development and Robust Optimization of a Microstructure Dependent Material Model: Leveraging Sequential Monte-Carlo Methods to Enhance Symbolic Regression Analysis

Solving Stochastic Inverse Problems for Structure-Property Linkages Using Data-Consistent Inversion

Uncertainty Quantification in Computational Thermodynamics - From the Atomistic to the Continuum Scale

Uncertainty Quantification of Microstructures with a New Technique: Shape Moment Invariants

Use of Atomistic Based Informatics to Model Ionic Bombardment to Synthesize Boron Carbides

De Novo Design of Therapeutic Agents Against COVID-19 Using Artificial Intelligence

Questions about ProgramMaster? Contact programming@programmaster.org