ProgramMaster Logo
Conference Tools for 2020 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 2020 TMS Annual Meeting & Exhibition
Symposium Computational Discovery and Design of Emerging Materials
Sponsorship TMS Materials Processing and Manufacturing Division
TMS: Computational Materials Science and Engineering Committee
Organizer(s) Arunima K. Singh, Arizona State University
Houlong Zhuang, Arizona State University
Sugata Chowdhury, National Institute of Standards and Technology
Arun Kumar Mannodi Kanakkithodi, Purdue University
Scope Recent advancements in computational methods, computing power and materials informatics present us with an exciting opportunity to predictively discover and design materials for a variety of technologically relevant applications. In particular, quantum mechanical ab-initio methods such as density-functional theory simulations, dynamical mean-field theory, quantum Monte-Carlo simulations and time-dependent density functional theory have been pivotal in developing an atomistic-scale fundamental understanding of complex phenomena, and in the discovery and the design of several emerging materials such as – superconductors, topological insulators, magnetic materials, photocatalysts, battery materials, and most recently, quantum materials.

This symposium will cover the state-of-the art in the application as well as the integration of computational methods, particularly ab-initio simulation methods, with experiments and materials informatics applied to the discovery and design of emerging materials.


* Computational discovery and design of correlated electron materials, quantum materials, and superconductors
* Computational discovery and design of magnetic materials and topological insulators
* Application of computational methods for photocatalytic and battery materials discovery and design
* Computational discovery and design of materials for nanoelectronics
* Application of materials informatics approaches such as machine learning, genetic algorithms, and cluster expansion for an accelerated discovery and design of materials

Abstracts Due 07/15/2019
Proceedings Plan Planned: Supplemental Proceedings volume

A General Machine Learning Framework for Impurity Level Prediction in Semiconductors
Accelerated Discovery of Materials with Programmable Decomposition in Flow Batteries via Machine Learning
Accelerating the Genetic Algorithm for Structure Prediction in 2D Materials using Machine Learning
Active Learning Guided Polymer Space Exploration and Discovery
Analysis of Chemical Activity of Bismuthene in the Presence of Environment Gas Molecules by Means of Ab-initio Calculations
Computation Accelerated Design of Fast Ion Conducting Materials for Solid-state Batteries
Computational Design of Non-Precious Transition Metal/ Nitrogen Doped Carbon Electrocatalysts for Sustainable Energy Technology
Computational Discovery of Strongly Correlated Quantum Matter through Downfolding
Computational Methodological Study of Mn(taa) Spin Crossover Compound
Computational Synthesis of 2D Materials: A High-throughput Approach to Materials Design
Data-driven Discovery of the Functional Form of the Superconducting Critical Temperature
Density Functional Theory and Machine Learning Guided Prediction of Thermal Properties of Rare-earth Disilicates
Design of Metastable Materials: Heterostructural Alloys and Novel Nitrides
Designing High Glass Transition Temperature Polymers using Machine Learning
Discovery and Characterization of 1D Inorganic Polymers Through Datamining and Density Functional Theory
Effect of Spin-orbit Coupling on Magnetic Phase Transition of Anti-ferromagnetic Weyl-Semimetal
Electronic Excitations and Ultrafast Dynamics: Pushing Towards Materials Engineering and Design
Exploring Van der Waals 2D Heterostructures using a Combined Machine Learning and Density Functional Theory Approach
First-principles-based Hybrid Perovskite Materials Design for Memristor
First-principles Investigation of Dopants, Defects, and Defect Complexes in 2D Transition Metal Dichalcogenides
First-principles Theory of Nonlinear Optical Responses in 2D Materials and Topological Materials
Frequency-dependent Dielectric Constant Prediction of Polymeric Dielectrics with Machine Learning
From Pentagonal Geometries to Two-dimensional Materials
Haber–Bosch Reaction Mechanism and Kinetics on Highly Reactive Iron Surface and Hierarchical High-throughput in Silico Screening Catalyst Design
High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage
High-Throughput Screening and Synthesis of Semiconductor Electrodes for Photocatalytic Water Splitting
High Throughput Exploration of Two-dimensional Topological Artificial Lattices
Identification of 11 New Solid Lithium-ion Conductors with Promise for Batteries Using Data Science Approaches
Influence of Strain on Mesoscopic 2D Film Growth from Phase Field Methods
Introducing the MEAM Interatomic Potential for NiTiHf Shape Memory Alloys
L-15: Discovery of Rare-earth-free Magnetic Materials Using Adaptive Genetic Algorithm and First-principles Calculations
L-16: Machine Learning Models for the Lattice Thermal Conductivity Prediction of Inorganic Materials
L-17: Searching for Electrical Conductivity Tunable Organic Molecules for Single-molecule Electronics
Landscape Study of Deformation Effects (cleave/shear) and Vacancies on the Structural Electronic and Mechanical Properties of MAX Phase Alloys
Machine-learning based Discovery of Novel Scintillator Chemistries
Machine Learned Models for Transition Metal Dichalcogenide
Machine Learning Guided Search for Single Phase High Entropy Oxides
Neural Network Potentials for Water-in-salt Electrolytes
Predicting Functional Defects by Design in Energy and Quantum Materials
Predicting Organic Ligands Mechanical Behavior with Deep Neural Network and Understanding the Mechanism
Predicting Physical Properties of SiO2-based Glasses by Machine Learning
Predicting Polymer Crystallinity Using Multi-fidelity Information Fusion with Machine Learning
Predicting the Properties of Crystals with High Accuracy Using Deep Learning
Sorting through Messy Materials with First Principles Calculations
Stable Structures of 2D Materials, Thin Films, and Surface Reconstructions on Substrates using an Evolutionary Algorithm Approach
The Relationship Between Compositional Mixing and Phase Stability of Metal-halide Perovskites: Theoretical Study
Toward Rational Design and Discovery of Metastable Materials
Towards a First-principles Description of Stronger Correlations: Novel Superconductors to Topological Materials
Tunability of Martensitic Transformation in Mg-Sc Shape Memory Alloys: a DFT Study
Tuning Mechanical Behavior of Graphene: From Microscopic Defect Modeling to Macroscopic Property Prediction
Two-dimensional Functional Materials with Pentagonal Structure
Use of Atomistic-based Modeling and Materials Informatics to Design and Synthesize Ultra-thin Tunnel Junctions

Questions about ProgramMaster? Contact