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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Metadata Entry Optimization for NASA's Biological Institutional Scientific Collection (NBISC)

The NASA Biological Institutional Sample Collection (NBISC) at NASA’s Ames Research Center is a critical resource housing non-human samples collected from spaceflight missions and ground analog studies, primarily consisting of specimens from rats, mice, and select microbes. The primary objective of NBISC is to systematically receive, document, preserve, and facilitate access to these samples for the global scientific community. NBISC promotes international collaboration and maximizes the return on investment for precious tissues from spaceflight and analog experiments. Researchers can request physical samples through an online request form and subsequent written proposal review process. This study addresses two core research objectives: streamlining the NBISC sample lifecycle processes and strategizing for managing an influx of 50,000 tissue samples from a series of cosmic radiation analog experiments carried out at the NASA Space Radiation Laboratory (NSRL) by Drs. Eleanor Chang (Lawrence Berkeley Laboratory) and Polly Blakely (SRI). The Chang/Blakely studies investigated Harderian gland (HG) tumorigenesis in mice exposed to low dose and LET radiation comprising 8 different exposure protocols in over 4000 mice. NBISC sample metadata is stored in a Laboratory Information Management System (SLIMS). To streamline sample data entry, we customize python scripts using information extracted from the individual experimental protocols. The scripts automate entry into multiple SLIMS data fields including protocol name, unique sample barcode, tissue and sub-tissue information, freezer location, sample preservation method, etc. The semi-automated procedure significantly decreases the time spent on data entry by several orders of magnitude. Automation and data organization are essential, as they free up time for curation and promotion of the collection which, in turn, increase the accessibility of samples to the broader research community. NBISC benefits from streamlined data ingestion, and the methodologies developed here are applicable to other projects which use SLIMS including the NASA Biospecimen Sharing Program and GeneLab. As of Fall 2023, plans include transferring sample data from SLIMS to public facing repositories (OSDR and NLSP), expanding the reach of the Chang/Blakely sample collection. The Human Research Program Space Radiation Element plans to transfer non-human tissues from many more investigations to NBISC in the coming year.

Sample Repository↗
NASA NTRSDate not supplied

Metadata Entry Optimization For NASA's Biological Institutional Scientific Collection (NBISC)

The NASA Biological Institutional Sample Collection (NBISC) at NASA’s Ames Research Center is a critical resource housing non-human samples collected from spaceflight missions and ground analog studies, primarily consisting of specimens from rats, mice, and select microbes. The primary objective of NBISC is to systematically receive, document, preserve, and facilitate access to these samples for the global scientific community. NBISC promotes international collaboration and maximizes the return on investment for precious tissues from spaceflight and analog experiments. Researchers can request physical samples through an online request form and subsequent written proposal review process. This study addresses two core research objectives: streamlining the NBISC sample lifecycle processes and strategizing for managing an influx of 50,000 tissue samples from a series of cosmic radiation analog experiments carried out at the NASA Space Radiation Laboratory (NSRL) by Drs. Eleanor Chang (Lawrence Berkeley Laboratory) and Polly Blakely (SRI). The Chang/Blakely studies investigated Harderian gland (HG) tumorigenesis in mice exposed to low dose and LET radiation comprising 8 different exposure protocols in over 4000 mice. NBISC sample metadata is stored in a Laboratory Information Management System (SLIMS). To streamline sample data entry, we customize python scripts using information extracted from the individual experimental protocols. The scripts automate entry into multiple SLIMS data fields including protocol name, unique sample barcode, tissue and sub-tissue information, freezer location, sample preservation method, etc. The semi-automated procedure significantly decreases the time spent on data entry by several orders of magnitude. Automation and data organization are essential, as they free up time for curation and promotion of the collection which, in turn, increase the accessibility of samples to the broader research community. NBISC benefits from streamlined data ingestion, and the methodologies developed here are applicable to other projects which use SLIMS including the NASA Biospecimen Sharing Program and GeneLab. As of Fall 2023, plans include transferring sample data from SLIMS to public facing repositories (OSDR and NLSP), expanding the reach of the Chang/Blakely sample collection. The Human Research Program Space Radiation Element plans to transfer non-human tissues from many more investigations to NBISC in the coming year.

