Representations of Materials for Machine Learning

High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning relationships among composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by a machine learning model. Datasets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and property of interests. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs of machine learning models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus, require further investigation.Comment: 20 pages, 5 figures, To Appear in Annual Review of Materials Research 5

Theoretical Models of Spintronic Materials

In the past three decades, spintronic devices have played an important technological role. Half-metallic alloys have drawn much attention due to their special properties and promised spintronic applications. This dissertation describes some theoretical techniques used in first-principal calculations of alloys that may be useful for spintronic device applications with an emphasis on half-metallic ferromagnets. I consider three types of simple spintronic materials using a wide range of theoretical techniques. They are (a) transition metal based half-Heusler alloys, like CrMnSb, where the ordering of the two transition metal elements within the unit cell can cause the material to be ferromagnetic semiconductors or semiconductors with zero net magnetic moment, (b) half-Heusler alloys involving Li, like LiMnSi, where the Li stabilizes the structure and increases the magnetic moment of zinc blende half-metals by one Bohr magneton per formula unit, and (c) zinc blende alloys, like CrAs, where many-body techniques improve the fundamental gap by considering the physical effects of the local field. Also, I provide a survey of the theoretical models and numerical methods used to treat the above systems

Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning

AbstractCalculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multi-component catalysts. To address this issue, we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide ranges of thermodynamic conditions and can be implemented with any internal energy model U, allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements.</jats:p

Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning

AbstractCalculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multi-component catalysts. To address this issue, we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide ranges of thermodynamic conditions and can be implemented with any internal energy model U, allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements.</jats:p