Md Shafayat Hossain

Md Mazharul Islam, Shamiul Alam, Md Shafayat Hossain et al.

The revolution in artificial intelligence (AI) brings up an enormous storage and data processing requirement. Large power consumption and hardware overhead have become the main challenges for building next-generation AI hardware. To mitigate this, Neuromorphic computing has drawn immense attention due to its excellent capability for data processing with very low power consumption. While relentless research has been underway for years to minimize the power consumption in neuromorphic hardware, we are still a long way off from reaching the energy efficiency of the human brain. Furthermore, design complexity and process variation hinder the large-scale implementation of current neuromorphic platforms. Recently, the concept of implementing neuromorphic computing systems in cryogenic temperature has garnered intense interest thanks to their excellent speed and power metric. Several cryogenic devices can be engineered to work as neuromorphic primitives with ultra-low demand for power. Here we comprehensively review the cryogenic neuromorphic hardware. We classify the existing cryogenic neuromorphic hardware into several hierarchical categories and sketch a comparative analysis based on key performance metrics. Our analysis concisely describes the operation of the associated circuit topology and outlines the advantages and challenges encountered by the state-of-the-art technology platforms. Finally, we provide insights to circumvent these challenges for the future progression of research.

Ashiqur Rasul, Md Shafayat Hossain, Ankan Ghosh Dastider et al.

Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new materials from ab initio calculations. In this work, an effective and robust deep learning-based model is proposed by incorporating persistent homology and graph neural network which offers an accuracy of 91.4% and an F1 score of 88.5% in classifying topological vs. non-topological materials, outperforming the other state-of-the-art classifier models. The incorporation of the graph neural network encodes the underlying relation between the atoms into the model based on their own crystalline structures and thus proved to be an effective method to represent and process non-euclidean data like molecules with a relatively shallow network. The persistent homology pipeline in the suggested neural network is capable of integrating the atom-specific topological information into the deep learning model, increasing robustness, and gain in performance. It is believed that the presented work will be an efficacious tool for predicting the topological class and therefore enable the high-throughput search for novel materials in this field.

Hossain Hridoy, Tahiya Chowdhury, Md Shafayat Hossain

The most technologically consequential materials are often the rarest: they occupy narrow regions of chemical space, obey competing physical constraints, and appear only sparsely in existing databases. High-kappa dielectrics, high-Tc superconductors, and ferromagnetic insulators are to name a few. This scarcity fundamentally limits today's data-driven materials discovery, where machine-learning models excel at interpolation but struggle to generate genuinely new candidates. Here, we introduce DielecMIND, an artificial intelligence framework that reframes materials discovery as a reasoning-driven exploration instead of a database-screening problem. Using high-kappa dielectrics as a data-scarce and technologically stringent test case, DielecMIND combines large-language-model hypothesis generation for the first time with physics validated first-principles calculation to navigate chemical space beyond known compounds. Prior to our work, only 14 experimentally or computationally validated materials with kappa > 150 were known. Our framework discovers and validates 5 new such compounds, expanding this rare-materials class by a remarkable = 35% in a single study. Among them, we find that Ba2TiHfO6 exhibits a dielectric constant of 637, minimal loss at low optical frequencies, and stability up to 800 K. Beyond dielectrics, this work demonstrates a new paradigm for artificial-intelligence-guided discovery: one that generates a small number of physically grounded, experimentally plausible candidates yet measurably expands sparsely populated functional materials spaces. Thus, DielecMIND points toward a general strategy for discovering rare, high-impact functional materials where data scarcity has long constrained progress.

Qiulin Zeng, Tahiya Chowdhury, Md Shafayat Hossain

Discovering materials that must simultaneously satisfy multiple competing constraints remains a central challenge in computational materials design, particularly in data-scarce regimes where conventional data-driven approaches are least effective. Magnetic insulators represent a stringent example: the electronic conditions that favor magnetic order often also promote metallicity, while insulating behavior suppresses the interactions that stabilize magnetism. As a result, experimentally viable magnetic insulators are rare and difficult to identify through conventional screening. Here, we introduce MagMatLLM, a constraint-guided generative discovery framework that integrates language-model-based crystal generation with evolutionary selection, surrogate screening, and first-principles validation to target simultaneous stability, magnetism, and insulating behavior. Unlike stability-first approaches, the framework enforces functional constraints during generation and selection, steering the search toward sparsely populated regions of materials space defined by competing physical requirements. Using this workflow, we identify twelve previously unreported candidate magnetic insulators, including Tm$_4$Co$_2$Cr$_2$O$_{12}$ and Cr$_4$Nb$_2$O$_{12}$. Of these, ten are dynamically stable by phonon analysis and exhibit finite band gaps and nonzero magnetic moments in spin-polarized density functional theory calculations. Beyond the specific compounds identified here, this work establishes a general constraint-guided paradigm for multi-objective materials discovery in sparse chemical spaces and provides a transferable strategy for the design of quantum materials under competing physical constraints.

Shamiul Alam, Md Shafayat Hossain, Ahmedullah Aziz

Controlling the spread of infectious diseases, such as the ongoing SARS-CoV-2 pandemic, is one of the most challenging problems for human civilization. The world is more populous and connected than ever before, and therefore, the rate of contagion for such diseases often becomes stupendous. The development and distribution of testing kits cannot keep up with the demand, making it impossible to test everyone. The next best option is to identify and isolate the people who come in close contact with an infected person. However, this apparently simple process, commonly known as - contact tracing, suffers from two major pitfalls: the requirement of a large amount of manpower to track the infected individuals manually and the breach in privacy and security while automating the process. Here, we propose a Bluetooth based contact tracing hardware with anonymous IDs to solve both the drawbacks of the existing approaches. The hardware will be a wearable device that every user can carry conveniently. This device will measure the distance between two users and exchange the IDs anonymously in the case of a close encounter. The anonymous IDs stored in the device of any newly infected individual will be used to trace the risky contacts and the status of the IDs will be updated consequently by authorized personnel. To demonstrate the concept, we simulate the working procedure and highlight the effectiveness of our technique to curb the spread of any contagious disease.