Biospecimen↗
DOE OSTI2018

Biomimetic model-based advanced control strategy integrated with multi-agent optimization for nonlinear chemical processes

Here, a novel framework is proposed for integrating biomimetic-based advanced control and multi-agent optimization approaches for nonlinear chemical process applications. In particular, a Biologically-Inspired Optimal Control Strategy, denoted as BIO-CS, is combined with multi-agent optimization (MAO) techniques to provide optimal solutions for dynamic systems. In this combined framework, the BIO-CS algorithm employs gradient-based optimal control solvers for the intermediate problems associated with the leader-follower agents’ local interactions. Also, the MAO uses the capabilities of heuristic-based optimization techniques by sharing process information to obtain optimal operating setpoints for the controller considering an overall process objective. The applicability of the proposed method is demonstrated using a nonlinear, multivariable, process model of a fermentation system. Specifically, the optimal operating points are computed by the MAO implementation for setpoint tracking, trajectory tracking and plant-model mismatch scenarios for BIO-CS application. Results of the developed framework are compared to a gradient-based Sequential Quadratic Programming (SQP) technique and a classical proportional-integral (PI) controller in terms of optimization and control studies, respectively. As an additional contribution, BIO-CS is also cast as a model predictive controller (MPC) for the first time and compared to the agent-based BIO-CS approach in terms of computational time and tracking error. Closed-loop control results show up to 46% improvement in tracking performance during transient for the multi-agent BIO-CS when compared to BIO-CS as MPC for additional computational expense. The obtained results illustrate the capabilities of this novel integrated framework including BIO-CS as MPC to achieve desired nonlinear system performance for various scenarios.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Artificial Intelligence and Machine Learning for Bioenergy Research: Opportunities and Challenges

The integration of artificial intelligence and machine learning (AI/ML) with automated experimentation, genomics, biosystems design, and bioprocessing technologies is poised to revolutionize scientific investigation and, particularly, bioenergy research. To identify the opportunities and challenges in this emerging research area, the U.S. Department of Energy’s (DOE) Biological and Environmental Research program (BER) and Bioenergy Technologies Office (BETO) held a joint virtual workshop on AI/ML for Bioenergy Research (AMBER) on August 23–25, 2022. These interests have since been amplified in a September 2022 Executive Order, “Advancing Biotechnology and Biomanufacturing Innovation for a Sustainable, Safe, and Secure U.S. Bioeconomy,” to promote a whole-of government approach to biotechnology development (White House 2022). Approximately 50 scientists with various backgrounds and expertise from academia, industry, and DOE national laboratories met to discuss the opportunities and challenges of AI/ML for bioenergy research. Workshop participants were tasked with assessing the potential for AI/ML and laboratory automation to advance biological understanding and engineering in general. They particularly examined how integrating AI/ML tools with laboratory automation could accelerate biosystems design and optimize biomanufacturing. Discussions included the data and computational infrastructure needed to augment biosystems design applications and the expertise and workforce development efforts urgently required to shift integrated systems toward bioenergy research more broadly. Participants discussed many existing and future applications of AI/ML for biosystems design ranging from enzymes to plants and microbes, microbiomes, and bioprocess development. They also identified three key categories of scientific and technical opportunities and challenges: high-quality data, AI/ML algorithms, and laboratory automation. Several main takeaways emerged from the workshop: 1. Numerous AI/ML and automated experimentation applications exist for a variety of DOE mission needs in energy and the environment; 2. Exemplary research grand challenges for which AI/ML could provide solutions include: building microbes and microbial communities to specifications, developing closed-loop autonomous design and control for biosystems design, and advancing scale-up and automation; 3. Lack of sufficient high-quality, annotated data hinders the development of AI/ML applications; 4. New and improved AI/ML tools are needed, particularly those meeting the specific needs of the BER and BETO research communities; 5. Trade-offs in performance, cost, and reliability exist between deploying commercially available versus building custom-developed instrumentation and software for automated or autonomous experimentation; translation of manual to automated or autonomous methods is often a nontrivial endeavor; 6. Training a new generation of young scientists who can develop and apply AI/ML tools is needed to solve long-standing scientific challenges in bioenergy research. The integration of AI/ML tools and automated experimentation represents a new data-driven research paradigm complementary to the traditional hypothesis-driven research paradigm. This paradigm accelerates design and optimization of biological systems and processes for a variety of DOE mission needs in energy and the environment. The AMBER workshop broadly explored the potential of this new paradigm for bioenergy research, of particular interest to BER and BETO, and identified key challenges and opportunities that DOE can address in the coming years by leveraging its unique capabilities and resources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A science-driven approach to optimize the design for a biological small-angle neutron scattering instrument

