Machine learning (ML) models for materials properties are constructed from three parts: training data, a set of attributes that describe each material, and a machine learning algorithm to map attributes to properties. For the purposes of creating a general-purpose method, we focused entirely on the attributes set because the method needs to be agnostic to the type of training data and because it is possible to utilise already-developed machine learning algorithms. Specifically, our objective is to develop a general set of attributes based on the composition that can be reused for a broad variety of problems.
We found that the attributes which model a material property best can vary significantly depending on the property and type of materials in the data set. To quantify the predictive ability of each attribute, we fit a quadratic polynomial using the attribute and measured the root mean squared error of the model. We found the attributes that best describe the formation energy of crystalline compounds are based on the electronegativity of the constituent elements (e.g., maximum and mode electronegativity). In contrast, the best-performing attributes for band gap energy are the fraction of electrons in the p shell and the mean row in the periodic table of the constituent elements. In addition, the attributes that best describe the formation energy vary depending on the type of compounds. The formation energy of intermetallic compounds is best described by the variances in the melting temperature and number of d electrons between constituent elements, whereas compounds that contain at least one nonmetal are best modelled by the mean ionic character (a quantity based on electronegativity difference between constituent elements). Taken together, the changes in which attributes are the most important between these examples further support the necessity of having a large variety of attributes available in a general-purpose attribute set.
Objective General English By R S Agarwal Free Download Pdf
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It is worth noting that the 145 attributes described in this paper should not be considered the complete set for inorganic materials. The chemical informatics community has developed thousands of attributes for predicting the properties of molecules.35 What we present here is a step towards creating such a rich library of attributes for inorganic materials. While we do show in the examples considered in this work that this set of attributes is sufficient to create accurate models for two distinct properties, we expect that further research in materials informatics will add to the set presented here and be used to create models with even greater accuracy. Furthermore, many materials cannot be described simply by average composition. In these cases, we propose that the attribute set presented here can be extended with representations designed to capture additional features such as structure (e.g., Coulomb Matrix17 for atomic-scale structure) or processing history. We envision that it will be possible to construct a library of general-purpose representations designed to capture structure and other characteristics of a material, which would drastically simplify the development of new machine learning models.
In the following sections, we detail two distinct applications for our novel material property prediction technique to demonstrate its versatility: predicting three physically distinct properties of crystalline compounds and identifying potential metallic glass alloys. In both cases, we use the same general framework, i.e., the same attributes and partitioning-based approach. In each case, we only needed to identify the most accurate machine learning algorithm and find an appropriate partitioning strategy. Through these examples, we discuss all aspects of creating machine-learning-based models to design a new material: assembling a training set to train the models, selecting a suitable algorithm, evaluating model accuracy and employing the model to predict new materials.
In this work, we introduced a general-purpose machine learning framework for predicting the properties of a wide variety of materials and demonstrated its broad applicability via illustration of two distinct materials problems: discovering new potential crystalline compounds for photovoltaic applications and identifying candidate metallic glass alloys. Our method works by using machine learning to generate models that predict the properties of a material as a function of a wide variety of attributes designed to approximate chemical effects. The accuracy of our models is further enhanced by partitioning the data set into groups of similar materials. In this manuscript, we show that this technique is capable of creating accurate models for properties as different as the electronic properties of crystalline compounds and glass formability of metallic alloys. Creating new models with our strategy requires only finding which machine learning algorithm maximises accuracy and testing different partitioning strategies, which are processes that could be eventually automated.64 We envision that the versatility of this method will make it useful for a large range of problems, and help enable the quicker deployment and wider-scale use machine learning in the design of new materials.
Background: Virtual care for patients with coronavirus disease 2019 (COVID-19) allows providers to monitor COVID-19-positive patients with variable trajectories while reducing the risk of transmission to others and ensuring health care capacity in acute care facilities. The objective of this descriptive analysis was to assess the initial adoption, feasibility and safety of a family medicine-led remote monitoring program, COVIDCare@Home, to manage the care of patients with COVID-19 in the community.
Methods: COVIDCare@Home is a multifaceted, interprofessional team-based remote monitoring program developed at an ambulatory academic centre, the Women's College Hospital in Toronto. A descriptive analysis of the first cohort of patients admitted from Apr. 8 to May 11, 2020, was conducted. Lessons from the implementation of the program are described, focusing on measure of adoption (number of visits per patient total, with a physician or with a nurse; length of follow-up), feasibility (received an oximeter or thermometer; consultation with general internal medicine, social work or mental health, pharmacy or acute ambulatory care unit) and safety (hospitalizations, mortality and emergency department visits).
In a world of increasing economic nationalism and geopolitical tensions, establishing national champions is likely to remain an important policy objective for governments seeking to advance their national interests. In this context, the Growth Strategy Trilemma framework I develop below can guide policymakers in striking a balance among economic growth, stability, and national champion objectives.
The framework highlights the challenges policymakers face in balancing the competing demands of economic growth, financial and fiscal stability, and the establishment of national champions. Pursuing any two of these objectives comes at the cost of partially sacrificing the third, making it a trilemma.
These trade-offs are not simply about choosing one objective over the other, but rather striking a balance among the three (on a continuum). The best approach for any government will depend on a range of contextual factors, including the state of the economy, the financial system's health, electoral pressures in the political system, and the geopolitical environment.
In this paper, we introduce the class of generalized strongly convex functions using Raina's function. We derive two new general auxiliary results involving first and second order $ (p, q) $-differentiable functions and Raina's function. Essentially using these identities and the generalized strongly convexity property of the functions, we also found corresponding new generalized post-quantum analogues of Dragomir-Agarwal's inequalities. We discuss some special cases about generalized convex functions. To support our main results, we offer applications to special means, to hypergeometric functions, to Mittag-Leffler functions and also to $ (p, q) $-differentiable functions of first and second order that are bounded in absolute value.
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