Discovering predictive rules of chemistry from property landscapes

Tibbetts, Katharine Moore; Li, Richard; Pelczer, István; Rabitz, Herschel

doi:10.1016/j.cplett.2013.03.040

Cited by 9 publications

(11 citation statements)

References 22 publications

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“…Identification and interpretation of orderings O that produce meaningful correlations with reactivity and other chemical properties have been investigated by OptiChem principles in the context of optimization with chemical reagents and substrates in ref. [34][35][36]. A well-known example of ordering is based on the nucleophilic strength of halogen anions increasing with their polarizability (i.e., F À o Cl À o Br À o I À ).…”

Section: Introductionmentioning

confidence: 99%

“…In the context of chemistry, the correlation between properties and suitable choices of O can in some cases be explained by known physical properties that depend on chemical composition (e.g., electron-donating ability, steric effects). 35,36 In the domain of strong field interaction with molecules, the lack of fundamental understanding about the systematics of photonic reagent-substrate interactions presents a significant challenge to fully explain the physical basis of the observed trends. The correlations observed in this work have an evident qualitative relation to the electronic character expected of the halogen sequence F -Cl -Br -I.…”

Section: Introductionmentioning

confidence: 99%

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Optimal control of molecular fragmentation with homologous families of photonic reagents and chemical substrates

Tibbetts

Rabitz

2013

Phys. Chem. Chem. Phys.

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This work investigates laser optimally controlled fragmentation in a series of chemically related halomethane compounds in order to determine the extent to which regular trends in objective yields are observed upon variation of both the laser pulse shape and the chemical substrate. Three control objectives defined by ratios of fragment ions were considered, where families of shaped pulses were found that produced optimal objective yields. For the objective defined by the ratio of a halogen ion to a methyl halide ion, a systematic correlation between the optimal objective yield and chemical composition was revealed. Furthermore, some of the optimal shaped pulses were found to successfully control the same objective in closely related molecular substrates. These results provide a basis to expect systematic chemical responses from optimally shaped laser pulses acting as "photonic reagents" in analogy with the action of traditional chemical reagents.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal control of molecular fragmentation with homologous families of photonic reagents and chemical substrates

Tibbetts

Rabitz

2013

Phys. Chem. Chem. Phys.

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“…The task of a genetic algorithm is to scan the search space of the gene domains to identify the most suitable phenotypes, as measured by a fitness function. The relationship between the genome and the phenotype gives rise to the fitness landscape (see Tibbetts et al [ 2 ] for a detailed background). Figure 3 illustrates a fitness landscape for two hypothetical genes, say, block size and processing temperature of a polymer synthesis process whose aim is to achieve high rates of hardness.…”

Section: Materials Discoverymentioning

confidence: 99%

Big data and machine learning for materials science

et al. 2021

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Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.

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“…Figure illustrates fitness landscapes with two variables where the landscape contains multiple local maxima with different property values and is monotonic. Thus, evolutionary algorithms are applicable to any well-defined chemical or physical property and to all molecules or materials where it is possible to specify an appropriate genome that permits free movement on the fitness landscape …”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Discovery and Optimization of Materials Using Evolutionary Approaches

2016

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Materials science is undergoing a revolution, generating valuable new materials such as flexible solar panels, biomaterials and printable tissues, new catalysts, polymers, and porous materials with unprecedented properties. However, the number of potentially accessible materials is immense. Artificial evolutionary methods such as genetic algorithms, which explore large, complex search spaces very efficiently, can be applied to the identification and optimization of novel materials more rapidly than by physical experiments alone. Machine learning models can augment experimental measurements of materials fitness to accelerate identification of useful and novel materials in vast materials composition or property spaces. This review discusses the problems of large materials spaces, the types of evolutionary algorithms employed to identify or optimize materials, and how materials can be represented mathematically as genomes, describes fitness landscapes and mutation operators commonly employed in materials evolution, and provides a comprehensive summary of published research on the use of evolutionary methods to generate new catalysts, phosphors, and a range of other materials. The review identifies the potential for evolutionary methods to revolutionize a wide range of manufacturing, medical, and materials based industries. CONTENTSCorresponding Author 6130 Notes 6130 Biographies 6130 References 6130

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Discovering predictive rules of chemistry from property landscapes

Cited by 9 publications

References 22 publications

Optimal control of molecular fragmentation with homologous families of photonic reagents and chemical substrates

Optimal control of molecular fragmentation with homologous families of photonic reagents and chemical substrates

Big data and machine learning for materials science

Discovery and Optimization of Materials Using Evolutionary Approaches

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