Biological small-angle neutron scattering (SANS) instruments facilitate critical analysis of the structure and dynamics of complex biological systems. However, with the growth of experimental demands and the advances in optical systems design, a new neutron optical concept is necessary to overcome the limitations of current instruments. This work presents an approach to include experimental objectives ( i.e. the science to be supported by a specific neutron scattering instrument) in the optimization of the neutron optical concept. The approach for a proposed SANS instrument at the Second Target Station of the Spallation Neutron Source at Oak Ridge National Laboratory, USA, is presented here. Further, the instrument is simulated with the McStas software package. The optimization process is driven by an evolutionary algorithm using McStas output data, which are processed to calculate an objective function designed to quantify the expected performance of the simulated neutron optical configuration for the intended purpose. Each McStas simulation covers the complete instrument, from source to detector, including realistic sample scattering functions. This approach effectively navigates a high-dimensional parameter space that is otherwise intractable; it allows the design of next-generation SANS instruments to address specific scientific cases and has the potential to increase instrument performance compared with traditional design approaches.

47 OTHER INSTRUMENTATION↗
NASA NTRSDate not supplied

Yeast Strain Development and Hardware Testing in Preparation of a Lunar BioSensor

With Artemis missions underway, it is clear we are going back to the Moon to stay. Before sending Astronauts for long-duration missions, it is crucial to understand the technological and biomedical countermeasures needed to protect them before they get there. We can use knowledge gained from biological CubeSats to guide the next generation of experiments to support human habitation on the Moon. Lunar Explorer Instrument for space biology Applications (LEIA) is NASA’s latest BioSensor, adapted BioSentinel, the only CubeSat to travel Beyond Low Earth Orbit. BioSentinel launched on Artemis I and is currently >50 million kilometers from Earth (as of July 2024). LEIA aims to identify biological responses to the Lunar environment, which unprotected against would pose a threat to astronauts (cancer, cardiovascular disease, neurological impairment). The suite of instruments within LEIA detects Lunar radiation using two on-board radiation sensors (ARES charged particle detector, Mini-Fast Neutron Detector), then monitors real-time biological responses to the Lunar environment via an autonomous microfluidic system, fit with 3-LED emitter and detector boards and the alamarBlue metabolic indicator dye. LEIA will use a genetic approach in addition to synthetic biology to test counter-measure production in space, with the goal to inform and protect astronauts for future Moon missions. We have conducted preliminary tests in preparation for launch to the anticipated South Pole of the Moon, optimizing the biology (strain down-selection, desiccation tolerance, radiation sensitivity) and improving the hardware (including a blue LED to detect the beta-carotene countermeasure product). Our team will discuss these findings in several parts – an overview of the LEIA mission (Mark Settles), adapting flexible CubeSat platforms for deep-space applications (Sergio Santa Maria, Kira Rienecker), developing new technologies to support LEIA ground studies (Chinmayee Govinda Raj), and yeast strain development and hardware testing in preparation for LEIA (presented here).

synthetic biology↗
DOE OSTI2017

Amide linkages mimic phosphates in RNA interactions with proteins and are well tolerated in the guide strand of short interfering RNAs

