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Machine learned interatomic potentials using random features

npj Computational Materials 2022年第1期8卷 48-57页

作者： gurjot dhaliwal Prasanth B.Nair Chandra Veer Singh Department of Mechanical and Industrial Engineering University of Toronto5 King’s College RdTorontoM5S 3G8ONCanada Institute for Aerospace Studies University of Toronto4925 Dufferin StTorontoM3H 5T6ONCanada Department of Materials Science and Engineering University of Toronto184 College StTorontoM5S 3E4ONCanada

We present a method to model interatomic interactions such as energy and forces in a computationally efficient *** proposed model approximates the energy/forces using a linear combination of random features,thereby enabling fast parameter estimation by solving a linear least-squares *** discuss how random features based on stationary and non-stationary kernels can be used for energy approximation and provide results for three classes of materials,namely two-dimensional materials,metals and *** and energy predictions made using the proposed method are in close agreement with density functional theory calculations,with training time that is 96%lower than standard kernel *** Dynamics calculations using random features based interatomic potentials are shown to agree well with experimental and density functional theory *** frequencies as computed by random features based interatomic potentials are within 0.1%of the density functional theory ***,the proposed random features-based potential addresses scalability issues encountered in this class of machine learning problems.

关键词： random stationary interatomic

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Alkali Ionic Conductivity in Inorganic Glassy Electrolytes

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Journal of Materials Science and Chemical Engineering 2023年第7期11卷 31-72页

作者： Ram Krishna Hona Mandy Guinn Uttam S. Phuyal S’Nya Sanchez gurjot S. dhaliwal Department of Environmental Science United Tribes Technical College Bismarck USA School of Arts and Science University of Mount Olive Mount Olive USA Intertribal Research and Resource Center United Tribes Technical College Bismarck USA

Glassy electrolytes could be a potential candidate for all-solid-state batteries that are considered new-generation energy storage devices. As glasses are one of the potential fast ion-conducting electrolytes, progressive advances in glassy electrolytes have been undergoing to get commercial attention. However, the challenges offered by ionic conductivity at room temperature (10−5 - 10−3 S∙cm−1) in comparison to those of organic liquid electrolytes (10−2 S∙cm−1) hindered the applicability of such electrolytes. To enhance the research development on ionic conductivity, the overall picture of the ionic conductivity of glassy electrolytes is reviewed in this article with a focus on alkali oxide and sulfide glasses. We portray here the techniques applied for alkali ion conductivity enhancement, such as methods of glass preparation, host optimization, doping, and salt addition for enhancing alkali ionic conductivity in the glasses.

关键词： Glass Electrolyte Solid State Ionic Glass Battery Charge Antiperovskite Ionic Conductivity

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Specific Heat Capacity of A₂FeCoO_6-δ (A = Ca or Sr)

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Journal of Materials Science and Chemical Engineering 2023年第4期11卷 1-10页

作者： S’Nya Sanchez Mandy Guinn Uttam S. Phuyal gurjot S. dhaliwal Ram Krishna Hona Environmental Science Department United Tribes Technical College Bismarck ND USA School of Arts and Science University of Mt. Olive Mount Olive NC USA Intertribal Research and Resource Center United Tribes Technical College Bismarck ND USA

A2FeCoO6-δ (A = Ca or Sr) is synthesized by the solid-state synthesis method and their specific heat capacities are evaluated at 40˚C using a heat flow meter. The effect of the A-cation size on the specific heat capacity of these compounds is observed. The specific heat capacity of Sr2FeCoO6-δ is found to be the highest, and that of Ca2FeCoO6-δ is the lowest while CaSrFeCoO6-δ shows the intermediate value. The specific heat capacity decreases with the decrease of the average A-site ionic radius, demonstrating the relationship between heat capacity and A-site ionic radius. The relationship between specific heat capacity and molar mass is also confirmed as the δ value decreases or molar mass increases from Ca2FeCoO6-δ to CaSrFeCoO6-δ to Sr2FeCoO6-δ.

关键词： Perovskite Oxide Specific Heat Capacity Oxygen Vacancy XRD Vacancy Order

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