Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts

Wu, Lianping; Guo, Tieding; Li, Teng

doi:10.1016/j.isci.2021.102398

Cited by 56 publications

(48 citation statements)

References 46 publications

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“…A Tafel slope value of 31 mV dec –1 for stretch-annealed catalysts thus suggests n b = 3 and ν = 2, which translated into the fact that the second (deprotonation of OH ad to form O ad ) and fourth (deprotonation of OOH ad to form OO ad ) electron transfer reactions can be the sluggish (energetically unfavorable) process ( ν = 2), and the fourth reaction acts as a major rate-determining step ( n b = 3). The proposed rate-determining step agrees with the proposed OER energetics on Cu, 73 , 74 further validating our Tafel analysis. The Tafel slope value varies between 31 and 84 depending on the pre-treatment of the PVdF-HFP substrate, probably due to the change in the OER energetics and/or the existence of the mixed rate-determining step.…”

Section: Results and Discussionsupporting

confidence: 85%

Tuning the Polarity of a Fibrous Poly(vinylidene fluoride-co-hexafluoropropylene)-Based Support for Efficient Water Electrolysis

et al. 2022

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Water electrolysis under alkaline conditions is of interest due to the applicability of non-precious metal-based materials for electrocatalysts. However, the successful design and synthesis of earth-abundant and efficient catalysts for the oxygen evolution reaction (OER) remain a significant challenge. This work presents cost-effective and straightforward ways to improve the OER activity under alkaline conditions by activating the catalyst–support and reactant–support interaction. Micro/nano-sized fibrous poly(vinylidene fluoride- co -hexafluoropropylene) (PVdF-HFP) was synthesized via simple and scalable electrospinning and subsequently coated with Cu by electroless deposition to obtain the electrocatalyst with a large specific surface area, enhanced mass transport, and high catalyst utilization. Scanning electron microscopy, infrared spectroscopy, and X-ray diffraction confirmed the successful synthesis of the series of Cu/PVdF-HFP fibrous catalysts with varied ferroelectric polarizability of the PVdF-HFP support in the order of stretch-anneal > anneal > stretch > without pre-treatment of the catalyst. The best OER activity was confirmed for the Cu/PVdF-HFP catalyst with stretch and annealed treatment among the catalysts tested, suggesting that both the reaction kinetics and energetics of stretch-annealed Cu/PVdF-HFP catalysts were optimal for the OER. The electron delocalization between Cu and PVdF-HFP substrates (electron transfer from Cu to the negatively charged (δ – eff ) PVdF-HFP region at the Cu|PVdF-HFP interface) and the enhanced transport of reactive hydroxide species and/or the increase in the local pH by positively charged (δ + eff ) PVdF-HFP region concertedly accelerate the OER activity. The overall activity for the prototype water electrolyzer increased 10-fold with stretch-anneal treatment compared to the one without pre-treatment, highlighting the effect of tuning the catalyst–support and reactant–support interaction on improving the efficiency of the water electrolysis.

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Section: Results and Discussionsupporting

confidence: 85%

Tuning the Polarity of a Fibrous Poly(vinylidene fluoride-co-hexafluoropropylene)-Based Support for Efficient Water Electrolysis

et al. 2022

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“…The ML method is commonly used in accelerating the prediction of the overpotential of oxygen evolution reactions. 27 , 28 DFT calculations showed that Fe–N 3 O-1, Fe–N 4 , Co–N 2 O 2 , Ni–N 2 O 2 , Ni–N 3 O-1, Ni–N 2 O 2 -1, and Fe–N 2 O 2 possessed higher OER performances than other units, and ML analysis indicated that the electrical properties of the active center were the most critical factor for catalytic OER performance. Subsequently, we synthesized one of the best performed Ni–N 2 O 2 coordination unit COFs.…”

