An indomitable particle: Colloidal silica takes on extreme environments

Machine learning for adaptive experimental design – Reducing experimental time and costs by 50-80%

Brought to you by Intellegens

Overview

Find out how machine learning is empowering a new approach to Design of Experiments (DOE) that delivers better results with 50-80% fewer experiments than conventional methods. You can quickly find the optimal composition, chemistry, or processing parameters to achieve commercial performance goals. Answering such questions is key to the design of formulations, chemicals, materials, and biopharmaceuticals.

You will learn how machine learning enables adaptive experimental design, which focuses effort on those routes most likely to be successful. Since experimental costs associated with a typical industrial R&D project run to hundreds of thousands of dollars, the resulting reductions in workload deliver a significant return on investment.

Key Objectives:

Understand limitations of conventional DOE
See how machine learning enables an adaptive experimental design approach
Read case study examples that demonstrate typical reductions in experimental workload of 50-80%
Find out how you can implement this approach in your experimental programs

Brought to you by:

This content was created by Intellegens, without editorial input from the C&EN Media Group. For more information on C&EN's custom products, visit our C&EN Media Kit.

Please complete the form to download the white paper.

Forwarded this link? Clear the form

Email Address*

Salutation*

First Name*

Last Name*

Company*

Job Title

Address 1*

City*

State*

Zip / Post Code*

Country/Territory/Area*

Business Phone*

How do you see the role of design of experiments (DoE) in enhancing these processes?*

Can you describe how your organization currently approaches experimentation and decision-making?*

I would like to receive C&EN’s weekly newsletter with the latest in chemistry news

I would like to receive more information on products and services from the American Chemical Society

address2

*By submitting this form, you agree to receive more information on related products and services from the American Chemical Society and its sponsor via email. You understand that you can withdraw your consent at any time. ACS takes your privacy seriously. For more information, please see the ACS Privacy Policy.