ProgramMaster Logo
Conference Tools for 2021 TMS Annual Meeting & Exhibition
Register as a New User
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools
About this Symposium
Meeting 2021 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Computational Materials Science and Engineering Committee
Organizer(s) Kamal Choudhary, National Institute of Standards and Technology
Garvit Agarwal, Argonne National Laboratory
Wei Chen, University At Buffalo
Mitchell Wood, Sandia National Laboratories
Vahid Attari, Texas A&M University
Oliver Johnson, Brigham Young University
Richard G. Hennig, University of Florida
Scope Technological advances rely on the discovery, characterization, and development of materials with novel properties. Computational investigations at the continuum, mesoscopic, and quantum level, have proven to be effective for both characterizing properties and identifying new material systems of interest. High-throughput approaches have recently helped in scanning the vast space of possible materials and contributed to the formation of large, public databases, enabling a cascade of research efforts branching from the original work. Artificial intelligence (automation, classification, regression) techniques are seen as a reliable way to further accelerate such searches for new materials. Special care is needed to ensure uniformity and quality of generated databases, but possibly even more importantly, to quantify and expose uncertainties in the data to provide accurate predictions. Furthermore, a way to validate the accuracy of simulation techniques, comparisons with experimental and other computational approaches are necessary.

This symposium will focus on artificial intelligence methods, big data issues, computational methodology validation, as well as propagation and quantification of uncertainty in computational approaches at various length scales. The goal of the symposium is to cover these research topics in an interdisciplinary approach, which connects theory and experiment, with a broad view towards materials applications.
Topics addressed in this symposium will include (but are not limited to):
• Big data: issues, techniques, and applications
• Machine learning and other artificial intelligence approaches applied to material science: model development, applications, and validation
• Data mining: difficulties, techniques, and applications
• Validation, and uncertainty propagation and quantification (UQ) for:
- atomistic modeling (DFT and classical force fields)
- meso and continuum scale modeling
- multi-scale modeling

Abstracts Due 07/20/2020
Proceedings Plan Planned:

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