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The recent growing attention towards non-aqueous Li–air batteries (LABs) stems from their high energy density, positioning them as a key solution to the surging demand for electrical energy driven by portable electronics. Despite the potential of LABs, sluggish cathode kinetics and large overpotentials, coupled with storage challenges from insoluble discharge products, impede their commercialization. Herein, we have proposed an interpretable machine learning (ML) approach to screen the synergistic effect of transition metal pairs in dual metal site catalysts (DMSCs) as a new frontier cathode for efficient LABs. The extreme gradient boosting regression-based ML algorithm systematically explored different transition metal combinations and predicted efficient cathode electrocatalysts for LABs. Our findings revealed highly active DMSCs for LAB reactions, which have the potential to exhibit promising ORR/OER activity, surpassing that of the novel platinum cathode. The outcome of the ML algorithm unveils the significant role of metal d-electrons in predicting the catalytic performance. Additionally, the determined Coulomb interaction energy for active DMSCs provides valuable insights, which hold promise for modeling efficient LABs and advancing their practical applications in the future. [ABSTRACT FROM AUTHOR]

Based on intramolecular band alignments between the organic and inorganic units, 2D hybrid perovskites can be of four types (Ia, Ib, IIa and IIb). Specific optoelectronic devices (photovoltaics, light emitting diodes, spintronics, etc.) demand specific charge carrier property that originates due to different types of band alignments. In this study, we have proposed a machine learning technique to classify 2D perovskites based on their band alignment types using molecular and elemental features. Our proposed model can successfully classify type I–II, type Ia–Ib and type IIa–IIb using binary classification and all four types using multiclass classification. We have also formulated an equation for determining the probability of the different band alignment types based on the contribution coefficients of the considered features. We believe such an interpretable glass-box model can open a new paradigm for the study of electronic properties of 2D perovskite materials. [ABSTRACT FROM AUTHOR]

Electrochemical window (ECW) potential is a key parameter for estimating voltage and understanding electrolyte stability. Determining the ECW is challenging both experimentally and computationally. Experimental ECW values vary with measurement conditions and the purity of ionic liquids (ILs), while the classical MD followed by DFT approach has been quite fruitful to achieve experimental values but limited by the lack of appropriate force fields. Herein, high throughput machine learning (ML) techniques are considered to predict the ECW of different organic cation based ILs. To train ML models, ECW values are calculated using the MD-DFT method. Among the seven considered algorithms, extra trees regression (ETR) emerges as the optimal model, exhibiting a low MAE of 0.37 V for the ECW prediction. Our ML predicted MAE of 0.37 V surpasses the previous DFT computed MAE of 0.68 V for two different methods of ECW calculation. The ML model successfully predicts the ECW of 660 IL electrolytes, and the interpretable SHAP library provides insight regarding local and global feature contributions. The predicted ECW values align well with experimental values, offering a promising alternative to expensive computational and tedious experimental approaches for electrolyte selection. [ABSTRACT FROM AUTHOR]

Dual‐ion batteries (DIBs) have been considered a viable alternative to increasingly costly and hazard‐prone lithium‐ion batteries (LIBs), which have reached a level of saturation. DIBs differ from LIBs in the way that the cations and anions originate from the electrolyte, thus signifying the active role played by electrolyte. In this Review, the major developments in research in the field of DIBs are summarized with a major emphasis on computational approaches in this direction. The various computational methods for understanding and designing electrodes are discussed. The advancements in electrode and electrolyte design for efficient DIBs are highlighted. Further, the ways to investigate solid–electrolyte interphase formation through simulations to comprehend the role of various components are discussed. Finally, directions are given on which future computational research can be carried out to design futuristic DIBs to provide useful guidelines to the researchers to understand and design DIBs. [ABSTRACT FROM AUTHOR]

Catalytic conversion of CO2 into value‐added chemicals is necessary to mitigate the increasing global warming and energy‐related issues. The selective production of C2 or C2+ products by photoreduction of CO2 is a challenging task due to the sluggish kinetics of C─C coupling. Herein, the potential of a silver nanoparticle (Ag NP) incorporated metal‐organic framework (MOF), NU‐1000 is introduced, to harvest light and ease the uphill electron transfer. The growth of silver nanoparticles on thiol‐functionalized NU‐1000 is studied using the atomic pair distribution function (PDF) analysis. The C2 product formed using Ag@NU‐1000‐SH without any sacrificial agents is acetic acid, which is confirmed by 1H‐NMR and HRMS data. The catalyst displays a high acetate output of 293.58 µmolg−1h−1 with a selectivity of 79.4%. The XPS and DFT calculations evidence that the presence of the charge‐polarised states in the as‐synthesized catalyst act as asymmetric centers that favor the C─C coupling reaction. The combined experimental (in situ DRIFT spectroscopic studies) and theoretical calculations reveal that the C─C coupling takes place via the coupling of COOH*intermediates. [ABSTRACT FROM AUTHOR]