Section: Introductionmentioning

confidence: 98%

“…The ML method is commonly used in accelerating the prediction of the overpotential of oxygen evolution reactions. 27,28 DFT calculations showed that Fe−…”

Section: ■ Introductionmentioning

confidence: 99%

In Silico Design of Covalent Organic Framework-Based Electrocatalysts

Zhou

Yang

Wang

et al. 2021

JACS Au

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Covalent organic frameworks (COFs) are an emerging type of porous crystalline material for efficient catalysis of the oxygen evolution reaction (OER). However, it remains a grand challenge to address the best candidates from thousands of possible COFs. Here, we report a methodology for the design of the best candidate screened from 100 virtual M–N x O y (M = 3d transition metal)-based model catalysts via density functional theory (DFT) and machine learning (ML). The intrinsic descriptors of OER activity of M–N x O y were addressed by the machine learning and used for predicting the best structure with OER performances. One of the predicted structures with a Ni–N 2 O 2 unit is subsequently employed to synthesize the corresponding Ni–COF. X-ray absorption spectra characterizations, including XANES and EXAFS, validate the successful synthesis of the Ni–N 2 O 2 coordination environment. The studies of electrocatalytic activities confirm that Ni–COF is comparable with the best reported COF-based OER catalysts. The current density reaches 10 mA cm –2 at a low overpotential of 335 mV. Furthermore, Ni–COF is stable for over 65 h during electrochemical testing. This work provides an accelerating strategy for the design of new porous crystalline-material-based electrocatalysts.

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“…[25] Topological information-based ML algorithms not only eliminate the need for data preprocessing (e.g., sorting and formatting) but also allow for a precise description of complex data features (e.g., symmetry and topological structures). [26] These unique advantages have led to surging interests in applying this ML algorithm in various domains, such as social networks, [27] drug discovery, [28] materials science, [29,30] and structural biochemistry [31] (e.g., AlphaFold [32] ). It is expected that such ML algorithms can be applied to the rational design of a wide range of atomic catalysts.…”

Section: Introductionmentioning

confidence: 99%

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

Guo

2022

Adv Funct Materials

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Surging interests exist in double‐atom catalysts (DACs), which not only inherit the advantages of single‐atom catalysts (SACs) (e.g., ultimate atomic utilization, high activity, and selectivity) but also overcome the drawbacks of SACs (e.g., low loading and isolated active site). The design of DACs, however, remains cost‐prohibitive for both experimental and computational studies, due to their huge design space. Herein, by means of density functional theory (DFT) and topological information‐based machine‐learning (ML) algorithms, we present a data‐driven high‐throughput design principle to evaluate the stability and activity of 16 767 DACs for oxygen evolution (OER) and oxygen reduction (ORR) reactions. The rational design reveals 511 types of DACs with OER activity superior to IrO2 (110), 855 types of DACs with ORR activity superior to Pt (111), and 248 bifunctional DACs with high catalytic performance for both OER and ORR. An intrinsic descriptor is revealed to correlate the catalytic activity of a DAC with the electronic structures of the DAC and its bonding carbon surface structure. This data‐driven high‐throughput approach not only yields remarkable prediction precision (>0.926 R‐squared) but also enables a notable 144 000‐fold reduction of screening time compared with pure DFT calculations, holding promise to drastically accelerate the design of high‐performance DACs.

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Machine learning-accelerated prediction of overpotential of oxygen evolution reaction of single-atom catalysts

Cited by 56 publications

References 46 publications

Tuning the Polarity of a Fibrous Poly(vinylidene fluoride-co-hexafluoropropylene)-Based Support for Efficient Water Electrolysis

Tuning the Polarity of a Fibrous Poly(vinylidene fluoride-co-hexafluoropropylene)-Based Support for Efficient Water Electrolysis

In Silico Design of Covalent Organic Framework-Based Electrocatalysts

Data‐Driven High‐Throughput Rational Design of Double‐Atom Catalysts for Oxygen Evolution and Reduction

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