Machine Learning Decomposition Onset Temperature of Lubricant Additives
Source abstract
Abstract
The thermal stability of lubricant additives is a fundamental parameter in practical applications, which is determined by the molecular structure. The ability to predict thermal properties, particularly lubricant additives’ decomposition onset temperature, is of ultimate importance. We develop the Gaussian process regression model to present the relationship between molecular descriptors and onset temperature of decomposition of lubricant additives. This model is highly stable and accurate, which is promising as a fast, robust, and low-cost tool for estimating various types of lubricant additives’ decomposition onset temperature.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Machine Learning in Materials Science · Computational Drug Discovery Methods · Chemical Thermodynamics and Molecular Structure
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (43)
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