A data ecosystem to support machine learning in materials science

Blaiszik, Ben; Ward, Logan; Schwarting, Marcus; Gaff, Jonathon; Chard, Ryan; Pike, Daniel W.; Chard, Kyle; Foster, Ian

doi:10.1557/mrc.2019.118

Cited by 155 publications

(105 citation statements)

References 29 publications

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“…These uniform assemblies were slightly larger than the PCMs formed from PEG-PLK and PEG-PVBTMA, attributed to the highly solvated PMPC corona that accounts for the difference between the lengths determined by a hydrodynamic volume and Guinier approximation for each scattering technique, respectively. Scattering data are available through the Materials Data Facility [69,70] at doi:10.18126/n4un-6usf [71].…”

Section: Discussionmentioning

confidence: 99%

Comparing Zwitterionic and PEG Exteriors of Polyelectrolyte Complex Micelles

et al. 2020

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A series of model polyelectrolyte complex micelles (PCMs) was prepared to investigate the consequences of neutral and zwitterionic chemistries and distinct charged cores on the size and stability of nanocarriers. Using aqueous reversible addition-fragmentation chain transfer (RAFT) polymerization, we synthesized a well-defined diblock polyelectrolyte system, poly(2-methacryloyloxyethyl phosphorylcholine methacrylate)-block-poly((vinylbenzyl) trimethylammonium) (PMPC-PVBTMA), at various neutral and charged block lengths to compare directly against PCM structure–property relationships centered on poly(ethylene glycol)-block-poly((vinylbenzyl) trimethylammonium) (PEG-PVBTMA) and poly(ethylene glycol)-block-poly(l-lysine) (PEG-PLK). After complexation with a common polyanion, poly(sodium acrylate), the resulting PCMs were characterized by dynamic light scattering (DLS) and small angle X-ray scattering (SAXS). We observed uniform assemblies of spherical micelles with a diameter ~1.5–2× larger when PMPC-PVBTMA was used compared to PEG-PLK and PEG-PVBTMA via SAXS and DLS. In addition, PEG-PLK PCMs proved most resistant to dissolution by both monovalent and divalent salt, followed by PEG-PVBTMA then PMPC-PVBTMA. All micelle systems were serum stable in 100% fetal bovine serum over the course of 8 h by time-resolved DLS, demonstrating minimal interactions with serum proteins and potential as in vivo drug delivery vehicles. This thorough study of the synthesis, assembly, and characterization of zwitterionic polymers in PCMs advances the design space for charge-driven micelle assemblies.

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Section: Discussionmentioning

confidence: 99%

Comparing Zwitterionic and PEG Exteriors of Polyelectrolyte Complex Micelles

et al. 2020

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“…DLHub also provides a model serving framework for executing inference and other tasks over arbitrary data. DLHub has been exploited by several research projects with success, such as [30], [31].…”

Section: Related Work Regarding Machine Learning Platformsmentioning

confidence: 99%

A Cloud-Based Framework for Machine Learning Workloads and Applications

et al. 2020

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In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models. INDEX TERMS Cloud computing, computers and information processing, deep learning, distributed computing, machine learning, serverless architectures.

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“…The raw and processed data required to reproduce these findings are available to download from https://doi.org/10.18126/TW5W-XTWE [53] via the Materials Data Facility [54,55].…”

Section: Research Datamentioning

confidence: 99%

Biofilm and Cell Adhesion Strength on Dental Implant Surfaces via the Laser Spallation Technique

Boyd

Miller

Grady

2019

Preprint

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ObjectivesThe aim of this study is to quantify the adhesion strength differential between an oral bacterial biofilm and an osteoblast-like cell monolayer to a dental implant-simulant surface and develop a metric that quantifies the biocompatible efficacy of implant surfaces.MethodsHigh-amplitude short-duration stress waves generated by laser pulse absorption are used to spall bacteria and cells from titanium substrates. By carefully controlling laser fluence and calibration of laser fluence with applied stress, the adhesion difference between dental carry Streptococcus mutans biofilms and MG 63 osteoblast-like cell monolayers on smooth and rough titanium substrates is obtained. The Adhesion Index consists of a ratio of cell adhesion strength to biofilm adhesion strength obtaining a nondimensionalized parameter for biocompatibility assessments.ResultsAdhesion strength of 145±42 MPa is measured for MG 63 on smooth titanium, which increases to 288±24 MPa on roughened titanium. Adhesion strength for S. mutans on smooth titanium is 315±9 MPa and remained relatively constant at 332±9 MPa on roughened titanium. The Adhesion Index for smooth titanium is 0.46±0.12 which increased to 0.87±0.05 on roughened titanium.SignificanceThe laser spallation technique provides a platform to examine the tradeoffs of adhesion modulators on both biofilm and cell adhesion. This tradeoff is characterized by the Adhesion Index, which is proposed to aid biocompatibility screening and could result in improved implantation outcomes. The Adhesion Index is implemented to determine surface factors that promote favorable adhesion of cells greater than biofilms. Here, an Adhesion Index >> 1 suggests favorable biocompatibility.Graphical AbstractHighlightsBiofilm and cell monolayer adhesion are measured via the laser spallation techniqueSmooth and roughened dental implant-mimicking titanium surfaces are investigatedSurface roughness increases cell adhesion but does not alter the adhesion of biofilmsAn Adhesion Index is developed to directly quantify the adhesive competition between bacteria and cells on an implant surface

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A data ecosystem to support machine learning in materials science

Cited by 155 publications

References 29 publications

Comparing Zwitterionic and PEG Exteriors of Polyelectrolyte Complex Micelles

Comparing Zwitterionic and PEG Exteriors of Polyelectrolyte Complex Micelles

A Cloud-Based Framework for Machine Learning Workloads and Applications

Biofilm and Cell Adhesion Strength on Dental Implant Surfaces via the Laser Spallation Technique

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