While the use of RNA interference (RNAi) in molecular biology and functional genomics is a well-established technology, in vivo applications of synthetic short interfering RNAs (siRNAs) require chemical modifications. We recently found that amides as non-ionic replacements for phosphodiesters may be useful modifications for optimization of siRNAs. Herein, we report a comprehensive study of systematic replacement of a single phosphate with an amide linkage throughout the guide strand of siRNAs. The results show that amides are surprisingly well tolerated in the seed and central regions of the guide strand and increase the silencing activity when placed between nucleosides 10 and 12, at the catalytic site of Argonaute. A potential explanation is provided by the first crystal structure of an amide-modified RNA–DNA with Bacillus halodurans RNase H1. The structure reveals how small changes in both RNA and protein conformation allow the amide to establish hydrogen bonding interactions with the protein. Molecular dynamics simulations suggest that these alternative binding modes may compensate for interactions lost due to the absence of a phosphodiester moiety. Our results suggest that an amide can mimic important hydrogen bonding interactions with proteins required for RNAi activity and may be a promising modification for optimization of biological properties of siRNAs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Precursor prioritization for p-cymene production through synergistic integration of biology and chemistry

Abstract The strategy of synergistic application of biological and chemical catalysis is an important approach for efficiently converting renewable biomass into chemicals and fuels. In particular, the method of determining the appropriate intermediate between the two catalytic methods is critical. In this work, we demonstrate p -cymene production through the integration of biosynthesis and heterogenous catalysis and show how a preferred biologically derived precursor could be determined. On the biological side, we performed the limonene and 1,8-cineole production through the mevalonate pathway. Titers of 0.605 g/L and a 1.052 g/L were achieved, respectively. This difference is in agreement with the toxicity of these compounds toward the producing microorganisms, which has implications for subsequent development of the microbial platform. On the heterogeneous catalysis side, we performed the reaction with both biological precursors to allow for direct comparison. Using hydrogenation/dehydrogenation metals on supports with acid sites, both limonene and 1,8-cineole were converted to p -cymene with similar yields under equivalent reaction conditions. Thus, we could determine that the most promising strategy would be to target 1,8-cineole, the higher titer and lower toxicity bio-derived precursor with subsequent catalytic conversion to p -cymene. We further optimized the biological production of 1,8-cineole via fed-batch fermentation and reached the titer of 4.37 g/L which is the highest known 1,8-cineole titer from microbial production. This work provides a valuable paradigm for early stage considerations to determine the best route for the high-efficiency production of a target biobased molecule using an integration of biology and chemistry.

09 BIOMASS FUELS↗
DOE OSTI2018

Biological System Characterization to Address Biofouling of the 200 West Pump-and-Treat Injection Wells

Contaminated groundwater in the 200W Area of the Hanford Site is being treated ex situ by the 200W Pump-and-Treat (P&T) Facility and the subsurface plume is under hydraulic containment. The 200W P&T Facility removes several contaminants of concern from groundwater, as well as nitrate. Facility capacity is constrained by biofouling of injection wells, which limits the ability to efficiently inject treated effluent water in to the subsurface. In a previous report, Thomle et al. provided a comprehensive engineering assessment of biofouling at the 200W P&T Facility. In that report, both operational changes and facility modifications were recommended to help mitigate the symptoms and consequences of biofouling by expanding treatment processes in the plant. The 200W P&T Facility’s fluidized bed reactor (FBR) is a biological treatment system that has been identified as a principal source of biofouling. Carbon and nutrient inputs to the FBR are needed to sustain high rates of activity, but can also be responsible for microbial proliferation in the effluent water distribution pipeline, and loss injection well capacity. The primary objective of this work was to fill critical knowledge gaps associated with the FBR that can be used to optimize the biological system for continuous operation and sustained performance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Revisiting Theoretical Tools and Approaches for the Valorization of Recalcitrant Lignocellulosic Biomass to Value-Added Chemicals

