Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts

Zafari, Mohammad; Kumar, Deepak; Umer, Muhammad; Kim, Kwang S.

doi:10.1039/c9ta12608b

Cited by 164 publications

(139 citation statements)

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“…To this end, highthroughput computer screening is a powerful approach. [39][40][41][42][43][44][45][46] (TM)SA catalysts embedded in M-N 4 /C 4 and M-N 3 /C 3 moieties of G N /Gr have been reported to be promising as HER/OER/ORR electrocatalysts. 31,34 The localized d orbital energy levels in (TM)SAs are modified by the coordination environments.…”

Section: Introductionmentioning

confidence: 99%

Tuning metal single atoms embedded in N_xC_y moieties toward high-performance electrocatalysis

Kim

Umer

et al. 2021

Energy Environ. Sci.

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Section: Introductionmentioning

confidence: 99%

Tuning metal single atoms embedded in N_xC_y moieties toward high-performance electrocatalysis

Kim

Umer

et al. 2021

Energy Environ. Sci.

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219

190

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“…Adapted and reprinted with permission from Refs. . Copyright Wiley–VCH, Royal Society of Chemistry, and Elsevier.…”

Section: Chasing Electrochemical Nitrogen Reduction Secretsmentioning

confidence: 96%

“…As an alternative, Zafari et al. have recently proposed a deep neural network to predict efficient E‐NRR systems among B‐doped graphene single‐atom catalysts (see Figure B) . The coordination number of the transition metal and the number of hydrogen atoms were the principal factors influencing the determining step, that is, the hydrogenation of N 2 to N 2 H. CrB 3 C 1 was the most promising system, with a minimal overpotential of 0.13 V. This machine learning method was also able to predict free energies with an accuracy of ±0.11 eV.…”

Section: Chasing Electrochemical Nitrogen Reduction Secretsmentioning

confidence: 99%

Across the Board: Federico Bella on Electrochemical Nitrogen Reduction

Bella

2020

ChemSusChem

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In this series of articles, the board members of ChemSusChem discussrecent research articles that they consider of exceptional quality and importance for sustainability.T his entry features Prof. F. Bella, who discusses the electrochemical reduction of nitrogen to produce ammonia through the addition of protons and electrons under mild conditions (25 8C, 1atm). This reaction has the potential to replace the energy-intensive traditional Haber-Bosch process but faces severalc hallenges and pitfalls.To day,t he fortune of 8billion people living in this world is closely dependento ns ome essential factors, among whichf ertilizers surely play ak ey role. The scientific communityi nt he field of chemical sciences fully shares the idea that the Haber-Bosch process for the synthesis of ammonia at al arge scale allowed the demographic boom of the twentieth century,o wing to the possibility of fertilizing the land and feeding people. In parallel, the use of ammonia as an intermediate for the industrial chemistry,a saproduct for domestic use, and as ac omponent for variousp rocesses behind the production of plastics furtherh ighlighted the strategic role of Haber-Bosch plants at ag lobal scale. [1] We should take into account that 170 million tons of ammonia are produced per year and that, even if ah undred years of development have led to an overall decrease in energy consumption and production cost, the Haber-Bosch process is responsible for approximately 2% of the current global energy consumption. Considering the high temperature and pressure conditions of this process, along with the requirement of clean hydrogen and nitrogen reactantg ases, it should also be kept in mind that ammonia industrial synthesis is responsible for 1.44 %o fg lobal CO 2 emissions (2.86 tons of CO 2 per ton of NH 3 in average). As at hird critical point, Haber-Bosch plants requireb illions of euros of capital to be built through an engineering project that takes several years to complete. [2] The scientific community hasr ecently come back to an old "Holy Grail" concept:t rying to perform ammonia synthesis under mild conditions and without the huge constrains of the Haber-Bosch large-scale (geo-localized) plants. Thisc hallenge is extremelyd ifficult because the aim is to completely eliminate carbonemissions and deliver both hydrogen and required powerf rom renewable sources. [3] This hot topic in the scientific literature( 2018-today) has been approached throughd ifferent researchs trategies:b iochemical, photocatalytic, chemical looping, plasma-chemical,and electrochemical.Electrochemistry is today al eadingd iscipline within applied chemicals ciences, and the strategies developed in the last ten years in the fields of CO 2 conversion,H 2 technologies, and solar energy conversion/storage devices seem usefult op ave the way to resolve the electrochemical nitrogen reduction reaction (E-NRR) challenge. The advantage of the electrochemical route lies in the thermodynamic force,w hich allows af lexible control of electrochemical potentials,t hus r...

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“…It is, therefore, quite convenient to construct a large dataset containing the energetic information of a class of functional materials by DFT calculations, where ML models can then be adopted to construct a scaling relationship between the structural and energetic properties of the materials. This scheme is widely applied in designing electrochemical catalysts [43] for the hydrogen evolution reaction, [44] oxygen evolution/reduction reaction, [45] nitrogen reduction reaction, [46] and carbon dioxide reduction reaction [47] . As a result, ML is effective in promoting the development of metal–air batteries.…”

Section: Microscale and Mesoscale Simulationsmentioning

confidence: 99%

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

Chen

Shen

et al. 2021

Angewandte Chemie

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Emerging machine learning (ML) methods are widely applied in chemistry and materials science studies and have led to a focus on data‐driven research. This Minireview summarizes the application of ML to rechargeable batteries, from the microscale to the macroscale. Specifically, ML offers a strategy to explore new functionals for density functional theory calculations and new potentials for molecular dynamics simulations, which are expected to significantly enhance the challenging descriptions of interfaces and amorphous structures. ML also possesses a great potential to mine and unveil valuable information from both experimental and theoretical datasets. A quantitative “structure–function” correlation can thus be established, which can be used to predict the ionic conductivity of solids as well as the battery lifespan. ML also exhibits great advantages in strategy optimization, such as fast‐charge procedures. The future combination of multiscale simulations, experiments, and ML is also discussed and the role of humans in data‐driven research is highlighted.

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Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts

Abstract: Machine learning (ML) methods would significantly reduce the computational burden of catalysts screening for nitrogen reduction reaction (NRR).

Cited by 164 publications

References 50 publications

Tuning metal single atoms embedded in N_xC_y moieties toward high-performance electrocatalysis

Tuning metal single atoms embedded in N_xC_y moieties toward high-performance electrocatalysis

Across the Board: Federico Bella on Electrochemical Nitrogen Reduction

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

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Machine learning-based high throughput screening for nitrogen fixation on boron-doped single atom catalysts

Abstract: Machine learning (ML) methods would significantly reduce the computational burden of catalysts screening for nitrogen reduction reaction (NRR).

Cited by 164 publications

References 50 publications

Tuning metal single atoms embedded in NxCy moieties toward high-performance electrocatalysis

Tuning metal single atoms embedded in NxCy moieties toward high-performance electrocatalysis

Across the Board: Federico Bella on Electrochemical Nitrogen Reduction

Applying Machine Learning to Rechargeable Batteries: From the Microscale to the Macroscale

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Tuning metal single atoms embedded in N_xC_y moieties toward high-performance electrocatalysis

Tuning metal single atoms embedded in N_xC_y moieties toward high-performance electrocatalysis