Mridul Kumar

Mridul Kumar, Yevgeny Rakita

Phase-change materials (PCMs) such as Ge-Sb-Te alloys are widely used in non-volatile memory applications due to their rapid and reversible switching between amorphous and crystalline states. However, their functional properties are strongly governed by nanoscale variations in composition and structure, which are challenging to resolve using conventional techniques. Here, we apply unsupervised machine learning to 4-dimensional scanning transmission electron microscopy (4D-STEM) data to identify compositional and structural heterogeneity in Ge-Sb-Te. After preprocessing and dimensionality reduction with principal component analysis (PCA), cluster validation was performed with t-SNE and UMAP, followed by k-means clustering optimized through silhouette scoring. Four distinct clusters were identified which were mapped back to the diffraction data. Elemental intensity histograms revealed chemical signatures change across clusters, oxygen and germanium enrichment in Cluster 1, tellurium in Cluster 2, antimony in Cluster 3, and germanium again in Cluster 4. Furthermore, averaged diffraction patterns from these clusters confirmed structural variations. Together, these findings demonstrate that clustering analysis can provide a powerful framework for correlating local chemical and structural features in PCMs, offering deeper insights into their intrinsic heterogeneity.

Mridul Kumar, Deepali Jain, Zeeshan Saifi et al.

Soil macronutrients, particularly potassium ions (K$^+$), are indispensable for plant health, underpinning various physiological and biological processes, and facilitating the management of both biotic and abiotic stresses. Deficient macronutrient content results in stunted growth, delayed maturation, and increased vulnerability to environmental stressors, thereby accentuating the imperative for precise soil nutrient monitoring. Traditional techniques such as chemical assays, atomic absorption spectroscopy, inductively coupled plasma optical emission spectroscopy, and electrochemical methods, albeit advanced, are prohibitively expensive and time-intensive, thus unsuitable for real-time macronutrient assessment. In this study, we propose an innovative soil testing protocol utilizing a dataset derived from synthetic solutions to model soil behaviour. The dataset encompasses physical properties including conductivity and pH, with a concentration on three key macronutrients: nitrogen (N), phosphorus (P), and potassium (K). Four machine learning algorithms were applied to the dataset, with random forest regressors and neural networks being selected for the prediction of soil nutrient concentrations. Comparative analysis with laboratory soil testing results revealed prediction errors of 23.6% for phosphorus and 16% for potassium using the random forest model, and 26.3% for phosphorus and 21.8% for potassium using the neural network model. This methodology illustrates a cost-effective and efficacious strategy for real-time soil nutrient monitoring, offering substantial advancements over conventional techniques and enhancing the capability to sustain optimal nutrient levels conducive to robust crop growth.