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WEB High Throughput Screening for Electrochemical Nitrogen Reduction for the Ammonia Synthesis via Machine Learning

Wednesday (05.08.2020)
08:47 - 08:47 Poster Room
Part of:
- Poster *web*Simulation of Spinning Processes with Experimental Validation 1 Stefan Hermanns
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- Poster *web*Numerical Study of Solidification in Metallic Droplet 1 Dandan Yao
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- Poster *web*Thermodynamic assessment and modelling of interactions in liquid Ni alloys/oxide systems 1 Saverio Sitzia
- Poster *web*The limiting effect of interfacial band alignment on efficiency of earth-abundant chalcogenide photovoltaic materials 1 Dr. Elaheh Ghorbani
- Poster *web*HMC hub matter for as a resource for materials research in the German research landscape 1 Dr. Oonagh Mannix
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- Poster *web*Rational Design of Metal Anchored Carbon Quantum Dots as Optimal Efficient Electrocatalysts for Hydrogen production Using Machine Learning and High-throughput Experimentation 1 Yujin Chae
- Poster *web*Correlating Raman Spectra of Ibuprofen, Nicotinamide and Their Dimers 1 Milad Asgarpour Khansary
- Poster *web*High Throughput Screening for Electrochemical Nitrogen Reduction for the Ammonia Synthesis via Machine Learning 1 JaeHyoung Lim
- Poster Thermodynamic investigation of oxidation behavior of NiAl intermetallics with embedded Cr and Mo 1 Golnar Geramifard

Session -M: Modelling, Simulation, and Data
Belongs to:
Topic X: Poster Session

Ammonia (NH3) as a fundamental chemical material is essential to industrial areas for fertilizer, hydrogen storage, and fuels. Generally, the production of 180 million tons of NH3 per year depends on the Haber-Bosch process. This energy-intensive process under high temperature (400~ oC) and high pressures (150~ bar) consumes nature gas with huge CO2 emissions. Electrochemical nitrogen reduction for the ammonia synthesis has emerged as a sustainable approach to overcoming the limits of the Haber-Bosch process. So, we want to find a new electrochemical NRR catalyst through high throughput screening using machine learning. High throughput screening of electrocatalysts for nitrogen reduction needs many calculations in the search space, making the computational cost for predicting eligible electrocatalyst. To overcome drawback, we used an artificial neural network (ANN). This neural network consists of Long short-term memory model (LSTM) and light gradient boosting machine model (LGBM), and it predicts for eligible electrocatalysts through supervised learning using peraday efficiency and yield. Various parameters used in deep learning models include simplified molecular input line entry system(smiles), loading mass, conductor, electrolytes and temperature, which are important factors in finding new NRR catalysts. We anticipate the study of high throughput screening using machine learning to help discover NRR catalysts and to be the starting point for expansion to other catalysts research.

JaeHyoung Lim
Chonnam National University
Additional Authors:
  • DoYeon Hwang
    Chonnam National University
  • Prof. Uk Sim
    Chonnam National University
  • Dr. Heechae Choi
    University of Cologne


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