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	Uncertainty Quantification in Computational Thermodynamics - From the Atomistic to the Continuum Scale
Author(s)	Noah H. Paulson, Joshua Gabriel, Thien C Duong, Marius Stan
On-Site Speaker (Planned)	Noah H. Paulson
Abstract Scope	The design of materials requires comprehensive descriptions of the thermodynamic properties of materials and their constituents. The CALculation of PHase Diagrams (CALPHAD) approach leverages measured and calculated thermodynamic properties and phase stabilities to calibrate mathematical thermodynamic descriptions and provide critical predictive capabilities. In practice, uncertainties are present in the thermodynamic descriptions and resulting predictions as a result of the distribution of calibration data and differences in error between data sources. We describe several approaches, developed at Argonne National Laboratory, that provide thermodynamic information to CALPHAD through atomistic calculations and machine learning that consider the multiple origins of uncertainty and the propagation to higher length-scale predictions. Specifically, we explore uncertainty of enthalpy and specific heat derived from Density Functional Theory (DFT) including an efficient k-nearest-neighbors based acceleration scheme and machine learned interatomic potentials, and Bayesian methods to automatically weight thermodynamic datasets used in the calibration of CALPHAD models.
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