High-throughput search for magnetic and topological order in transition metal oxides

The energy landscape of magnetic materials

Magnetic materials can display many solutions to the electronic-structure problem, corresponding to different local or global minima of the energy functional. In Hartree-Fock or density-functional theory different single-determinant solutions lead to different magnetizations, ionic oxidation states, hybridizations, and inter-site magnetic couplings. The vast majority of these states can be fingerprinted through their projection on the atomic orbitals of the magnetic ions. We have devised an approach that provides an effective control over these occupation matrices, allowing us to systematically explore the landscape of the potential energy surface. We showcase the emergence of a complex zoology of self-consistent states; even more so when semi-local density-functional theory is augmented - and typically made more accurate - by Hubbard corrections. Such extensive explorations allow to robustly identify the ground state of magnetic systems, and to assess the accuracy (or not) of current functionals and approximations.

Scientific Discovery Framework Accelerating Advanced Polymeric Materials Design

Organic polymer materials, as the most abundantly produced materials, possess a flammable nature, making them potential hazards to human casualties and property losses. Target polymer design is still hindered due to the lack of a scientific foundation. Herein, we present a robust, generalizable, yet intelligent polymer discovery framework, which synergizes diverse capabilities, including the in situ burning analyzer, virtual reaction generator, and material genomic model, to achieve results that surpass the sum of individual parts. Notably, the high-throughput analyzer created for the first time, grounded in multiple spectroscopic principles, enables in situ capturing of massive combustion intermediates; then, the created realistic apparatus transforming to the virtual reaction generator acquires exponentially more intermediate information; further, the proposed feature engineering tool, which embedded both polymer hierarchical structures and massive intermediate data, develops the generalizable genomic model with excellent universality (adapting over 20 kinds of polymers) and high accuracy (88.8%), succeeding discovering series of novel polymers. This emerging approach addresses the target polymer design for flame-retardant application and underscores a pivotal role in accelerating polymeric materials discovery.

First-principles predictions of room-temperature ferromagnetism in orthorhombic MnX2 (X = O, S) monolayers

Two-dimensional ferromagnets with high spin-polarization at ambient temperature are of considerable interest because they might be useful for making nanoscale spintronic devices. We report that even though bulk phases of MnO2 are generally antiferromagnetic with low ordering temperatures, the corresponding MnO2 and MnS2 monolayers are ferromagnetic, and MnS2 is a high temperature half metallic ferromagnet. Based on first-principles calculations, we find that the MnO2 monolayer is an intrinsic ferromagnetic semiconductor with a Curie temperature TC of ∼300 K, while the half-metallic MnS2 monolayer has a remarkably high TC of ∼1150 K. Both compounds have substantial magnetocrystalline anisotropy, out of plane in the case of MnO2 monolayers, and in plane along the b-axis of orthorhombic MnS2 monolayer. Interestingly, a metal-insulator phase transition occurs in the MnS2 monolayer when the applied biaxial strain is beyond -2%. Tuning near this metal-insulator transition offers additional possibilities for devices. The present work shows that MnX2 (X = O, S) monolayers have the properties required for ultrathin nano-spintronic devices.

Gradient boosted and statistical feature selection workflow for materials property predictions

With the emergence of big data initiatives and the wealth of available chemical data, data-driven approaches are becoming a vital component of materials discovery pipelines or workflows. The screening of materials using machine-learning models, in particular, is increasingly gaining momentum to accelerate the discovery of new materials. However, the black-box treatment of machine-learning methods suffers from a lack of model interpretability, as feature relevance and interactions can be overlooked or disregarded. In addition, naive approaches to model training often lead to irrelevant features being used which necessitates the need for various regularization techniques to achieve model generalization; this incurs a high computational cost. We present a feature-selection workflow that overcomes this problem by leveraging a gradient boosting framework and statistical feature analyses to identify a subset of features, in a recursive manner, which maximizes their relevance to the target variable or classes. We subsequently obtain minimal feature redundancy through multicollinearity reduction by performing feature correlation and hierarchical cluster analyses. The features are further refined using a wrapper method, which follows a greedy search approach by evaluating all possible feature combinations against the evaluation criterion. A case study on elastic material-property prediction and a case study on the classification of materials by their metallicity are used to illustrate the use of our proposed workflow; although it is highly general, as demonstrated through our wider subsequent prediction of various material properties. Our Bayesian-optimized machine-learning models generated results, without the use of regularization techniques, which are comparable to the state-of-the-art that are reported in the scientific literature.

