Search Results (5)

Identifying efficient and physically meaningful descriptors is crucial for the rational design of hydrogen evolution reaction (HER) catalysts. In this study, we systematically investigate the HER activity of transition metal dichalcogenide (TMD) monolayers by combining density functional theory (DFT) calculations and machine learning techniques. By exploring the relationship between key electronic properties, including the conduction band minimum (CBM), p_z band center, and hydrogen adsorption free energy (ΔG_*H), we establish a strong linear correlation between the CBM and ΔG_*H, identifying the CBM as a reliable and physically meaningful descriptor for HER activity. Furthermore, this correlation is validated in vacancy-defected TMD systems, demonstrating that the CBM remains an effective descriptor even in the presence of structural defects. To enable the rapid and accurate prediction of the CBM, we develop an interpretable three-dimensional model using the Sure Independence Screening and Sparsifying Operator (SISSO) algorithm. The SISSO model achieves a high predictive accuracy, with correlation coefficients (r) and coefficients of determination (R²) reaching 0.98 and 0.97 in the training and 0.99 and 0.99 in the validation tests, respectively. This study provides an efficient computational framework that combines first-principles calculations and machine learning to accelerate the screening and design of high-performance TMD-based HER catalysts. Full article

The field of materials science has experienced a transformative shift with the emergence of high-entropy materials (HEMs), which possess a unique combination of properties that traditional single-phase materials lack. Among these, high-entropy nitrides (HENs) stand out for their exceptional mechanical strength, thermal stability, and resistance to extreme environments, making them highly sought after for applications in aerospace, defense, and energy sectors. Central to the design of these materials is their entropy forming ability (EFA), a measure of a material’s propensity to form a single-phase, disordered structure. This study introduces the application of the sure independence screening and sparsifying operator (SISSO), a machine learning technique, to predict the EFA of HEN ceramics. By utilizing a rich dataset curated from theoretical computational data, SISSO has been trained to identify the most critical features contributing to EFA. The model’s strong interpretability allows for the extraction of complex mathematical expressions, providing deep insights into the material’s composition and its impact on EFA. The predictive performance of the SISSO model is meticulously validated against theoretical benchmarks and compared with other machine learning methodologies, demonstrating its superior accuracy and reliability. This research not only contributes to the growing body of knowledge on HEMs but also paves the way for the efficient discovery and development of new HEN materials with tailored properties for advanced technological applications. Full article

The band gap is a key parameter in semiconductor materials that is essential for advancing optoelectronic device development. Accurately predicting band gaps of materials at low cost is a significant challenge in materials science. Although many machine learning (ML) models for band gap prediction already exist, they often suffer from low interpretability and lack theoretical support from a physical perspective. In this study, we address these challenges by using a combination of traditional ML algorithms and the ‘white-box’ sure independence screening and sparsifying operator (SISSO) approach. Specifically, we enhance the interpretability and accuracy of band gap predictions for binary semiconductors by integrating the importance rankings of support vector regression (SVR), random forests (RF), and gradient boosting decision trees (GBDT) with SISSO models. Our model uses only the intrinsic features of the constituent elements and their band gaps calculated using the Perdew–Burke–Ernzerhof method, significantly reducing computational demands. We have applied our model to predict the band gaps of 1208 theoretically stable binary compounds. Importantly, the model highlights the critical role of electronegativity in determining material band gaps. This insight not only enriches our understanding of the physical principles underlying band gap prediction but also underscores the potential of our approach in guiding the synthesis of new and valuable semiconductor materials. Full article

Industries have been a major culprit in increasing carbonaceous emissions and major contributors to global warming over the past decades. Factories in the urban periphery tend to warm cities more as compared with rural surroundings. Recently, nature-based solutions have been promoted to provide solutions related to climate adaptations and mitigation issues and challenges. Among these solutions, urban trees have proven to be an effective solution to remove air pollutants and mitigate air pollution specifically caused by carbon emissions. This work was designed to assess the role of tree species in mitigating air emissions of carbon around the vicinity of various industrial sites. For this purpose, three different industrial sites (weaving, brick kiln, and cosmetic) were selected to collect data. Selected industrial sites were divided into two areas, i.e., (a) area inside the industry and (b) area outside the industry. The samples were collected from 100 square meters inside the industries and 100 square meters outside the industries. Five different trees species comprised of four replications were selected for sampling. About twenty trees species from inside and outside of the industries were measured, making it 120 trees from all three selected industries for estimating aboveground and belowground biomass, showing their carbon estimation. The results showed that Moringa oleifera depicted overall higher total biomass from both inside (2.58, 0.56, and 4.57 Mg ha⁻¹) and outside sites from all three selected industries. In terms of total carbon stock and carbon sequestration inside the industry sites, Syzygium cumini had the most dominant values in the weaving industry (2.82 and 10.32 Mg ha⁻¹) and brick kiln (3.78 and 13.5 Mg ha⁻¹), while in the cosmetic industry sites, Eucalyptus camaldulensis depicted higher carbon, stock, and sequestration values (7.83 and 28.70 Mg ha⁻¹). In comparison, the sites outside the industries’ vicinity depicted overall lower carbon, stock, and sequestration values. The most dominant tree inside came out to be Dalbergia sisso (0.97 and 3.54 Mg ha⁻¹) in the weaving industry sites, having higher values of carbon stock and carbon sequestration. Moringa oliefra (1.26 and 4.63) depicted dominant values in brick kiln sites, while in the cosmetic industry, Vachellia nilotica (2.51 and 9.19 Mg ha⁻¹) displayed maximum values as compared with other species. The findings regarding belowground biomass and carbon storage indicate that the amount of soil carbon decreased with the increase in depth; higher soil carbon stock values were depicted at a 0–20 cm depth inside and outside the industries. The study concludes that forest tree species present inside and outside the vicinity of various industries have strong potential in mitigating air emissions. Full article

The current study was conducted to estimate the carbon (C) storage status of agroforestry systems, via a non-destructive strategy. A total of 75 plots (0.405 ha each) were selected by adopting a lottery method of random sampling for C stock estimations for soil, trees and crops in the Mandi-Bahauddin district, Punjab, Pakistan. Results revealed that the existing number of trees in selected farm plots varied from 25 to 30 trees/ha. Total mean tree carbon stock ranged from 9.97 to 133 Mg C ha⁻¹, between 5–10 km away from the brick kilns in the study area. The decreasing order in terms of carbon storage potential of trees was Eucalyptus camaldulensis > Syzygium cumin > Popolus ciliata > Acacia nilotica > Ziziphus manritiana > Citrus sinensis > Azadirachtta Indica > Delbergia sisso > Bambusa vulgaris > Melia azadarach > Morus alba. Average soil carbon pools ranged from 10.3–12.5 Mg C ha⁻¹ in the study area. Meanwhile, maximum C stock for wheat (2.08 × 10⁶ Mg C) and rice (1.97 × 10⁶ Mg C) was recorded in the cultivated area of Tehsil Mandi-Bahauddin. The entire ecosystem of the study area had an estimated woody vegetation carbon stock of 68.5 Mg C ha⁻¹ and a soil carbon stock of 10.7 Mg C ha⁻¹. These results highlight that climate-smart agriculture has great potential to lock up more carbon and help in the reduction of CO₂ emissions to the atmosphere, and can be further used in planning policies for executing tree planting agendas on cultivated lands and for planning future carbon sequestration ventures in Pakistan. Full article