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Automation and control of laser wakefield accelerators using Bayesian optimization.

ABSTRACT: Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the spectral and spatial phase of the laser and the plasma density and length. Most notably, the model built by the algorithm enabled optimization of the laser evolution that might otherwise have been missed in single-variable scans. Subtle tuning of the laser pulse shape caused an 80% increase in electron beam charge, despite the pulse length changing by just 1%.

SUBMITTER: Shalloo RJ

PROVIDER: S-EPMC7732832 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Automation and control of laser wakefield accelerators using Bayesian optimization.

Shalloo R J RJ Dann S J D SJD Gruse J-N JN Underwood C I D CID Antoine A F AF Arran C C Backhouse M M Baird C D CD Balcazar M D MD Bourgeois N N Cardarelli J A JA Hatfield P P Kang J J Krushelnick K K Mangles S P D SPD Murphy C D CD Lu N N Osterhoff J J Põder K K Rajeev P P PP Ridgers C P CP Rozario S S Selwood M P MP Shahani A J AJ Symes D R DR Thomas A G R AGR Thornton C C Najmudin Z Z Streeter M J V MJV

Nature communications 20201211 1

Laser wakefield accelerators promise to revolutionize many areas of accelerator science. However, one of the greatest challenges to their widespread adoption is the difficulty in control and optimization of the accelerator outputs due to coupling between input parameters and the dynamic evolution of the accelerating structure. Here, we use machine learning techniques to automate a 100 MeV-scale accelerator, which optimized its outputs by simultaneously varying up to six parameters including the ...[more]

PMID: 33311487

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Project description:BackgroundIn recent years, the production of inclusion bodies that retain substantial catalytic activity was demonstrated. These catalytically active inclusion bodies (CatIBs) are formed by genetic fusion of an aggregation-inducing tag to a gene of interest via short linker polypeptides. The resulting CatIBs are known for their easy and cost-efficient production, recyclability as well as their improved stability. Recent studies have outlined the cooperative effects of linker and aggregation-inducing tag on CatIB activities. However, no a priori prediction is possible so far to indicate the best combination thereof. Consequently, extensive screening is required to find the best performing CatIB variant.ResultsIn this work, a semi-automated cloning workflow was implemented and used for fast generation of 63 CatIB variants with glucose dehydrogenase of Bacillus subtilis (BsGDH). Furthermore, the variant BsGDH-PT-CBDCell was used to develop, optimize and validate an automated CatIB screening workflow, enhancing the analysis of many CatIB candidates in parallel. Compared to previous studies with CatIBs, important optimization steps include the exclusion of plate position effects in the BioLector by changing the cultivation temperature. For the overall workflow including strain construction, the manual workload could be reduced from 59 to 7 h for 48 variants (88%). After demonstration of high reproducibility with 1.9% relative standard deviation across 42 biological replicates, the workflow was performed in combination with a Bayesian process model and Thompson sampling. While the process model is crucial to derive key performance indicators of CatIBs, Thompson sampling serves as a strategy to balance exploitation and exploration in screening procedures. Our methodology allowed analysis of 63 BsGDH-CatIB variants within only three batch experiments. Because of the high likelihood of TDoT-PT-BsGDH being the best CatIB performer, it was selected in 50 biological replicates during the three screening rounds, much more than other, low-performing variants.ConclusionsAt the current state of knowledge, every new enzyme requires screening for different linker/aggregation-inducing tag combinations. For this purpose, the presented CatIB toolbox facilitates fast and simplified construction and screening procedures. The methodology thus assists in finding the best CatIB producer from large libraries in short time, rendering possible automated Design-Build-Test-Learn cycles to generate structure/function learnings.

Dataset Information

Automation and control of laser wakefield accelerators using Bayesian optimization.

Publications

Automation and control of laser wakefield accelerators using Bayesian optimization.

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