Manipulating Topological Phases in Magnetic Topological Insulators

Magnetic topological insulators (MTIs) are a group of materials that feature topological band structures with concurrent magnetism, which can offer new opportunities for technological advancements in various applications, such as spintronics and quantum computing. The combination of topology and magnetism introduces a rich spectrum of topological phases in MTIs, which can be controllably manipulated by tuning material parameters such as doping profiles, interfacial proximity effect, or external conditions such as pressure and electric field. In this paper, we first review the mainstream MTI material platforms where the quantum anomalous Hall effect can be achieved, along with other exotic topological phases in MTIs. We then focus on highlighting recent developments in modulating topological properties in MTI with finite-size limit, pressure, electric field, and magnetic proximity effect. The manipulation of topological phases in MTIs provides an exciting avenue for advancing both fundamental research and practical applications. As this field continues to develop, further investigations into the interplay between topology and magnetism in MTIs will undoubtedly pave the way for innovative breakthroughs in the fundamental understanding of topological physics as well as practical applications.

Investigating the A n+1B n X3n+1 Homologous Series: A New Platform for Studying Magnetic Praseodymium Based Intermetallics

The recent discovery of the A _n+1B _n X_3n+1 (A = lanthanide, B = transition metal, X = tetrel) homologous series provides a new platform to study the structure-property relationships of highly correlated electron systems. Several members of Ce _n+1Co _n Ge_3n+1 (n = 1, 4, 5, 6, and ∞) show evidence of heavy electron behavior with complex magnetic interactions. While the Ce analogues have been investigated, only n = 1, 2, and ∞ of Pr _n+1Co _n Ge_3n+1 have been synthesized, with n = 1 and 2 showing a nonsinglet magnetic ground state. The Pr analogues can provide a platform for direct comparison of highly correlated behavior. In this perspective, we discuss the impetus for synthesizing the Pr _n+1Co _n Ge_3n+1 members and present the structural characterization of the n = 3 and n = 4 members. We lay the foundation for future investigations of the Pr _n+1Co _n Ge_3n+1 family of compounds and highlight the importance of complementary methods to characterize new quantum materials.

Predictions on the Phase Constitution of SmCo7−XMx Alloys by Data Mining

Based on a home-built Sm-Co-based alloys database, this work proposes a support vector machine model to study the concurrent effects of element doping and microstructure scale on the phase constitution of SmCo₇-based alloys. The results indicated that the doping element’s melting point and electronegativity difference with Co are the key features that affect the stability of the 1:7 H phase. High-throughput predictions on the phase constitution of SmCo₇-based alloys with various characteristics were achieved. It was found that doping elements with electronegativity differences with Co that are smaller than 0.05 can significantly enhance 1:7 H phase stability in a broad range of grain sizes. When the electronegativity difference increases to 0.4, the phase stability becomes more dependent on the melting point of the doping element, the doping concentration, and the mean grain size of the alloy. The present data-driven method and the proposed rule for 1:7 H phase stabilization were confirmed by experiments. This work provides a quantitative strategy for composition design and tailoring grain size to achieve high stability of the 1:7 H phase in Sm-Co-based permanent magnets. The present method is applicable for evaluating the phase stability of a wide range of metastable alloys.

Quantum Materials Manufacturing

The quantum age is just around the corner. As quantum systems become more stable, robust, and mainstream, tackling the challenge of high-throughput manufacturing will require further developments in materials synthesis, characterization, assembly, and diagnostics. As the building blocks of future technologies scale down to atomic and molecular scales, a paradigm shift in manufacturing will begin to take shape. Inspired by a quantum manufacturing world that elevates the Materials Genome Initiative to the next level, a "human-in-the-loop" framework for high-throughput manufacturing, which addresses key opportunities and challenges to be overcome, is outlined.

Big data and machine learning for materials science

Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.