Related papers: Enhancing Magnetocaloric Material Discovery: A Mac…
A new class of rare-earth-free permanent magnets is proposed. The parent compound of this class is Co$_3$Mn$_2$Ge, and its discovery is the result of first principles theory combined with experimental synthesis and characterisation. The…
The discovery of new superconducting materials, particularly those exhibiting high critical temperature ($T_c$), has been a vibrant area of study within the field of condensed matter physics. Conventional approaches primarily rely on…
This thesis demonstrate the efficacy of designing and developing machine learning (ML) algorithms to selected use cases that encompass many of the outstanding challenges in the field of experimental high energy physics. Although simple…
Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its…
Exploration of new superconductors still relies on the experience and intuition of experts and is largely a process of experimental trial and error. In one study, only 3% of the candidate materials showed superconductivity. Here, we report…
We have investigated the magnetocaloric effects in antiferromagnets and compared them with those in ferromagnets using Monte Carlo simulations. In antiferromagnets, the magnetic entropy reaches a maximum value at a finite magnetic field…
Magnetic refrigeration based on the magnetocaloric effect at room temperature is one of the most attractive alternative to the current gas compression/expansion method routinely employed. Nevertheless, in giant magnetocaloric materials,…
Recent realizations of intrinsic, long-range magnetic orders in two-dimensional (2D) van der Waals materials have ignited tremendous research interests. In this work, we employ the XXZ Heisenberg model and Monte Carlo simulations to study a…
High-entropy alloys are solid solutions of multiple principal elements, capable of reaching composition and feature regimes inaccessible for dilute materials. Discovering those with valuable properties, however, relies on serendipity, as…
Magnetic refrigeration presents an energy-efficient and environmentally benign alternative to traditional vapour-compression cooling technologies. It relies on the magnetocaloric effect, in which the temperature of a magnetic material…
The behaviour of molecules in space is to a large extent governed by where they freeze out or sublimate. The molecular binding energy is thus an important parameter for many astrochemical studies. This parameter is usually determined with…
Heusler alloys are promising for several applications, including magnetic refrigeration, due to high magnetocaloric and magnetovolume effects. One way to optimize this potential is by increasing the magnetovolume effect. Using density…
The optimal management of a building's microclimate to satisfy the occupants' needs and objectives in terms of comfort, energy efficiency, and costs is particularly challenging. This complexity arises from the non-linear, time-dependent…
This work introduces an approach rooted in quantum thermodynamics to enhance sampling efficiency in quantum machine learning (QML). We propose conceptualizing quantum supervised learning as a thermodynamic cooling process. Building on this…
Power and thermal management are critical components of High-Performance-Computing (HPC) systems, due to their high power density and large total power consumption. The assessment of thermal dissipation by means of compact models directly…
Highly coherent mechanical resonators are invaluable to ultrasensitive detection techniques by enabling detection of small forces. Studying mechanical resonators in a thermal equilibrium state at millikelvin temperatures provides a…
Low-temperature alloys are important for a wide spectrum of modern technologies ranging from liquid hydrogen, and superconductivity to quantum technology. These applications push the limit of material performance into extreme coldness,…
A Boltzmann machine whose effective "temperature" can be dynamically "cooled" provides a stochastic neural network realization of simulated annealing, which is an important metaheuristic for solving combinatorial or global optimization…
Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to…
We present a general framework for modeling power magnetic materials characteristics using deep neural networks. Magnetic materials represented by multidimensional characteristics (that mimic measurements) are used to train the neural…