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Discovery of Novel Photocatalysts Using Machine Learning Approach

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Machine Learning for Advanced Functional Materials

Abstract

 Machine learning (ML) in photocatalysis research is still at its very beginning. Given recent developments, it is clear that computational screening techniques are a powerful and efficient means for achieving accelerated discovery of new materials. This is true of arriving at novel photocatalysts as well. In particular, structural, electronic, optical properties, diffusion and defect studies of photocatalysts are investigated using any one of or a combination of computational approaches such as density functional theory (DFT), molecular dynamics (MD) simulations, and ML method. There is also need to understand the underlying physics, wherein the multi-feature analysis approaches of materials informatics can play a role. However currently the major issue is the lack of training data. To overcome this bottleneck, we believe that the combination of continued photocatalysis experimental and computational research along with materials informatics can offer a way forward. We note here that to screen photocatalyst based on their efficiencies, ML technique would require accurate and adequate descriptors. Formation energy, cohesive energy, binding energy, energy band gap, conduction band minimum (CBM), valence band maximum (VBM), effective masses, dielectric constants, refractive index, absorption co-efficient, electron energy loss function, work function and defect formation energy are reasonable descriptors for determining photocatalytic hydrogen production activity. When compared to traditional approaches that are entirely experimental and/or computational, the ML model would offer an economical approach to rapid performance prediction, potentially relevant even for novel compositions. This chapter explains the state of the art in materials informatics as it pertains to photocatalysts. We recognize that this area is still at its nascent stage. Hence we will also indicate the key gaps that exist that require immediate attention and the roadmap forward. The combination of ML with photocatalysis expertise could pave the way for the creation of a comprehensive catalyst screening platform that would greatly help in making photocatalysis prominent and technologically relevant, at the same time.

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Authors and Affiliations

  1. Department of Physics, Research Institute for Natural Science, and Institute for High Pressure at Hanyang University, Hanyang University, 222 Wangsimni-Ro, Seongdong-Ku, Seoul, 04763, Republic of Korea

    G. Sudha Priyanga

  2. Institute of Physics, Bijenička C. 46, 10000, Zagreb, HR, Croatia

    Gaurav Pransu

  3. Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA

    Harshita Krishna

  4. Department of Metallurgical and Materials Engineering, Indian Institute of Technology Madras, Chennai, 600036, India

    Tiju Thomas

  5. Indian Solar Energy Harnessing Center (ISEHC) - An Energy Consortium, Indian Institute of Technology Madras, Chennai, 600036, India

    Tiju Thomas

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  1. G. Sudha Priyanga
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  4. Tiju Thomas
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Correspondence to Tiju Thomas .

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  1. University of Sao Paulo, Sao Carlos Institution of Physics, Grupo de Polimeros, São Paulo, Brazil

    Nirav Joshi

  2. Department of Civil Engineering Materials and Structures, Indian Institute of Technology Jammu, Jammu, India

    Vinod Kushvaha

  3. Proof of Concept and Innovation Group, Stanley Black and Decker (United States), Atlanta, GA, USA

    Priyanka Madhushri

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Priyanga, G.S., Pransu, G., Krishna, H., Thomas, T. (2023). Discovery of Novel Photocatalysts Using Machine Learning Approach. In: Joshi, N., Kushvaha, V., Madhushri, P. (eds) Machine Learning for Advanced Functional Materials. Springer, Singapore. https://doi.org/10.1007/978-981-99-0393-1_11

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