## [Illinois] CSE Seminar Series: Advances in First-principles Computational Materials Science

#### Category

#### Published on

#### Abstract

Title: Advances in first-principles computational materials science

Subtitle: Things we can calculate now, that we couldn't when I was in grad school.

The capability to rationally design new materials with tailored properties and functionality on a computer remains a grand challenge whose success would have tremendous impact on several globally-relevant issues. Guided materials design in the form of Integrated Computational Materials Engineering (ICME) is now taking its foundational steps towards establishing the infrastructure and methodologies to realize this grand challenge. But, while computational materials science has seen several advances in the last few years thanks to steady progress in computational power and numerical algorithms, there still remain a number of materials properties that are elusive to quantitative computational simulation. I will discuss recent developments within the field of computational materials science that highlight what material properties we now can -- and what we still cannot -- compute. I'll illustrate a few real-world examples related to mechanical properties for alloy design, optical properties for advanced photovoltaics and photocatalysis design, and thermal properties for thermoelectric design, using methods such as molecular dynamics, density functional theory, and quantum Monte Carlo. I'll also highlight some large outstanding problems for computational materials science.

Subtitle: Things we can calculate now, that we couldn't when I was in grad school.

The capability to rationally design new materials with tailored properties and functionality on a computer remains a grand challenge whose success would have tremendous impact on several globally-relevant issues. Guided materials design in the form of Integrated Computational Materials Engineering (ICME) is now taking its foundational steps towards establishing the infrastructure and methodologies to realize this grand challenge. But, while computational materials science has seen several advances in the last few years thanks to steady progress in computational power and numerical algorithms, there still remain a number of materials properties that are elusive to quantitative computational simulation. I will discuss recent developments within the field of computational materials science that highlight what material properties we now can -- and what we still cannot -- compute. I'll illustrate a few real-world examples related to mechanical properties for alloy design, optical properties for advanced photovoltaics and photocatalysis design, and thermal properties for thermoelectric design, using methods such as molecular dynamics, density functional theory, and quantum Monte Carlo. I'll also highlight some large outstanding problems for computational materials science.

#### Bio

Ertekin works in the field of computational materials research: a research area that uses computer simulations, modeling, and theories to describe the properties of materials, including nanoscale systems such as carbon nanotubes, and bulk systems such as new materials for solar cells. Carbon nanotubes—cylindrical tubes made with the strongest tensile strength and stiffest elastic modulus material system currently known—are new mechanical components used in modern devices.

As a graduate student, Ertekin developed models of defect-mediated plastic deformation in carbon nanotubes, and has explored phenomena such as ductile-brittle phase transitions at the nanoscale. Her post-doctoral work has focused on describing how defects can be introduced in a material to control its properties, such as how to make silicon a better absorber of sunlight for photovoltaics, and how to turn ceramic oxides into conducting materials for oxide-based fuel cells.

-From MechSE News

#### Sponsored by

Computational Science and Engineering (CSE) at the University of Illinois at Urbana-Champaign

#### Cite this work

Researchers should cite this work as follows: