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Meeting 2017 TMS Annual Meeting & Exhibition
Symposium Computational Approaches to Materials for Energy Applications
Presentation Title Structure Prediction in Novel Energy Materials Design
Author(s) Maximilian Amsler, Chris Wolverton
On-Site Speaker (Planned) Maximilian Amsler
Abstract Scope Recently, novel approaches in computational materials science have led to significant advances in materials design, including high-throughput calculations, data-mining and machine learning. Another increasingly popular technique is ab-initio structure prediction, such as the Minima Hopping Method, which implements a highly efficient global geometry optimization algorithm to identify thermodynamically stable and metastable compounds. I will illustrate how this method can be used to tackle materials design challenges from different perspectives, supported by examples of its recent, successful application.
Proceedings Inclusion? Undecided


Ab Initio Calculations of Carrier Radiative Lifetimes
Accelerated Discovery of Novel Low-thermal-conductivity Crystals by First-principles Data-driven Approach
Design of Heteroepitaxialy Grown Quantum Dots Under External Force Fields
Different Aspects of Disorder in Materials for Energy Conversion Studied by the KKR-CPA Calculation
Energy Landscape of Point Defects in Body-centered-cubic Metals
First Principles Calculations of the Stability and Physical Properties of Thermoelectric Materials
Monte Carlo Modeling of Phonon Transport in Nanostructures
Optimizing Materials for Solar Energy Conversion: In Search for Descriptors
Structure Prediction in Novel Energy Materials Design
Systematic Search for Lithium Ion Conducting Compounds by Screening of Compositions Combined with Atomistic Simulation
Tuning Thermal Conductivity of Metal-Organic–Frameworks
Visual Search Strategies for Thermoelectrics

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