In this workshop, we wish to bring together an international group of leading theoreticians, computational, and experimental scientists in this field to discuss the main challenges facing the modern art of high-throughput materials design, identify possible collaborative solutions, and shape the future development of this emergent new area in materials science.
Specifically, the workshop will be devoted to discussing the following important aspects of this field:
• Mapping the different computational approaches currently used for creating new materials by design.
• Improvement of accuracy of Density Functional methods and its importance to useful high-throughput calculations.
• Understanding the theoretical and computational challenges in the high throughput design of complex materials such as transition metal oxides.
• Foster international collaborative efforts between theoreticians, computational, and experimental scientists in this field.
• Creation of a comprehensive databases of updated computational and experimental information in the field of materials design.
• From materials to devices, interfaces, and applications – how can high throughput and machine learning approaches accelerate the process.
• Specific applications such as theremoelectric materials, photovoltaics, energy storage, and magnetism will be discussed.
• What can the high-throughput material science community learn from the experience gained in other fields such a bioinformatics and computational drug design.
During the workshop we are going to hold 3 discussion panels:
- Efficient Strategies for materials design
- Combining information from different sources – how to form comprehensible databases of experiment and theory
- Future directions
As these issues bring highly inter-disciplinary questions we aim to bring together experts in materials sciences, ab-initio calculation methods, computer science, machine learning, ,data mining and high throughput calculations, high throughput experiment, and optimization methods and statistical analysis.