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Related Concept Videos

Inorganic Nitrogen Assimilation01:22

Inorganic Nitrogen Assimilation

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Nitrogen is an essential element in biological systems, forming a crucial component of proteins, nucleic acids, and other cellular constituents. Many bacteria and archaea acquire nitrogen in the form of nitrate (NO₃⁻) or ammonia (NH₃), which are then assimilated into biomolecules through specific enzymatic pathways.Assimilatory Nitrate ReductionWhen nitrate enters the cell, it undergoes a two-step reduction process known as assimilatory nitrate reduction. Initially, the enzyme...
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Machine Learning Design of Single-Atom Catalysts for Nitrogen Fixation.

Shuyue Wang1,2, Chao Qian1,2, Shaodong Zhou1,2

  • 1College of Chemical and Biological Engineering, Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, Zhejiang University, Hangzhou 310027, P. R. China.

ACS Applied Materials & Interfaces
|August 17, 2023
PubMed
Summary

Machine learning and first-principles calculations accelerate the design of single-atom catalysts for nitrogen reduction reaction (NRR). This powerful strategy accurately predicts catalyst performance, aiding in the development of efficient vanadium-group catalysts.

Keywords:
DFT calculationsmachine learningnitrogen reductionsingle-atom catalyststheoretical design

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Catalysis

Background:

  • Transition-metal single-atom catalysts are crucial for various chemical reactions.
  • Efficient design of these catalysts requires accurate prediction of their performance.
  • Nitrogen reduction reaction (NRR) is a key process for ammonia synthesis and nitrogen fixation.

Purpose of the Study:

  • To combine first-principles calculations with machine learning for designing novel single-atom catalysts.
  • To screen and identify promising single-atom catalysts for the nitrogen reduction reaction (NRR).
  • To develop an accurate predictive model for hydrogenation barriers in NRR.

Main Methods:

  • Utilized first-principles calculations to investigate catalyst properties.
  • Employed machine learning, specifically gradient boosting regression, for performance prediction.
  • Screened a large number of single-atom catalyst structures using high-throughput computation.

Main Results:

  • Identified V/Nb/Ta-N single-atom catalysts as promising candidates based on stability, activity, and selectivity.
  • Achieved accurate prediction of hydrogenation barriers for NRR with a root-mean-squared error of 0.07 eV.
  • Successfully predicted the NRR performance for over 1000 single-atom catalyst structures.

Conclusions:

  • The integration of high-throughput computation and machine learning is a powerful strategy for accelerating catalyst design.
  • This approach enables rapid and accurate prediction of NRR performance.
  • The study provides valuable structure-performance correlations for vanadium-group catalysts, guiding future catalyst development.