Abstract
Long developing period and cumbersome evaluation for the lubricating materials performance seriously jeopardize the successful development and application of any database system in tribological field. Such major setback can be solved effectively by implementing approaches with high throughput calculation. However, it often involves with vast number of output files, which are computed on the basis of first principle computation, having different data format from that of their experimental counterparts. Commonly, the input, storage and management of first principle calculation files and their individually test counterparts, implementing fast query and display in the database, adding to the use of physical parameters, as predicted with the performance estimated by first principle approach, may solve such setbacks. Investigation is thus performed for establishing database website specifically for lubricating materials, which satisfies both data: (i) as calculated on the basis of first principles and (ii) as obtained by practical experiment. It further explores preliminarily the likely relationship between calculated physical parameters of lubricating oil and its respectively tribological and anti-oxidative performance as predicted by lubricant machine learning model. Success of the method facilitates in instructing the obtainment of optimal design, preparation and application for any new lubricating material so that accomplishment of high performance is possible.
Highlights
Establishment of an effective and efficient data structure generally requires the premise for building up an appropriate database platform
Data input modules in this paper are designed to include inputting data from both experiments and/or those files calculated by first-principles, which commonly fuse together the values of physical parameter and service performance of the associated materials
Implementation of first principles calculations to software like VASP26, Materials Studio[27] and CASTEP28 has been carried out. It has used Materials Studio simulation software to calculate physical parameters of the lubricating additives in this article, and its outputs are imported into relevant modules in the database, machine learning models within Tensorflow for predicting the associated performance
Summary
Establishment of an effective and efficient data structure generally requires the premise for building up an appropriate database platform. A Structure-Performance database of lubricating materials has been built to store data and analyze the information achievable from high throughput calculation and experiment. The functionalities of the preprocessor and the database fundamentally consist of: uploading and inputting data (both tested and simulated ones) in different formats; searching information by keying in keywords or parameters; predicting performance via first principle calculations.
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