Renewable energy: Present research and future scope of Artificial Intelligence

Jha, Sunil; Bilalovic, Jasmin; Jha, Anju; Patel, Nilesh; Zhang, Han

doi:10.1016/j.rser.2017.04.018

Cited by 227 publications

(58 citation statements)

References 350 publications

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“…Extensive research is shifting towards employing machine learning in theoretical chemistry for developing inter‐atomic potentials or performing ab initio molecular dynamics (AIMD) simulations . ML is also employed in protein design, molecular design, drug design or materials discovery …”

Section: Path Ahead: Vimi For Ml‐based Catalyst Designmentioning

confidence: 99%

“…[48] ML is also employed in protein design, [49] molecular design, [50,51] drug design [52] or materials discovery. [53][54][55] Through several ground-breaking works by Norskov and his coworkers [56][57][58][59][60] on developing the d-band model and the computational hydrogen electrode (CHE) scheme, quantum mechanical calculations based on DFT have become the quintessential methodology for understanding reaction mechanisms and predicting activity and selectivity of catalyst materials for electrochemical energy storage and conversion (see review paper [22] and references therein). However, complexity of the electrode-electrolyte interface and the immense parameter space it entails limit such methods in terms of consistency and accuracy, and their applicability in materials screening.…”

Section: Path Ahead: Vimi For Ml-based Catalyst Designmentioning

confidence: 99%

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Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts

et al. 2019

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Similar to advancements gained from big data in genomics, security, internet of things, and e‐commerce, the materials workflow could be made more efficient and prolific through advances in streamlining data sources, autonomous materials synthesis, rapid characterization, big data analytics, and self‐learning algorithms. In electrochemical materials science, data sets are large, unstructured/heterogeneous, and difficult to process and analyze from a single data channel or platform. Computer‐aided materials design together with advances in data mining, machine learning, and predictive analytics are expected to provide inexpensive and accelerated pathways towards tailor‐made functionally optimized energy materials. Fundamental research in the field of electrochemical energy materials focuses primarily on complex interfacial phenomena and kinetic electrocatalytic processes. This perspective article critically assesses AI‐driven modeling and computational approaches that are currently applied to those objects. An application‐driven materials intelligence platform is introduced, and its functionalities are scrutinized considering the development of electrocatalyst materials for CO2 conversion as a use case.

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Section: Path Ahead: Vimi For Ml‐based Catalyst Designmentioning

confidence: 99%

Section: Path Ahead: Vimi For Ml-based Catalyst Designmentioning

confidence: 99%

Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts

et al. 2019

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“…Polynomial neural networks (PNNs) provide an efficient, general interpolation method for nonlinear functions of several variables and are applied in potential energy surfaces study (Blank et al, 1995). The outcomes related to wind energy, solar energy, geothermal energy, hydro energy, ocean energy, bioenergy, hydrogen energy, and hybrid energy have been summarised (Jha et al, 2017). To estimate hourly values of the diffuse solar-radiation at the surface in São Paulo City, Brazil, a perceptron neural-network technique was applied (Soares et al, 2004).…”

Section: Literature Reviewmentioning

confidence: 99%

Application of AHP-VIKOR and GMDH framework to develop an indicator to identify utilisation potential of wave energy converter with respect to location

Chakraborty

Majumder

Khare

2019

IJSTDS

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The potential of analytical hierarchy process (AHP)-rough number based compromise ranking method (also known as VIKOR) multi-criteria decision-making (MCDM) and group method of data handling (GMDH) multimodal predictive method was analysed for smart representation of 'utilisation potential' of wave energy converters from specific locations. The significant parameters were identified by considering different case studies and their influence on converter efficiency. The soft-computation methods like AHP-VIKOR and GMDH were used for the first time to find the relative priority values of the parameters. The results from the multi-method estimation model were validated with the help of multi linear regression equation and certain real-time case analysis. With an accuracy of above, 99% the ensemble MCDM-ANN model depicted reliability, which ensures the author of its wide application for real benefits like cost reduction and efficiency maximisation of converters in the utilisation of the potential wave energy of locations.

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“…[44][45][46][47][48][49][50] These models are acceptable for use in specific scenarios. [44][45][46][47][48][49][50] These models are acceptable for use in specific scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…The third technique for modelling PEMFC systems involves the use of artificial intelligence (AI). [44][45][46][47][48][49][50] These models are acceptable for use in specific scenarios. AI models lack generality and require design variations to facilitate their adaptation to different PEMFC-system types.…”

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confidence: 99%

Membrane‐hydration‐state detection in proton exchange membrane fuel cells using improved ambient‐condition‐based dynamic model

et al. 2019

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Renewable energy: Present research and future scope of Artificial Intelligence

Cited by 227 publications

References 350 publications

Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts

Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts

Application of AHP-VIKOR and GMDH framework to develop an indicator to identify utilisation potential of wave energy converter with respect to location

Membrane‐hydration‐state detection in proton exchange membrane fuel cells using improved ambient‐condition‐based dynamic model

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