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In many subfields of chemistry and physics, numerous attempts have been made to accelerate scientific discovery using data-driven experimental design algorithms. Among them, Bayesian ...optimization has been proven to be an effective tool. A standard implementation (e.g., scikit-learn), however, can accommodate only small training data. We designed an efficient protocol for Bayesian optimization that employs Thompson sampling, random feature maps, one-rank Cholesky update and automatic hyperparameter tuning, and implemented it as an open-source python library called COMBO (COMmon Bayesian Optimization library). Promising results using COMBO to determine the atomic structure of a crystalline interface are presented. COMBO is available at https://github.com/tsudalab/combo.
Batteries are a critical component of modern society. The growing demand for new battery materials—coupled with a historically long materials development time—highlights the need for advances in ...battery materials development. Understanding battery systems has been frustratingly slow for the materials science community. In particular, the discovery of more abundant battery materials has been difficult. In this paper, we describe how machine learning tools can be exploited to predict the properties of battery materials. In particular, we report the challenges associated with a data-driven investigation of battery systems. Using a dataset of cathode materials and various statistical models, we predicted the specific discharge capacity at 25 cycles. We discuss the present limitations of this approach and propose a paradigm shift in the materials research process that would better allow data-driven approaches to excel in aiding the discovery of battery materials.
•Significance of structural modifications in Gd3Fe5-xScxO12 for magnetic refrigeration applications.•XRD, DFT, Mossbauer spectral analysis, and magnetic data consistently support preferred Sc3+ ...substitution at the octahedral site (x < 1.0).•Enhanced magnetocaloric properties: High magnetic entropy change (-ΔSm) of 3.82 J/(kg·K) at 37.5 K, H = 5 T.•High relative cooling power (RCP) of 408 J/kg at H = 5 T for × = 0.25.•Potential practical applications as a magnetocaloric material.
This paper presents the tunable magnetic and magnetocaloric properties of Scandium-doped gadolinium iron garnets, Gd3 Fe5-xScxO12 (x = 0.0 to 0.25) compounds prepared by a facile auto-combustion method. The sample analyzed has a dominant cubic crystal structure (space group: Ia3¯d)) with a small fraction of an orthorhombic secondary phase, as determined by Rietveld analysis of X-ray diffraction patterns. The structural, magnetic, Mössbauer spectroscopy and first-principles density functional theory (DFT) studies show a preferential substitution of Sc3+ at the octahedral site of Fe3+ions. The ferrimagnetic (FIM) transition is present in all samples, with the transition temperature decreasing from 560 K for × = 0.0 to 521 K for × = 0.25. The Sc3+dopedGd3Fe4.75Sc0.25O12exhibits an improved magnetocaloric effect (MCE) with a maximum magnetic entropy change (-ΔSMmax)3.82 J kg-1K−1, and a higher relative cooling power (RCP) value of 408 J kg−1, which is ∼ 7 % higher than the Gd3Fe5O12(380 J kg−1) sample. These findings suggest that by incorporating Sc3+, the magnetic and magnetocaloric properties of Gd3Fe5-xScxO12 can be tailored for potential applications in low-temperature magnetic refrigeration.
We investigate the magnetic and thermodynamic properties of transition metal dichalcogenides of the form A2X4, based on monolayer Mn2Se4, using data analytics. In particular, we combine ...first-principles calculations with machine learning methods to elucidate the microscopic origins of the magnetocrystalline anisotropy in these materials. We explore a large number of candidate transition metal dichalcogenides by varying the chemical compositions of the transition metal (A) sites and the chalcogen (X) sites. We investigate the magnetocrystalline anisotropy by studying the transition between in-plane and out-of-plane magnetization. Using data analytics we demonstrate that the interplay between the spin–orbit interactions of the chalcogen and transition metal atoms can impact the magnetic behavior. Finally, we identity several novel transition metal dichalcogenides with large anisotropies that are chemically stable.
•A database of magnetic anisotropy properties comprising TMD monolayers is created.•We develop AI tools to accelerate the prediction of magnetic and thermodynamic properties.•Novel materials with high anisotropy are predicted using artificial intelligence.
