From Data to Knowledge:  Chemical Data Management, Data Mining, and Modeling in Polymer Science

Adams, Nico; Schubert, Ulrich S.

doi:10.1021/cc034021b

Cited by 33 publications

(24 citation statements)

References 97 publications

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“…The application of data-mining techniques in polymer science has been reported by Adams and Schubert. [104,105] The authors give an overview on the development of a detailed data-management system to handle highthroughput screening data and to derive knowledge out of these experiments. As a main focus of their work, they refer to an informatics infrastructure that was developed to support the experimental work and especially the analysis of screening data.…”

Section: Reviewsmentioning

confidence: 99%

Combinatorial and High‐Throughput Materials Science

2007

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There is increasing acceptance of high-throughput technologies for the discovery, development, and optimization of materials and catalysts in industry. Over the years, the relative synchronous development of technologies for parallel synthesis and characterization has been accompanied by developments in associated software and information technologies. This Review aims to provide a comprehensive overview on the state of the art of the field by selected examples. Technologies developed to aid research on complex materials are covered as well as databases, design of experiment, data-mining technologies, modeling approaches, and evolutionary strategies for development. Different methods for parallel synthesis provide single sample libraries, gradient libraries for electronic or optical materials, similar to polymers and catalysts, and products produced through formulation strategies. Many examples illustrate the variety of isolated solutions and document the barely recognized variety of new methods for the synthesis and analysis of almost any material. The Review ends with a summary of success stories and statements on still-present problems and future tasks.

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Section: Reviewsmentioning

confidence: 99%

Combinatorial and High‐Throughput Materials Science

2007

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“…[23] As a result, design of experiments (DoE), data-handling, and data-mining approaches will be more and more important. [24,25] Last year, we provided an overview of the status of combinatorial and parallel polymer synthesis and the available high-throughput screening techniques. [26] However, after the publication of this review many new contributions have been reported, which makes it worthwhile to describe the evolution of the field during the last year.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial Methods, Automated Synthesis and High‐Throughput Screening in Polymer Research: The Evolution Continues

Meier¹,

Hoogenboom²,

Schubert³

2003

Macromol. Rapid Commun.

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118

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Summary: For speeding‐up preparation as well as investigating new polymeric materials, combinatorial techniques, parallel experimentation, and high‐throughput screening methods represent a very promising approach in polymer chemistry: a large variety of parameters can be screened simultaneously resulting in new structure–property relationships. As previously described, polymer chemistry seems to be perfectly suited for combinatorial approaches since it is relatively easy to vary many parameters during the synthesis, processing, blending, or compounding. Moreover, the development and application of high‐throughput screening techniques for polymer properties can accelerate the development of new materials and can result in new structure–property relationships. Therefore, these screening tools, together with parallel preparation techniques, will significantly decrease the time to market of new products. Here we provide an update of our recent overview covering new developments in the field of combinatorial and parallel polymer synthesis and high‐throughput screening.Development of high‐throughput screening techniques for polymer properties.magnified imageDevelopment of high‐throughput screening techniques for polymer properties.

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“…Modern R&D depends very much on data generated by high-throughput screening and experimentation for the identification of new chemical entities as drug candidates, new materials or catalysts [16]. In both materials and life sciences, computational methods are typically involved for Figure 1.…”

Section: The Data and Process Flow In Randdmentioning

confidence: 99%

“…Experimental planning, screening and optimization campaigns, data validation and analysis, visualization and reporting are the subsequent steps in the process. We will not discuss these issues here that are very well described elsewhere [16,18]. The workflow can be reiterated several times and objectives and constraints reassessed accordingly.…”

Section: The Data and Process Flow In Randdmentioning

confidence: 99%

Integrated Informatics in Life and Materials Sciences: An Oxymoron?

Gilardoni¹,

Ćurčin

Karunanayake³

et al. 2005

QSAR Comb. Sci.

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The pharmaceutical and chemical industries are facing significant internal and external pressure to boost the experimental efficiency and effectiveness by cutting the direct research costs and reducing the time to market for new sustainable products. Other key issues to secure a competitive return on investment are to enable the rescue of stalled product development projects, abort failing projects early, enhance collaborative multidisciplinary ventures, and ensure an effectual risk management and cost savings through safety testing and failure analysis. Typically, modern organizations rely on a variety of informatics solutions from well-established software vendors. These typically operate on different platforms and are controlled by different management systems using different data types and proprietary formats. Maintaining, integrating, updating and monitoring these powerful, but disparate, ensembles of tools, are an intricate and expensive operation. We present the concept of the Enterprise Discovery Planning (EDP) software platform design to facilitate the optimization of discovery activities at an enterprise level. We also briefly present how Inforsense addresses these issues.

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From Data to Knowledge: Chemical Data Management, Data Mining, and Modeling in Polymer Science

Cited by 33 publications

References 97 publications

Combinatorial and High‐Throughput Materials Science

Combinatorial and High‐Throughput Materials Science

Combinatorial Methods, Automated Synthesis and High‐Throughput Screening in Polymer Research: The Evolution Continues

Integrated Informatics in Life and Materials Sciences: An Oxymoron?

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