Biorefinery processes for converting lignocellulosic biomass to fuels and chemicals proceed via an integrated series of steps. Biomass is first pretreated and deconstructed using chemical catalysts and/or enzymes to liberate sugar monomers and lignin fragments. Deconstruction is followed by a conversion step in which engineered host organisms assimilate the released sugar monomers and lignin fragments, and produce value-added fuels and chemicals. Over the past couple of decades, a significant amount of work has been done to develop innovative biomass deconstruction and conversion processes that efficiently solubilize biomass, separate lignin from the biomass, maximize yields of bioavailable sugars and lignin fragments and convert the majority of these carbon sources into fuels, commodity chemicals, and materials. Herein, we advocate that advanced in silico approaches provide a theoretical framework for developing efficient processes for lignocellulosic biomass valorization and maximizing yields of sugars and lignin fragments during deconstruction and fuel and chemical titers during conversion. This manuscript surveys the latest developments in lignocellulosic biomass valorization with special attention given to highlighting computational approaches used in process optimization for lignocellulose pretreatment; enzyme engineering for enhanced saccharification and delignification; and prediction of the genome modification necessary for desired pathway fine-tuning to upgrade products from biomass deconstruction into value-added products. Physics-based modeling approaches such as density functional theory calculations and molecular dynamics simulations have been most impactful in studies aimed at exploring the molecular level details of solvent-biomass interactions, reaction mechanisms occurring in biomass-solvent systems, and the catalytic mechanisms and engineering of enzymes involved in biomass degradation. More recently, with ever increasing amounts of data from, for example, advanced mutli-omics experiments, machine learning approaches have begun to make important contributions in synthetic biology and optimization of metabolic pathways for production of biofuels and chemicals.

09 BIOMASS FUELS↗
DOE OSTI2015

WE-B-304-01: Treatment Planning Evaluation and Optimization Should Be Dose/volume and Not Biologically Based

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning by the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Enabling Lignin Valorization Through Integrated Advances in Plant Biology and Biorefining

Despite lignin having long been viewed as an impediment to the processing of biomass for the production of paper, biofuels, and high-value chemicals, the valorization of lignin to fuels, chemicals, and materials is now clearly recognized as a critical element for the lignocellulosic bioeconomy. However, the intended application for lignin will likely require a preferred lignin composition and form. To that end, effective lignin valorization will require the integration of plant biology, providing optimal feedstocks, with chemical process engineering, providing efficient lignin transformations. Recent advances in our understanding of lignin biosynthesis have shown that lignin structure is extremely diverse and potentially tunable, while simultaneous developments in lignin refining have resulted in the development of several processes that are more agnostic to lignin composition. Here, we review the interface between in planta lignin design and lignin processing and discuss the advances necessary for lignin valorization to become a feature of advanced biorefining.

09 BIOMASS FUELS↗
DOE OSTI2015

Exploiting algal NADPH oxidase for biophotovoltaic energy

Summary Photosynthetic microbes exhibit light‐dependent electron export across the cell membrane, which can generate electricity in biological photovoltaic (BPV) devices. How electrons are exported remains to be determined; the identification of mechanisms would help selection or generation of photosynthetic microbes capable of enhanced electrical output. We show that plasma membrane NADPH oxidase activity is a significant component of light‐dependent generation of electricity by the unicellular green alga Chlamydomonas reinhardtii . NADPH oxidases export electrons across the plasma membrane to form superoxide anion from oxygen. The C. reinhardtii mutant lacking the NADPH oxidase encoded by RBO1 is impaired in both extracellular superoxide anion production and current generation in a BPV device. Complementation with the wild‐type gene restores both capacities, demonstrating the role of the enzyme in electron export. Monitoring light‐dependent extracellular superoxide production with a colorimetric assay is shown to be an effective way of screening for electrogenic potential of candidate algal strains. The results show that algal NADPH oxidases are important for superoxide anion production and open avenues for optimizing the biological component of these devices.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Chapter 12: Gene Editing Technologies for Biofuel Production in Thermophilic Microbes

Thermophilic microbes are an attractive bioproduction platform due to their inherently lower contamination risk and their ability to perform thermostable enzymatic processes which may be required for biomass processing and other industrial applications. The engineering of microbes for industrial scale processes requires a suite of genetic engineering tools to optimize existing biological systems as well as to design and incorporate new metabolic pathways within strains. Yet, such tools are often lacking and/or inadequate for novel microbes, especially thermophiles. This chapter focuses on genetic tool development and engineering strategies, in addition to challenges, for thermophilic microbes. We provide detailed instructions and techniques for tool development for an anaerobic thermophile, Caldanaerobacter subterraneus subsp. tengcongensis, including culturing, plasmid construction, transformation, and selection. This establishes a foundation for advanced genetic tool development necessary for the metabolic engineering of this microbe and potentially other thermophilic organisms.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗
DOE OSTI2015

