Balancing Computational Chemistry's Green Potential With Its Environmental Impact

Abstract

Computational chemistry techniques offer tremendous potential for accelerating the discovery of sustainable chemical processes and reactions. However, the environmental impacts of the substantial computing power required for these digital methods are often overlooked. This study provides a comprehensive analysis of the carbon footprint associated with molecular simulations, machine learning, optimization algorithms, and the required data center and research activities within the field of digital chemistry. Successful applications of these methods tackling climate relate issues like CO2 conversion and storage are highlighted, contrasted with assessments of their environmental burden. Strategies to minimize the carbon emissions from computational efforts are evaluated, including sustainable data center practices, efficient code execution, reaction optimization, and sustainable research culture. Additionally, we surveyed tools and methodologies for tracking and reporting environmental impacts. Overall, guidelines and best practices are distilled for balancing the green potential of computational chemistry with responsible management of its environmental costs. Assessing and mitigating the field’s carbon footprint is crucial for ensuring digital chemical discoveries truly contribute to sustainability goals.