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Register a new userMachine Learning Guided Discovery of Gigantic Magnetocaloric Effect in HoB$_{2}$ Near Hydrogen Liquefaction Temperature
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Magnetic refrigeration exploits the magnetocaloric effect which is the entropy change upon application and removal of magnetic fields in materials, providing an alternate path for refrigeration other than the conventional gas cycles. While intensive research has uncovered a vast number of magnetic materials which exhibits large magnetocaloric effect, these properties for a large number of compounds still remain unknown. To explore new functional materials in this unknown space, machine learning is used as a guide for selecting materials which could exhibit large magnetocaloric effect. By this approach, HoB$_{2}$ is singled out, synthesized and its magnetocaloric properties are evaluated, leading to the experimental discovery of gigantic magnetic entropy change 40.1 J kg$^{-1}$ K$^{-1}$ (0.35 J cm$^{-3}$ K$^{-1}$) for a field change of 5 T in the vicinity of a ferromagnetic second-order phase transition with a Curie temperature of 15 K. This is the highest value reported so far, to our knowledge, near the hydrogen liquefaction temperature thus it is a highly suitable material for hydrogen liquefaction and low temperature magnetic cooling applications.
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Recently, a massive magnetocaloric effect near the liquefaction temperature of hydrogen has been reported in the ferromagnetic material HoB$_{2}$. Here we investigate the effects of Dy substitution in the magnetocaloric properties of Ho$_{1-x}$Dy$_{x}$B$_{2}$ alloys ($textit{x}$ = 0, 0.3, 0.5, 0.7, 1.0). We find that the Curie temperature ($textit{T}$$_{C}$) gradually increases upon Dy substitution, while the magnitude of the magnetic entropy change |$Delta textit{S}_{M}$| at $textit{T}$ = $textit{T}_{C}$ decreases from 0.35 to 0.15 J cm$^{-3}$ K$^{-1}$ for a field change of 5 T. Due to the presence of two magnetic transitions in these alloys, despite the change in the peak magnitude of |$Delta textit{S}_{M}$|, the refrigerant capacity ($textit{RC}$) and refrigerant cooling power ($textit{RCP}$) remains almost constant in all doping range, which as large as 5.5 J cm$^{-3}$ and 7.0 J cm$^{-3}$ for a field change of 5 T. These results imply that this series of alloys could be an exciting candidate for magnetic refrigeration in the temperature range between 10-50 K.
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development. The enormous complexity involved in existing multi-variable synthesis methods leads to high uncertainty, numerous trials and exorbitant cost. Recently, machine learning (ML) has demonstrated tremendous potential for material research. Here, we report the application of ML to optimize and accelerate material synthesis process in two representative multi-variable systems. A classification ML model on chemical vapor deposition-grown MoS2 is established, capable of optimizing the synthesis conditions to achieve higher success rate. While a regression model is constructed on the hydrothermal-synthesized carbon quantum dots, to enhance the process-related properties such as the photoluminescence quantum yield. Progressive adaptive model is further developed, aiming to involve ML at the beginning stage of new material synthesis. Optimization of the experimental outcome with minimized number of trials can be achieved with the effective feedback loops. This work serves as proof of concept revealing the feasibility and remarkable capability of ML to facilitate the synthesis of inorganic materials, and opens up a new window for accelerating material development.
Thermoelectric conversion using Seebeck effect for generation of electricity is becoming an indispensable technology for energy harvesting and smart thermal management. Recently, the spin-driven thermoelectric effects (STEs), which employ emerging phenomena such as the spin-Seebeck effect (SSE) and the anomalous Nernst effect (ANE), have garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the mechanism and materials parameters that govern the STEs. To address this problem, we enlist machine learning modeling to establish the key physical parameters controlling SSE. Guided by these models, we have carried out a high-throughput experiment which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation STE devices.
Machine learning models of materials$^{1-5}$ accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated results at one hundred thousandths of the cost of DFT$^{6}$. To provide guidance in experimental materials synthesis, these need to be coupled with an accurate yet effective search algorithm and training data consistent with experimental observations. Here we report an evolutionary algorithm powered search which uses machine-learned surrogate models trained on high-throughput hybrid functional DFT data benchmarked against experimental bandgaps: Deep Adaptive Regressive Weighted Intelligent Network (DARWIN). The strategy enables efficient search over the materials space of ~10$^8$ ternaries and 10$^{11}$ quaternaries$^{7}$ for candidates with target properties. It provides interpretable design rules, such as our finding that the difference in the electronegativity between the halide and B-site cation being a strong predictor of ternary structural stability. As an example, when we seek UV emission, DARWIN predicts K$_2$CuX$_3$ (X = Cl, Br) as a promising materials family, based on its electronegativity difference. We synthesized and found these materials to be stable, direct bandgap UV emitters. The approach also allows knowledge distillation for use by humans.
Magnetic entropy and adiabatic temperature changes in and above the room-temperature region has been measured for La0.7Sr0.3Mn1-xMxO3 (M = Al, Ti) by means of magnetization and heat capacity measurements in magnetic fields up to 6 T. The magnetocaloric effect becomes largest at the ferromagnetic ordering temperature Tc that is tuned to ~300 K by the substitution of Al or Ti for Mn. While the substitution of Al for Mn drastically reduces the entropy change, it extends considerably the working temperature span and improves the relative cooling power. The magnetocaloric effect seems to be only lightly affected by Ti substitution. Although manganites have been considered potential for magnetic refrigerants, the magnetocaloric effect in these materials is limited due to the existence of short-range ferromagnetic correlations above Tc.
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