The 2D-MADE research group investigates two-dimensional (2D) materials and devices for future nanoelectronics, quantum technologies, cryogenic electronics, and energy-efficient computing. We combine device fabrication, advanced characterisation, physics-based modelling, and machine-learning approaches to understand how materials, interfaces, geometry, and processing conditions determine device performance.
Our research bridges theoretical prediction and experimental realisation. By linking measured device behaviour with multiscale simulations, we develop predictive design rules for high-performance, reliable, and scalable electronic technologies based on 2D materials.
We develop and study electronic devices based on 2D materials, including transition metal dichalcogenides heterostructures, and related layered materials. Our work includes field-effect transistors, memory devices, and other nanoscale device structures. We investigate material quality, contacts, interfaces, dielectric integration, and fabrication processes to improve device performance, reliability, and scalability for logic and memory applications.
We explore van der Waals heterostructures formed by stacking atomically thin materials with controlled rotational alignment. Small twist angles can strongly modify electronic, optical, magnetic, and transport properties, creating opportunities for new device functionalities. Our interest in twistronics includes understanding how moiré superlattices, interlayer coupling, strain, and electrostatic control can be used to engineer novel phases of matter and tunable electronic devices.
We investigate 2D materials with topological electronic properties, including two-dimensional topological insulators. These systems can host protected edge states and unusual transport behaviour that may be robust against certain forms of disorder. Understanding and engineering topological phases in 2D materials could enable new approaches to low-power electronics, spin-based devices, and quantum technologies. We study how material composition, interfaces, strain, electrostatic gating, and heterostructure design influence topological behaviour.
2D materials offer promising platforms for quantum technologies because their electronic and optical properties can be engineered through electrostatic gating, stacking, strain, and proximity effects. We explore device concepts relevant to quantum applications, including quantum-confined structures, spin and valley degrees of freedom, superconducting and magnetic proximity effects, and hybrid heterostructures. Our goal is to understand how 2D materials can support controllable quantum states, quantum sensing, and future quantum-information hardware. We also investigate Josephson junctions based on 2D materials and van der Waals heterostructures, including devices that combine superconductors transition metal dichalcogenides, or other quantum materials. These structures provide a platform to study proximity-induced superconductivity, phase-coherent transport, tunable superconducting weak links, and device concepts relevant to superconducting quantum circuits and hybrid quantum technologies.
We investigate 2D-material devices for operation at cryogenic temperatures. Cryogenic electronics is increasingly important for quantum computing, space technologies, low-noise sensing, and advanced scientific instrumentation. Our work examines how transport, switching behaviour, contacts, interfaces, and device variability change at low temperatures. We are particularly interested in identifying 2D-material device concepts that can operate efficiently and reliably in cryogenic environments, including control and readout electronics compatible with quantum systems.
A central theme of our work is the close integration of experiments and modelling. We use electrical and materials characterisation to understand device behaviour, identify performance-limiting mechanisms, and calibrate simulation models. These validated models help us interpret experimental results, predict device performance under different conditions, and guide the design of improved materials, processes, and device architectures.
We use first-principles calculations, atomistic simulations, and technology computer-aided design (TCAD) to study 2D materials and devices across multiple length scales. This multiscale approach connects fundamental material properties with device-level behaviour. It supports the design and optimisation of nanoscale logic, memory, cryogenic, and quantum-device technologies while accounting for defects, interfaces, variability, and non-ideal material properties.
We apply machine-learning methods to accelerate the simulation and optimisation of 2D-material devices. These approaches help reduce the computational cost of complex calculations and enable faster exploration of materials, device geometries, and operating conditions. By combining machine learning with physics-based models and experimental data, we aim to accelerate discovery and improve the predictive capability of device simulations.
Our research follows an integrated pathway:
Materials and interfaces → heterostructure design → device fabrication → characterisation → calibrated modelling → predictive device design
This approach allows us to translate fundamental understanding of 2D materials, twistronics, topological phases, Josephson junctions, and quantum phenomena into practical device concepts for future nanoelectronics, cryogenic electronics, and quantum technologies.