A descriptor for the design of 2D MXene hydrogen evolution reaction electrocatalysts

Abstract

MXene-STM design flow: A physical descriptor ϕ is built to uncover the hydrogen evolution reaction (HER) trends in Ti2CO2-STM (single transition metal doping) catalysis.

Cu5FeS4 quantum dots as a single-component photo-assisted electrocatalyst for efficient hydrogen evolution

“…As a clean renewable energy, hydrogen (H 2 ) has been considered as the key to solve the energy crisis and various environmental problems. [1][2][3][4][5] Along this line, photocatalysis and electrocatalysis from renewable energy have emerged as clean alternatives for H 2 production beyond the traditional fossil fuel-based methods. [6][7][8][9] However, the low conversion efficiency of photocatalysis and the high cost of electrocatalysis greatly limit their large-scale applications.…”

Advancing MXene Electrocatalysts for Energy Conversion Reactions: Surface, Stoichiometry, and Stability

“…Such exhaustive configurational and compositional space renders MXenes as ideal candidates for high throughput computing and machine learning, especially when high entropy, solid solutions, non‐stoichiometric MXenes and surface terminations are also taken into consideration (see schematic in Figure 6). [105] Moreover, there has been a plethora of recent DFT‐based studies with different descriptors used for the discovery of promising MXene electrocatalysts [106–108] . This large combinatorial space is particularly relevant for designing MXenes as electrocatalysts since an array of structures and band positioning could ensue tailorable physical and chemical properties to drive favourably a range of energy conversion reactions.…”

“…[105] Moreover, there has been a plethora of recent DFT-based studies with different descriptors used for the discovery of promising MXene electrocatalysts. [106][107][108] This large combinatorial space is particularly relevant for designing MXenes as electrocatalysts since an array of structures and band positioning could ensue tailorable physical and chemical properties to drive favourably a range of energy conversion reactions.…”

AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)

“…Intuitively and inevitably, successful and effective implementation of ML as predictive tools in electrocatalyst discovery entails a careful selection of input features (e.g., geometric, electronic, and energy properties) based on the established databases from experiments and in silico data from DFT calculations. Recently, the search for alternative electrocatalysts to Pt has predominantly focused on 2D materials such as graphdiyne (GDY), metal–nitrogen-carbon (M–N-C), MXene, and MoS 2 . − Sun et al have utilized ML as a complementary tool to DFT calculations in investigating GDY-based catalytic HER process. Specifically, the bagged-trees approach was adopted with six input features (i.e., mass number, active sites, d/f electrons, electronegativity, electron affinity, and ionization potential) to predict the HER performance, which yielded nearly identical results to DFT calculations (Figure c).…”