Volume 3 Issue 6, June 2023

Proponents often tout quantum computing as a more energy efficient alternative to classical computing methods. However, the extent to which it can reduce energy usage remains unclear, as experts have not yet agreed on metrics to determine its energy consumption.

Accelerating climate action requires harnessing the power of decision-support tools in new ways. This vision cannot be realized without interdisciplinary computational scientists that are capable of integrating knowledge from the environmental, social and cognitive sciences.

Dr Y. Shirley Meng, Professor of Molecular Engineering at the University of Chicago and Chief Scientist at the Argonne Collaborative Center for Energy Storage Science (ACCESS), discusses her research on energy storage materials and the importance of multidisciplinary collaborations.

Dr Alexandre Caldas, a Director at the United Nations (UN) as Chief of Country Outreach, Technology and Innovation in the Science Division at the United Nations Environment Programme (UNEP) and Chair of the United Nations Geospatial Network across 40 agencies of the UN, talks to Nature Computational Science about the importance of data availability, the Sustainable Development Goals, and evolving policy.

Dr Carla Gomes, Ronald C. and Antonia V. Nielsen Professor of Computing and Information Science, director of the Institute for Computational Sustainability, and co-director of the newly established AI for Science Institute at Cornell University, discusses with Nature Computational Science her research on sustainability and how we can address the world’s most pressing issues little by little.

Dr Michael Falk, professor of Materials Science and Engineering, Mechanical Engineering, and Physics at Johns Hopkins University, talks to Nature Computational Science about his academic training at the intersection of physics and computer science, his research in condensed-matter physics, as well as his experience in improving diversity and inclusion in the physics research community.

A momentum-space algorithm is proposed to simulate electron dynamics with time-dependent density functional theory, which expands the scope of conventional real-space methods.

By conducting single-cell meta-analyses of inflammatory bowel disease, we identify rare or less-characterized cell subtypes linked to GWAS risk genes and therapeutic targets and dissect the commonalities and differences between ulcerative colitis and Crohn’s disease. Consequently, we present an interactive and user-friendly platform for the research community.

Increasing the number of parameters in a quantum neural network leads to a computational ‘phase transition’, beyond which training the network becomes significantly easier. An algebraic theory has been developed for this overparametrization phenomenon and predicts its onset above a certain parameter threshold.

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.

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.

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.

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.

A platform for single-cell meta-analysis of inflammatory bowel disease, named scIBD, enables identification of rare or less-characterized cell types and the dissection of the commonalities and differences between ulcerative colitis and Crohn’s disease.

This study presents an ab initio approach for the real-time charge carrier quantum dynamics in the momentum space, which is computationally more efficient than conventional real-space non-adiabatic molecular dynamics method. The method is applied to study hot carrier dynamics in graphene, which provides insights about the phonon-specific relaxation mechanism.

A theoretical framework for quantum neural network (QNN) overparametrization, a phase transition in loss landscape complexity, is established. The precise characterization of the critical number of parameters offered is expected to impact QNN design.

GRAPE is a software resource for graph learning and embedding that is orders of magnitude faster than existing state-of-the-art libraries, making large-graph analysis feasible in a wide range of real-world applications.