A solid-state nanopore combined with the quantum transport method has garnered substantial attention and intrigue for DNA sequencing due to its potential for providing rapid and accurate sequencing results, which could have numerous applications in disease diagnosis and personalized medicine. However, the intricate and multifaceted nature of the experimental protocol poses a formidable challenge in attaining precise single nucleotide analysis. Here, we report a machine learning (ML) framework combined with the quantum transport method to accelerate high-throughput single nucleotide recognition with C 3 N nanopores. The optimized eXtreme Gradient Boosting Regression (XGBR) algorithm has predicted the fingerprint transmission of each unknown nucleotide and their rotation dynamics with root mean square error scores as low as 0.07. Interpretability of ML black box models with the game theory-based SHapley Additive exPlanation method has provided a quasi-explanation for the model working principle and the complex relationship between electrode-nucleotide coupling and transmission. Moreover, a comprehensive ML classification of nucleotides based on binary, ternary, and quaternary combinations shows maximum accuracy and F1 scores of 100%. The results suggest that ML in tandem with a nanopore device can potentially alleviate the experimental hurdles associated with quantum tunneling and facilitate fast and high-precision DNA sequencing.

Owing to the quantized size and associated discrete energy levels, atomically precise silver nanoclusters (Ag NCs) hold great potential for designing functional luminescent materials. However, the thermally activated non-radiative transition of Ag(I)-based NCs has faded the opportunities. To acquire the structurally rigid architecture of cluster nodes for constraining such transitions, a new synthetic approach is unveiled here that utilizes a neutral template as a cluster-directing agent to assemble twenty Ag(I) atoms that ensure the maximum number of surface-protecting ligand attachment possibilities in a particular solvent medium. The solvent polarity triggers the precise structural design to circumvent the over-reliance of the templates, which results in the formation of [CO 2 @Ag 20 (SAdm) 10 (CF 3 COO) 10 (DMA) 2 ] NC (where SAdm = 1-adamantanethiolate and DMA = N , N -dimethylacetamide) exhibiting an unprecedented room-temperature photoluminescence emission. The high quantum yield of the generated blue emission ensures its candidature as an ideal donor for artificial light-harvesting system design, and it is utilized with the two-step sequential energy transfer process, which finally results in the generation of ideal white light. For implementing perfect white light emission, the required chromophores in the green and red emission regions were chosen based on their effective spectral overlap with the donor components. Due to their favorable energy-level distribution, excited state energy transfers occurred from the NC to β-carotene at the initial step, then from the conjugate of the NC and β-carotene to another chromophore, Nile Blue, at the second step via a sequential Förster resonance energy transfer pathway.

The sunlight-driven fixation of CO 2 into valuable chemicals constitutes a promising approach toward environmental remediation and energy sustainability over traditional thermal-driven fixation. Consequently, in this article, we report a strategic design and utilization of Mg-centered porphyrin-based metal-organic framework (MOFs) having relevance to chlorophyll in green plants as a visible light-promoted highly recyclable catalyst for the effective fixation of CO 2 into value-added cyclic carbonates under ambient conditions. Indeed, the Mg-centered porphyrin MOF showed good CO 2 capture ability with a high heat of adsorption (44.5 kJ/mol) and superior catalytic activity under visible light irradiation in comparison to thermal-driven conditions. The excellent light-promoted catalytic activity of Mg-porphyrin MOF has been attributed to facile ligand-to-metal charge transfer transition from the photoexcited Mg-porphyrin unit (SBU) to the Zr 6 cluster which in turn activates CO 2 , thereby lowering the activation barrier for its cycloaddition with epoxides. The in-depth theoretical studies further unveiled the detailed mechanistic path of the light-promoted conversion of CO 2 into high-value cyclic carbonates. This study represents a rare demonstration of sunlight-promoted sustainable fixation of CO 2 , a greenhouse gas into value-added chemicals.