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ChemML applies ML to characterize, predict, and remediate contaminant flow.


Contamination problems are difficult to solve. The data are typically limited and uncertain. The processes are complex and non-linear. The stakes are high. The consequences of failure are severe.

ChemML applies ML to analyze and process contaminant data.

ChemML extracts information about contamination processeses.

ChemML is not a black-box.ChemML develops science-informed ML models that characterize and predict the spread of contaminants.The obtained AI/ML models are applied to faciliate the management and remediation of contaminated sites.

It is important to note that traditional models representing complex geochemical process accounting for multiple geochemical species are challenging to develop. Typically, these models also requires huge computational resources and execution times. As a result, the assimilation of all the available site data in these models is challenging, and many cases impossible.

The traditional models also are typically underestimation contamination impacts and overestimating effects on the remdeication activities. This is cause by the way governing processes are represented in numerical geochemical models. As a result, the obtained model predictions can be erroneous and derails projects from success.

ChemML overcomes these limitations.

ChemML will be deployed as a Software-as-a-Service on the cloud. Tiered licensing and customer support options will be available.API's to public and proprietary datasets will be provided.ChemML frontend will include data management and visualization tools. The backend will provide access to cloud-computing resources for AI/ML analyses and interpretation.ChemML will include tools for sensitivity, uncertainty, and decision analyses.Near-real-time data assimilation and model updating will be available. Access to actionable information will be provided to the users through IoT devices and phone apps.

Please contact us for licensing information, commercial support, and consulting.