# Optimization Research in Other Divisions

## Energy Technologies Aarea (ETA)

## Accelerating the Discovery of New Batteries, Thermoelectrics, and Solar Cells with Optimized Materials Screening

**Anubhav Jain: ajain@lbl.gov**

New energy technologies are limited by the fundamental physical properties of the materials they are composed of. Thus, discovering and identifying new materials with improved functional properties is critical to the advancement of modern photovoltaic, thermoelectric, and battery technologies. Our group employs supercomputers in combination with *high-throughput materials design *(an accelerated design procedure akin to 'rapid computational prototyping') to simulate the properties of new materials for energy applications. However, among the billions of possible materials, only a handful may meet the technical and economic requirements necessary to be truly revolutionary. The large search space makes the high-throughput screening process impossible to conduct using exhaustive search, and one must be able to use past information (e.g., expert knowledge or the results of prior calculations) to guide the selection of materials candidates for computation.

We have already written a high-throughput computation and workflow engine called FireWorks to conduct simulations on a massive scale. Next, we are developing an optimization framework that integrates machine learning with FireWorks to find *more* *good materials* with *fewer computations*. Within a given computational budget, such a framework will maximize the chances of finding one or more revolutionary new materials and in essence provide a method such that, given a simulation procedure and a search space of candidates, can automatically determine the best materials candidates in that search space for a given application by conducting large numbers of ever-improving simulations automatically at supercomputing centers.

Figure: A random forest model is tested in the optimization framework in a search for 20 solar water-splitting perovskites among a space of nearly 19,000 possible candidates. The optimization engine outperforms both exhaustive search and an expert's chemical-rule based search. When the optimization engine is combined with the chemical-rule based search, the search is more than 19x as efficient as exhaustive search in finding all 20 candidates over 20 runs.

## Accelerator Technology and Applied Physics Division (ATAP)

## Predicting optimal parameters for new compact particle accelerators

**Remi Lehe: rlehe@lbl.gov**

Laser-wakefield acceleration is a new technology which could potentially shrink the size of particle accelerators from hundreds of meters to only a few tens of centimeters! Being able to build compact and cheap particle accelerators with this technology would have a strong impact on high-energy physics and fundamental science, but also on applications in medicine and homeland security.

In a laser-wakefield accelerator, an intense femtosecond laser pulse (represented in red below) propagates through a gas jet and generates an accelerating plasma structure in its wake (represented in blue below). Under the right parameters (in terms of gas density and laser intensity), some of the electrons “break off” from the plasma structure and get accelerated to high energy (represented with yellow arrows below) – a phenomenon known as *injection*.

Determining the region of parameter space in which injection does happen is of paramount importance, since this is the regime which is required for a number of applications of laser-wakefield acceleration. In this context, we used surrogate optimization in order to reduce the uncertainty on the threshold between injection and no injection (represented by a wide red line below) by concentrating numerical simulations around the region of interest.

### Particle Accelerator Multi-Objective Global Optimization Using Stochastic Evolutionary Algorithms

**Ji Qiang: jqiang@lbl.gov**

Particle accelerators are one of the most important scientific instruments and have important applications in science, industry, and national security. For example, since 2009, the free electron laser (FEL) accelerator Linac Coherent Light Source (LCLS) has produced the world’s brightest X-ray pulses that help reveal fundamental processes in materials and living things. In 2012, the Large Hadron Collider (LHC) accelerator at the European Organization for Nuclear Research (CERN) discovered the Higgs boson, which explains the origin of mass in fundamental particles. Meanwhile, particle accelerators are also one of the most complex and expensive instruments that can employ several ten thousands of elements and cost multiple billion dollars. In order to improve the performance and reduce the cost of those accelerators, machine control parameter optimization is needed. The number of control parameters can vary from a few dozen to a few thousand, and the objective functions are highly nonlinear. In our study, we have developed a parallel multi-objective unified differential evolution algorithm with variable population and external storage for global parameter optimization [1,2]. This optimizer, together with our particle accelerator beam dynamics simulator has been successfully applied to the LCLS-II accelerator design [3,4].

[1] J. Qiang, C.E. Mitchell, S. Paret, R. Ryne, Y. Chao, “A Parallel Multi-objective Differential Evolution Algorithm for Photoinjector Beam Dynamics Optimization”, 2013, Proc. of International Particle Accelerator Conference

[2] J. Qiang, C. Mitchell, A. Qiang, “Tuning of an Adaptive unified differential evolution algorithm for global optimization”, 2016, Proc. of 2016 Congress on Evolutionary Computation (CEC)

[3] J. Qiang, C. Mitchell, “Electron beam dynamics optimization using a unified differential evolution algorithm”, 2014, Proc. of Free Electron Laser

[4] J. Qiang, "Start-to-End Beam Dynamics Optimization of X-Ray FEL Light Source Accelerators", 2016, Proc. North America – Particle Accelerator Conference

More to come, check back soon....