Machine Learning Guided Discovery of Gigantic Magnetocaloric Effect in HoB_2 Near Hydrogen Liquefaction Temperature

by   Pedro Baptista de Castro, et al.

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.


page 5

page 17

page 18


Element selection for functional materials discovery by integrated machine learning of atomic contributions to properties

At the high level, the fundamental differences between materials origina...

How to Build the Optimal Magnet Assembly for Magnetocaloric Cooling: Structural Optimization with Isogeometric Analysis

In the search for more efficient and less environmentally harmful coolin...

Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Imaging techniques are essential tools for inquiring a number of propert...

Pigment Adsorption Optimization in Various Low Cost Adsorbents

Abstract: The applications of adsorption are very important. The followi...

Numerical methods for antiferromagnetics

Compared with ferromagnetic counterparts, antiferromagnetic materials ar...

Unveiling Exotic Magnetic Phases in Fibonacci Quasicrystalline Stacking of Ferromagnetic Layers through Machine Learning

In this study, we conduct a comprehensive theoretical analysis of a Fibo...

Investigation on Machine Learning Based Approaches for Estimating the Critical Temperature of Superconductors

Superconductors have been among the most fascinating substances, as the ...

Please sign up or login with your details

Forgot password? Click here to reset