Are you looking for a more affordable approach to predicting properties used for your process design needs? Join the Design Institute for Physical Properties (DIPPR) for a webinar to discover how DIPPR continues to transform the prediction of thermophysical properties using cutting-edge machine learning and molecular simulation techniques and integrates these into its systems-level critical evaluation process of all available experimental data for compounds.
Key Takeaways:
•How DIPPR evaluates and recommends property data
•Why improved prediction methods matter
•Best practices for developing ML models
•Real-world examples of ML in action
Webinar content is available with the kind permission of the author(s) solely for the purpose of furthering AIChE’s mission to educate, inform and improve the practice of professional chemical engineering. The content reflects the views, opinions, and recommendations of the presenters. AIChE does not warrant or represent, expressly or by implication, the correctness or accuracy of the content of the information presented. All other uses are forbidden without the express consent of the author(s). For permission to re-use, please contact chemepermissions@aiche.org. Attendee contact information, including email addresses, will be shared with AIChE, with the option to unsubscribe from future communications.
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