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This talk presents a comprehensive approach to computational materials science, integrating machine learning with first-principles methods to address the challenges of predicting material properties and dynamic processes. We introduce...

Neutron scattering techniques offer uniquely powerful, non-perturbative, and isotopically-sensitive means to investigate biological and soft-matter interfaces. In this presentation, Dr. Jarosław Majewski will first...

Neutron scattering techniques offer uniquely powerful, non-perturbative, and isotopically-sensitive means to investigate biological and soft-matter interfaces. In this presentation, Dr. Jarosław Majewski will first...

Achieving self-reliance in defence and strategic technologies is important for any nation, but there are major bottlenecks in materials & design, reverse engineering, stringent design and materials constraints. We have established unique facilities under the graphene centre to translate the reinforced metal/alloys composites into components. We are adding new dimensions by tuning material properties to the required or desired range using additives such as graphene/2D materials/ODS. These facilities are cutting-edge, industrial-scale metal additive manufacturing technologies. Using them, we will be...

The demand for resources and materials is rapidly increasing due to population growth, technological advancements, the need for an urgent response to climate change and rising energy demands. In this context...

The exponential growth of photovoltaic (PV) installations is an important and desirable element in the global response to climate change. PV technologies have seen swift and significant changes in the recent few years. From first generation solar cells featuring BSF to the current high efficiency heterojunction – interdigitated back contact solar cells, the PV technology has moved constantly towards higher efficiencies at lower marginal costs.

The development of high-performance accident-tolerant SiC composite cladding is critical for advancing Generation IV nuclear reactor technology. However, theoretical frameworks for the structural design of fiber-braided SiCf/SiC cladding remain underdeveloped, and issues related to gas-tightness—primarily caused by high porosity—have limited the further application of SiCf/SiC cladding. In this study...

High-entropy oxides (HEOs) have emerged as a novel class of functional materials, composed of more than four different metallic elements, typically in near-equal atomic ratios, which leads to unique combinations of properties arising from high configurational entropy. Spinel-structured HEOs in the form of Me_x(Cr, Fe, Mn, Ni)_3-xO₄ (Me = Co, Al, and 0≤x≤1) were synthesized using the Pechini method. Comprehensive characterization techniques, including X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray absorption spectroscopy (XAS/XMCD), Mössbauer spectroscopy, and vibrating sample magnetometry (VSM), were used to determine the structural and magnetic properties of the synthesized materials.

Luminescent nanoparticles based on wide bandgap metal oxides offer an alternative to lanthanide phosphors, as they assure the material demand for white light emitting diodes (WLEDs) and down-shifters for solar cells, mainly thanks to their high chemical and thermal stability, low toxicity and low cost of fabrication. We recently reported a simple route to synthesize highly efficient organic-inorganic hybrid material combining ZnO nanoparticles (NPs) and PAAH (polyacrylic acid) to be used as white down-converting phosphor [1]. This material turns out to be highly efficient is in terms of high photoluminescence quantum yield (PLQY = 70 %) and gives white light emission.

Machine learning has been applied in the natural sciences for many years, including in bioinformatics and molecular modeling. However, it is only within the last decade that the rapid development of deep learning, along with the increased availability of data and computational power, has enabled a qualitative leap in the application of these methods. In my presentation, I will showcase selected examples of machine learning techniques developed and applied in our laboratory, illustrating their potential in protein analysis and modeling.