Machine-Learning-Based Chemical Analysis

A computing device comprising:

• one or more processors;
• and
• memory storing instructions that, when executed by the one or more processors, cause the computing device to:
• receive an indication of elemental components associated with a resource, wherein the elemental components comprise at least one of:
• a percentage of each elemental component, and a density of each elemental component;
• analyze the elemental components to determine a plurality of chemical descriptors associated with the resource;
• identify one or more information gaps associated with the resource;
• query, based on an identification of the one or more information gaps, a database to determine additional information associated with the plurality of chemical descriptors to fill the one or more information gaps, wherein the additional information comprises at least one of:
• atomic weight, atomic details, magnetic bonding, electrical bonding, compound information, reactivity information, statefulness, or environmental behavior;
• generate, using one or more machine learning models and based on the plurality of chemical descriptors and based on the additional information, a plurality of probabilistic outcomes for a current state of the resource, wherein each of the plurality of probabilistic outcomes:
• identifies a chemical make-up of the resource, and
• is associated with a likelihood percentage that the current state of the resource comprises the identified chemical make-up;
• select, based on the likelihood percentage associated with each of the plurality of probabilistic outcomes, a first probabilistic outcome as being representative of the current state of the resource;
• generate, based on a selection of the first probabilistic outcome, a data object to represent a molecular composition of the current state of the resource wherein the data object comprises a digital twin that allows the computing device to track an evolution of the resource throughout its lifecycle;
• and
• store the data object in a distributed ledger.

A non-transitory computer-readable medium storing instructions that, when executed, cause a computing device to:

• receive an indication of elemental components associated with a resource, wherein the elemental components comprise at least one of:
• a percentage of each elemental component, and a density of each elemental component;
• analyze the elemental components to determine a plurality of chemical descriptors associated with the resource;
• identify one or more information gaps associated with the resource;
• query, based on an identification of the one or more information gaps, a database to determine additional information associated with the plurality of chemical descriptors to fill the one or more information gaps, wherein the additional information comprises at least one of:
• atomic weight, atomic details, magnetic bonding, electrical bonding, compound information, reactivity information, statefulness, or environmental behavior;
• generate, using one or more machine learning models and based on the plurality of chemical descriptors and based on the additional information, a plurality of probabilistic outcomes for a current state of the resource, wherein each of the plurality of probabilistic outcomes:
• identifies a chemical make-up of the resource, and
• is associated with a likelihood percentage that the current state of the resource comprises the identified chemical make-up;
• select, based on the likelihood percentage associated with each of the plurality of probabilistic outcomes, a first probabilistic outcome as being representative of the current state of the resource;
• generate, based on a selection of the first probabilistic outcome, a data object to represent a molecular composition of the current state of the resource wherein the data object comprises a digital twin that allows the computing device to track an evolution of the resource throughout its lifecycle;
• and
• store the data object in a distributed ledger.