Genetic and genomic analysis of RNases in model cyanobacteria

Cyanobacteria are diverse photosynthetic microbes with the ability to convert CO 2 into useful products. However, metabolic engineering of cyanobacteria remains challenging because of the limited resources for modifying the expression of endogenous and exogenous biochemical pathways. Fine-tuned control of protein production will be critical to optimize the biological conversion of CO 2 into desirable molecules. Messenger RNA (mRNA) are labile intermediates that play critical roles in determining the translation rate and steady state protein concentrations in the cell. The majority of studies on mRNA turnover have focused on the model heterotrophic bacteria Escherichia coli and Bacillus subtilis . These studies have elucidated many RNA modifying and processing enzymes and have highlighted the differences between these Gram-negative and Gram-positive bacteria, respectively. In contrast, much less is known about mRNA turnover in cyanobacteria. We generated a compendium of the major ribonucleases (RNases) and provide an in-depth analysis of RNase III-like enzymes in commonly studied and diverse cyanobacteria. Furthermore, using targeted gene deletion, we genetically dissected the RNases in Synechococcus sp. PCC 7002, one of the fastest growing and industrially attractive cyanobacterial strains. We found that all three cyanobacterial homologs of RNase III and a member of the RNase II/R family are not essential under standard laboratory conditions, while homologs of RNase E/G, RNase J1/J2, PNPase, and a different member of the RNase II/R family appear to be essential for growth. In conclusion, this work will enhance our understanding of native control of gene expression and will facilitate the development of an RNA-based toolkit for metabolic engineering in cyanobacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Microbial production of fuels, commodity chemicals, and materials from sustainable sources of carbon and energy

Anthropogenic carbon emissions are driving rapid changes to the earth's climate, disrupting whole ecosystems and endangering the stability of human society. Innovations in engineered microbial fermentation enable the fossil resource-free production of fuels, commodity chemicals, and materials, thereby reducing the carbon emissions associated with these products. Microorganisms have been engineered to catabolize sustainable sources of carbon and energy (i.e., plant biomass, plastic waste, and one-carbon feedstocks) and biosynthesize carbon-neutral or carbon-negative products. These engineering efforts exploit and optimize natural biological pathways or generate unnatural pathways which can biosynthesize chemicals that have not yet been accessed using synthetic chemistry. Further, recent advances in microbial fermentation seek not only to maximize the titer, rate, and yield of desired products, but also to tailor microbial catabolism to utilize inexpensive feedstocks. Ultimately, these advances aim to lower the cost of bioproduction so that microorganism-derived chemicals can be economically competitive with fossil-derived chemicals.

09 BIOMASS FUELS↗
DOE OSTI2019

Performance of biologically inspired algorithms tuned on TiO 2 nanoparticle benchmark system

Biologically inspired optimization algorithms (BIOA), such as particle swarm optimization (PSO) and differential evolution (DE), are often applied to a specific problem with the tacit assumptions that each algorithm will have a unique set of “ideal” tuning parameters that provide “best” performance, and that, once tuned, two different optimization algorithms ought to exhibit distinct performance. This work tests those assumptions by systematically comparing PSO and DE applied to identify stable and metastable configurations of a benchmark system, a titania dimer Ti 2 O 4 , whose energy is described by a modified Matsui-Akaogi pairwise potential. The hypotheses (H) are formally expressed as: (H1) for both PSO and DE there exists a unique, optimal set of tuning parameters that maximize its performance on a given problem; (H2) when running two different BIOAs, one algorithm must demonstrate superior performance over the other. By identifying and studying an approximate Pareto-optimal set of tuning parameters for DE and PSO, H1 is shown to be strictly false for DE, and tentatively false for PSO, while H2 is shown to be false with respect to certain statistics. Here, we further investigate the correlations between key tuning parameters and algorithm performance. Our conclusion may be implemented to further optimize and develop BIOAs applied to materials systems.

36 MATERIALS SCIENCE↗