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Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title	AI Guided High-throughput Exploration of Potential Energy Surfaces
Author(s)	Subramanian Sankaranarayanan
On-Site Speaker (Planned)	Subramanian Sankaranarayanan
Abstract Scope	Molecular dynamics (MD) is a powerful simulation technique for materials modeling. Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. In this talk, I will present some of our recent work on the use of decision trees operating in continuous action space to seamlessly bridge the electronic, atomistic and mesoscopic scales for materials modeling. Our automated ML framework allows for high-throughput exploration of potential energy surfaces and aims to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort) and the increasingly large user community from academia and industry that applies these models. We will present success stories for dozens of different elemental systems across the periodic Table.
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

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