Perspectives in 2023

While the adherence to fairness constraints has become common practice in the design of algorithms across many contexts, a more holistic approach should be taken to avoid inflicting additional burdens on individuals in all groups, including those in marginalized communities.

The field of human mobility has evolved drastically in the past 20 years. In this Perspective, the authors discuss three key areas in human mobility, framed as minds, societies and algorithms, where they expect to see substantial improvements in the future.

Although the number of quantum chemical studies on atmospheric cluster formation continue to rise, data-driven approaches can greatly expand the number of chemically relevant systems that can be covered and increase our understanding of the aerosol particle formation process.

The carbon footprint of computational sciences is substantial, but there is an immense opportunity to lead the way towards sustainable research. In this Perspective the authors lay some fundamental principles to transform computational science into an exemplar of broad societal impact and sustainability.

Addressing plastic pollution in the environment is difficult due to the high complexity and diversity of physical and chemical properties. This Perspective argues that process-based mass-balance models could provide a viable solution for evaluating environmental exposure to plastic pollution.

Artificial photosynthesis has the potential to capture and store solar energy in the form of chemical bonds. Computational approaches provide useful guidelines for the experimental design of photosynthetic devices, but to make this possible, many challenges must be overcome.

While digital twins have been recently used to represent cities and their physical structures, integrating complexity science into the digital twin approach will be key to deliver more explicable and trustworthy models and results.

Proton-coupled electron transfer occurs at a variety of length and time scales and often in complex environments. This Perspective summarizes a range of modeling strategies that can be used together to address remaining challenges and provide a better understanding of such reactions.

Machine learning models have been widely applied to boost the computational efficiency of searching vast chemical space of compositionally complex materials. This Perspective summarizes the recent developments and proposes future opportunities, such as the physics-informed machine learning models.

Complex materials offer promises for exotic materials properties that enable novel applications. Nevertheless, there are numerous computational challenges for a rational design of defects in such materials, thus inspiring opportunities for developing advanced defect models.

Chemical reaction networks are widely used to examine the behavior of chemical systems. While diverse strategies exist for chemical reaction network construction and analysis for a wide range of scientific goals, data-driven and machine learning methods must continue to capture increasingly complex phenomena to overcome existing unmet challenges.