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Abstract

The present disclosure describes techniques for generating a digital twin to represent the chemical properties, elemental properties, elemental components, parametric components, and/or molecular components for a resource. A sample of a resource may be obtained and analyzed to identify one or more molecular descriptors contained in the resource. Further analysis of the one or more molecular descriptors and/or the resource may identify gaps in the data and/or information about the resource. Using machine-learning models and a chemistry knowledgebase, the gaps in the data and/or information about the resource may be filled. Further, the machine-learning models described herein may be used to generate a digital twin of the resource that represents the resource in a digital form such that the resource may be tracked accurately throughout its lifecycle, including how the resource may change due to environmental conditions, storage conditions, and/or custodial changes.

First Claim