The development of statistical tools based on machine learning (ML) and deep networks is actively sought for materials design problems. While structure-property relationships can be accurately determined using quantum mechanical methods, these firstprinciples calculations are computationally demanding, limiting their use in screening a large set of candidate structures. Herein, we use convolutional neural networks to develop a predictive model for the electronic properties of metal halide perovskites (MHPs) that have a billions-range materials design space. We show that a well-designed hierarchical ML approach has a higher fidelity in predicting properties of the MHPs compared to straightforward methods. In this architecture, each neural network element has a designated role in the estimation process from predicting complex features of the perovskites such as lattice constant and octahedral till angle to narrowing down possible ranges for the values of interest. Using the hierarchical ML scheme, the obtained root-mean-square errors for the lattice constants, octahedral angle and bandgap for the MHPs are 0.01 Å, 5°, and 0.02 eV, respectively. Our study underscores the importance of a careful network design and a hierarchical approach to alleviate issues associated with imbalanced dataset distributions, which is invariably common in materials datasets.

The successful synthesis of high-entropy alloy (HEA) nanoparticles, a long-sought goal in materials science, opens a new frontier in materials science with applications across catalysis, structural alloys, and energetic materials. Recently, a Co25Mo45­Fe10Ni10Cu10 HEA made of earth-abundant elements was shown to have a high catalytic activity for ammonia decomposition, which rivals that of state-of-the-art, but prohibitively expensive, ruthenium catalysts. Using a computational approach based on first-principles calculations in conjunction with data analytics and machine learning, we build a model to rapidly compute the adsorption energy of H, N, and NH x (x = 1, 2, 3) species on CoMoFeNiCu alloy surfaces with varied alloy compositions and atomic arrangements. We show that the 25/45 Co/Mo ratio identified experimentally as the most active composition for ammonia decomposition increases the likelihood that the surface adsorbs nitrogen equivalently to that of ruthenium while at the same time interacting moderately strongly with intermediates. Our study underscores the importance of computational modeling and machine learning to identify and optimize HEA alloys across their near-infinite materials design space.

This paper presents two new theories and a new current representation to explain the magnetic force between two filamentary current elements as a result of electric force interactions between current charges. The first theory states that a current has an electric charge relative to its moving observer. The second theory states that the magnetic force is an electric force in origin. The new current representation characterizes a current as equal amounts of positive and negative point charges moving in opposite directions at the speed of light. Previous work regarded electricity and magnetism as different aspects of the same subject. One effort was made by J.O. Johnson to unify the origin of electricity and magnetism, but this effort yielded a formula that is unequal to the well-known magnetic force law. The explanation provided for the magnetic force depends on three factors: (1) representing the electric current as charges moving at the speed of light, (2) considering the relative velocity between moving charges, and (3) analyzing the electric field spreading in the space due to the movement of charges inside current elements. The electric origin of the magnetic force is proved by deriving the magnetic force law and Biot-Savart law using the electric force law. This work is helpful for unifying the concepts of magnetism and electricity.

This paper presents the first general framework to explain the magnetic force as a result of electric force interactions between current charges moving at any constant speed and combination. The explanation depends on analyzing the spreading electric field in the space and the movement of charges inside current elements. Previous work used special relativity to describe the magnetic force as an electric one, but this description contradicts the fact that electrically neutral wires stay neutral with or without current flowing through them, as well as, it does not facilitate the derivation of the infinitesimal laws of magnetism. In this paper, the provided explanation is proved by deriving the infinitesimal magnetic force law and Biot-Savart law using the basis of electric forces. This work lies at the intersection between Electrical Engineering and Physics, and it is important to understand what is magnetism and its origin. Such understanding may help engineers and scientists in making new advancements in magnetic materials and applied magnetic technologies.

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This paper presents a novel method to segment images and applies this method for segmenting bone fragments imaged using 3D Computed Tomography (CT). Existing image segmentation solutions tend to have difficulty in accurately delineating regions that have subtle variations along their boundaries or delineating regions which are spatially close. The proposed image segmentation algorithm introduces an original modification to the classical watershed transform and we refer to resulting approach as the Probabilistic Watershed Transform (PWT). The PWT uses a set of probability distributions to model the likelihood that a given pixel is a measurement obtained from each of the provided semantic classes. While the framework for the proposed PWT allows for completely general likelihood distributions, we specify several likelihood distributions which address known shortcomings in the watershed transform and, more generally, competing segmentation methods. Using these likelihood distributions, we apply the PWT to segment bone fragments within CT images of a bone fracture. A quantitative evaluation of the bone segmentation results is provided which compares our results with several leading competing methods as well as human-generated segmentation which show that the proposed method has some significant benefits for solving the bone fragment segmentation problem.