We investigate a strain-induced topological phase transition in the ferromagnetic Janus monolayer MnSbBiS
2
Te
2
using first-principles calculations. The electronic, magnetic, and topological ...properties are studied under biaxial strain within the range of −8 to +8%. The ground state of monolayer MnSbBiS
2
Te
2
is metallic with an out-of-plane magnetic easy axis. A band gap is opened when a compressive strain between −4% and −7% is applied. We observe a topological phase transition at a biaxial strain of −5%, where the material becomes a Chern insulator exhibiting a quantum anomalous hall (QAH) effect. We find that biaxial strain and spin-orbit coupling (SOC) are responsible for the topological phase transition in MnSbBiS
2
Te
2
. In addition, we find that biaxial strain can alter the direction of the magnetic easy axis of MnSbBiS
2
Te
2
. The Curie temperature is calculated using the Heisenberg model and is found to be 24 K. This study could pave the way to the design of topological materials with potential applications in spintronics, quantum computing, and dissipationless electronics.
Strain-induced topological phase transition in the ferromagnetic Janus monolayer MnSbBiS
2
Te
2
is displayed.
We investigate a strain-induced topological phase transition in the ferromagnetic Janus monolayer MnSbBiS2Te2 using first-principles calculations. The electronic, magnetic, and topological properties ...are studied under biaxial strain within the range of −8 to +8%. The ground state of monolayer MnSbBiS2Te2 is metallic with an out-of-plane magnetic easy axis. A band gap is opened when a compressive strain between −4% and −7% is applied. We observe a topological phase transition at a biaxial strain of −5%, where the material becomes a Chern insulator exhibiting a quantum anomalous hall (QAH) effect. We find that biaxial strain and spin–orbit coupling (SOC) are responsible for the topological phase transition in MnSbBiS2Te2. In addition, we find that biaxial strain can alter the direction of the magnetic easy axis of MnSbBiS2Te2. The Curie temperature is calculated using the Heisenberg model and is found to be 24 K. This study could pave the way to the design of topological materials with potential applications in spintronics, quantum computing, and dissipationless electronics.
We investigate a strain-induced topological phase transition in the ferromagnetic Janus monolayer MnSbBiS
Te
using first-principles calculations. The electronic, magnetic, and topological properties ...are studied under biaxial strain within the range of -8 to +8%. The ground state of monolayer MnSbBiS
Te
is metallic with an out-of-plane magnetic easy axis. A band gap is opened when a compressive strain between -4% and -7% is applied. We observe a topological phase transition at a biaxial strain of -5%, where the material becomes a Chern insulator exhibiting a quantum anomalous hall (QAH) effect. We find that biaxial strain and spin-orbit coupling (SOC) are responsible for the topological phase transition in MnSbBiS
Te
. In addition, we find that biaxial strain can alter the direction of the magnetic easy axis of MnSbBiS
Te
. The Curie temperature is calculated using the Heisenberg model and is found to be 24 K. This study could pave the way to the design of topological materials with potential applications in spintronics, quantum computing, and dissipationless electronics.
In this work, we investigate magnetic monolayers of the form A
i
A
ii
B
4
X
8
based on the well-known intrinsic topological magnetic van der Waals (vdW) material MnBi
2
Te
4
(MBT) using ...first-principles calculations and machine learning techniques. We select an initial subset of structures to calculate the thermodynamic properties, electronic properties, such as the band gap, and magnetic properties, such as the magnetic moment and magnetic order using density functional theory (DFT). Data analytics approaches are used to gain insight into the microscopic origin of materials' properties. The dependence of materials' properties on chemical composition is also explored. For example, we find that the formation energy and magnetic moment depend largely on A and B sites whereas the band gap depends on all three sites. Finally, we employ machine learning tools to accelerate the search for novel vdW magnets in the MBT family with optimized properties. This study creates avenues for rapidly predicting novel materials with desirable properties that could enable applications in spintronics, optoelectronics, and quantum computing.
In this work, we investigate magnetic monolayers of the form A
i
A
ii
B
4
X
8
based on the well-known intrinsic topological magnetic van der Waals (vdW) material MnBi
2
Te
4
(MBT) using first-principles calculations and machine learning techniques.
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