Discovery and Optimization of Materials Using Evolutionary Approaches

Le, Tu C.; Winkler, David A.

doi:10.1021/acs.chemrev.5b00691

Cited by 167 publications

(150 citation statements)

References 85 publications

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“…These limitations can be ameliorated by the use of genetic programming methods to construct DTs. Genetic programming is a member of the broad class of evolutionary algorithms that can efficiently search very large parameter spaces for locally optimal solutions to high dimensional materials spaces (Le and Winkler, 2016). The application of evolutionary algorithms for discovery and optimization of materials has been reviewed very recently (Le and Winkler, 2016 be successfully applied to modelling of ecotoxicity data (Buontempo et al, 2005).…”

Section: Methodsmentioning

confidence: 99%

“…Genetic programming is a member of the broad class of evolutionary algorithms that can efficiently search very large parameter spaces for locally optimal solutions to high dimensional materials spaces (Le and Winkler, 2016). The application of evolutionary algorithms for discovery and optimization of materials has been reviewed very recently (Le and Winkler, 2016 be successfully applied to modelling of ecotoxicity data (Buontempo et al, 2005). As the details of the technique can be found in literature (Wang et al, 2006, Buontempo et al, 2005, only a basic overview of the method is provided here.…”

Section: Methodsmentioning

confidence: 99%

“…Briefly, GPTree begins with a random population of solutions and repeatedly attempts to find better solutions by applying genetic operators such as mutation and crossover (for descriptions of these operators see Le and Winkler (2016)). The first step is to construct a user-specified number of trees (usually a large number) starting from a random compound and randomly chosen descriptor.…”

Section: Methodsmentioning

confidence: 99%

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Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

et al. 2016

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The number of engineered nanomaterials (ENMs) being exploited commercially is growing rapidly, due to the novel properties they exhibit. Clearly, it is important to understand and ameliorate any risks to health or the environment posed by the presence of ENMs. Datadriven models that decode the relationships between the biological activities of ENMs and their physicochemical characteristics provide an attractive means of maximizing the value of scarce and expensive experimental data. Although such structure-activity relationship (SAR) methods have become very useful tools for modelling nanotoxicity endpoints (nanoSAR), they have limited robustness and predictivity, and interpretation of the models they generate can be problematic. New computational modelling tools or new ways of using existing tools are required to model the relatively sparse and sometimes lower quality data on the biological effects of ENMs. The most commonly used SAR modelling methods work best with large data sets, are not particularly good at feature selection, and may not account for nonlinearity in the structure-property relationships. To overcome these limitations, we describe the application of a novel algorithm, a genetic programming-based decision tree construction tool (GPTree) to nanoSAR modelling. We demonstrate the use of GPTree in the construction of accurate and interpretable nanoSAR models by applying it to four diverse literature datasets.We describe the algorithm and compare model results across the four studies. We show that GPTree generates models with accuracies equivalent to or superior to those of prior modelling studies on the same datasets. GPTree is a robust, automatic method for generation of accurate nanoSAR models with additional advantages that it works with small datasets, automatically selects descriptors, and provides improved interpretability of models.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Accurate and interpretable nanoSAR models from genetic programming-based decision tree construction approaches

et al. 2016

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“…Such models can be used to understand the relationships between the chemical structure of inhibitors and their efficacy and to allow the inhibition of compounds not yet synthesized or tested to be predicted. They can also be used as surrogate fitness functions for the evolutionary design of new corrosion inhibitors with multiple desirable properties [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Performance of Organic Corrosion Inhibitors

Winkler

2017

Metals

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Abstract:The withdrawal of effective but toxic corrosion inhibitors has provided an impetus for the discovery of new, benign organic compounds to fill that role. Concurrently, developments in the high-throughput synthesis of organic compounds, the establishment of large libraries of available chemicals, accelerated corrosion inhibition testing technologies, and the increased capability of machine learning methods have made discovery of new corrosion inhibitors much faster and cheaper than it used to be. We summarize these technical developments in the corrosion inhibition field and describe how data-driven machine learning methods can generate models linking molecular properties to corrosion inhibition that can be used to predict the performance of materials not yet synthesized or tested. We briefly summarize the literature on quantitative structure-property relationships models of small organic molecule corrosion inhibitors. The success of these models provides a paradigm for rapid discovery of novel, effective corrosion inhibitors for a range of metals and alloys in diverse environments.

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“…Such a search space would be impossible to explore experimentally, and an exhaustive computational screening would be challenging for most properties, particularly if a quantum mechanical calculation is required in the screening process. One approach that has received attention in materials discovery is the use of evolutionary processes ( 13 ). …”

Section: Introductionmentioning

confidence: 99%