Data Science and Modeling for Green Chemistry
Award at a Glance
The Data Science and Modeling for Green Chemistry award aims to recognize the research and development of computational tools that guide the design of sustainable chemical processes and the execution of green chemistry that demonstrates compelling environmental, safety, and efficiency improvements over current technologies in the pharmaceutical industry and its allied industrial partners.
The award recognizes innovation or excellence in the research and development of computational tools empowering users to effectively design, implement, and evaluate green processes with reduced process mass intensity, waste, health and safety impact, and other aspirational improvements.
The award will be presented at the Green Chemistry & Engineering Conference. The recipient, or a member of the winning team, will be invited to share their technology in an oral presentation at this event. The recipient’s transportation, lodging, and registration fees for the conference are reimbursable up to $2,500 USD (additional funds available for international travel following ACS guidelines). The recipient or winning team will also receive a plaque recognizing the achievement and certificates will be given to each team member.
The computational invention can take the form of algorithms and/or software tools. All inventions that innovatively leverage machine learning/data science and other computational modeling techniques are in scope. Submissions should both highlight the technological breakthrough and show how the tool is specifically designed for end-users to drive towards greener processes (see selection criteria). Both academic and industrial research groups are eligible. Nominees do not have to be members of the American Chemical Society or the ACS GCIPR.
December 1, 2023, at 5:00 p.m. EST (GMT-5).
How to Apply
Applicants should submit through the ACS Green Chemistry Institute application portal. To use the portal, you will need to have or create a free ACS ID.
Please be prepared to submit the following information in the portal:
- Contact information of nominator
- Contact information of nominee(s)
- Title of green chemistry technology
- Focus area selection:
- Predictive tools for designing greener or safer reagents, processing conditions, or reaction outcomes
- AI platform technologies that have wide application across the pharmaceutical industry or have been used in the development of a drug on significant scale
- In silico approaches that minimize/reduce experimentation to arrive at superior reaction conditions
- Abstract (300 words) - Describe the technology, the problem it addresses, and its benefits regarding the Design Principles of Green Chemistry and engineering. Include the degree of implementation and transferability to manufacturing. Also, include any quantitative benefits such as the (potential) amounts of hazardous substances eliminated, energy saved, carbon dioxide emissions eliminated, and water saved.
- Detailed description (max 3 pages) - The judges will evaluate the problem, chemistry, and realized or potential benefits.
- Problem. Describe the challenges that existed prior to application of the new technology.
- Chemistry. Describe the scientific merits of the new computer-assisted technology, emphasizing novelty, scientific merit and application to solve a challenging problem.
- Potential or realized benefits. Detail the benefits to human health and environment by evaluation of your technology against the Design Principles of Green Chemistry, such as reduced toxicity of process materials, more reactive or sustainable catalyst design, minimization of experiments, reduction to PMI, optimization of yield/selectivity or similar benefit.
- Innovation and Novelty: The tool should demonstrate a high degree of innovation in implementing computational and modeling techniques for designing environmentally friendly processes in pharmaceutical manufacturing.
- Environmental Impact: The tool should effectively address and mitigate environmental impacts associated with pharmaceutical production, such as reducing waste generation, carbon emissions, water usage, and energy consumption.
- Efficiency and Cost-Effectiveness: The tool should provide resource-efficient solutions, thereby reducing costs associated with green manufacturing practices. It should demonstrate the ability to improve process efficiency and optimize resource utilization.
- Safety and Toxicity Prediction: The tool should incorporate accurate predictive models to assess the safety and toxicity profiles of chemical reactions, helping in the identification and design of less hazardous or non-toxic compounds.
- Versatility and Applicability: The tool should be applicable to a wide range of chemical reactions or processes within pharmaceutical manufacturing, ensuring its versatility for various green chemistry challenges.
- Integration with Other Tools/Software: The tool should have the potential to seamlessly integrate with other in silico tools or software commonly used in pharmaceutical manufacturing, enhancing its overall utility and interoperability.
- User-Friendly and Intuitive Interface: The tool should have a user-friendly interface, ensuring ease-of-use and accessibility to a broader audience, including researchers, process chemists, and other stakeholders.
- Validation and Reliability: The tool should have a strong validation framework, demonstrating reliable and accurate results through comparison with experimental data or benchmark studies.
- Openness and Availability: The tool should have an open-source or commercially available license that allows easy access and promotes collaboration, thus benefiting the broader scientific community.
- Proven Impact and Success Stories: The tool should showcase successful case studies or testimonials that highlight its positive impact on reducing the environmental footprint and improving sustainability in pharmaceutical manufacturing.