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Hysteresis is more than just an interesting oddity, which occurs in materials with a first-order transition. It is a real obstacle on the path from existing lab-scale prototypes of magnetic refrigerators towards commercialization of this…
Active learning (AL) is a powerful sequential optimization approach that has shown great promise in the discovery of new materials. However, a major challenge remains the acquisition of the initial data and the development of workflows to…
Rare-earth (RE) based frustrated magnets have attracted great attention as excellent candidates for magnetic refrigeration at sub-Kelvin temperatures, while the experimental identification on systems exhibiting both large volumetric cooling…
Machine Learning (ML) techniques are revolutionizing the way to perform efficient materials modeling. Nevertheless, not all the ML approaches allow for the understanding of microscopic mechanisms at play in different phenomena. To address…
Solid state-refrigerants have generated worldwide interest owing to their growing potential for use in efficient and green cooling devices. Caloric effects could be obtained by manipulating their degrees of freedom such as magnetization,…
Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive…
Accurate thermal analysis of composites and porous media requires detailed characterization of local thermal properties in small scale. For some important applications such as lithium-ion batteries, changes in the properties during the…
The magnetocaloric effect in exchange-coupled synthetic-antiferromagnet multilayers is investigated experimentally and theoretically. We observe a temperature-controlled inversion of the effect, where the entropy increases on switching the…
It is important to develop sustainable processes in materials science and manufacturing that are environmentally friendly. AI can play a significant role in decision support here as evident from our earlier research leading to tools…
An exciting development over the past few decades has been the use of high-throughput computational screening as a means of identifying promising candidate materials for a variety of structural or functional properties. Experimentally, it…
We employ a combination of machine learning and first-principles calculations to predict magnetic properties of rare-earth lean magnets. For this purpose, based on training set constructed out of experimental data, the machine is trained to…
Complexity of materials designed by machine learning is currently limited by the inefficiency of classical computers. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle…
The calculation of material phonon thermal conductivity from density functional theory calculations requires computationally expensive evaluation of anharmonic interatomic force constants and has remained a computational bottleneck in the…
The use of machine learning algorithms is an attractive way to produce very fast detector simulations for scattering reactions that can otherwise be computationally expensive. Here we develop a factorised approach where we deal with each…
Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of…
Accelerated discovery with machine learning (ML) has begun to provide the advances in efficiency needed to overcome the combinatorial challenge of computational materials design. Nevertheless, ML-accelerated discovery both inherits the…
Fluid ferroelectrics, a recently discovered class of liquid crystals that exhibit switchable, long-range polar order, offer opportunities in ultrafast electro-optic technologies, responsive soft matter, and next-generation energy materials.…
Many important physical processes have dynamics that are too complex to completely model analytically. Optimisation of such processes often relies on intuition, trial-and-error, or the construction of empirical models. Machine learning…
Temperature is a fundamental regulator of chemical and biochemical kinetics, yet capturing nonlinear thermal effects directly from experimental data remains a major challenge due to limited throughput and model flexibility. Recent advances…
Predictive materials synthesis is the primary bottleneck in realizing new functional and quantum materials. Strategies for synthesis of promising materials are currently identified by time-consuming trial and error approaches and there are…