Computing a greener tomorrow

This month’s Focus issue highlights ongoing work by computational scientists to help address the Sustainable Development Goals, as well as discusses how the sustainability of computational science itself can be improved.

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.

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.

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.

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.

The 2021 Nobel Prize in Physics recognized the importance of climate modeling and its role in explaining anthropogenic effects on climate change and global warming. To further understand our Earth’s climates, computational models pose new challenges to account for various complexities.

A deep generative model using radar observations is used to create skilful precipitation predictions that are accurate and support real-world utility.

A statistical forecast model using a deep-learning approach produces useful forecasts of El Niño/Southern Oscillation events with lead times of up to one and a half years.

A forward-synthesis platform, Allchemy, computationally determines how to ‘close the circle’, or use waste chemicals to make valuable pharmaceutical or agrochemical products, ranking possible routes by environmental, geospatial, and other factors.

A closed-loop machine learning methodology of optimizing fast-charging protocols for lithium-ion batteries can identify high-lifetime charging protocols accurately and efficiently, considerably reducing the experimental time compared to simpler approaches.

Current global climate models struggle to represent precipitation and related extreme events, with serious implications for the physical evidence base to support climate actions. A leap to kilometre-scale models could overcome this shortcoming but requires collaboration on an unprecedented scale.

The rapid growth of artificial intelligence (AI) is reshaping our society in many ways, and climate change is no exception. This Perspective presents a framework to assess how AI affects GHG emissions and proposes approaches to align the technology with climate change mitigation.

Rapid growth of AI could lead to more inventions and innovations in climate actions, yet evidence of this connection is lacking. The use of large-scale patent data and automated techniques helps elucidate trends in climate-related artificial intelligence inventions for different technology areas.

The authors conduct a national inventory on individual tree carbon stocks in Rwanda using aerial imagery and deep learning. Most mapped trees are located in farmlands; new methods allow partitioning to any landscape categories, effective planning and optimization of carbon sequestration and the economic benefits of trees.

Development and planning for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) has been years in the making. Nature Climate Change speaks to the Chair of the CMIP Panel, Veronika Eyring, about the aims and projected outcomes of the project.

Accurately predicting battery lifetime is difficult, and a prediction often cannot be made unless a battery has already degraded significantly. Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.

Large-scale projects have become increasingly important in physics. They are also a source of greenhouse gas emissions. Clarisse Aujoux, Odile Blanchard and Kumiko Kotera describe how to use transparent, open data to estimate these emissions — the first step in taking effective action to reduce them.

Reducing resource usage will improve the environmental impact of high-performance computing — but doing so can clash with the science goals of funders. Computational physicist Peter Skands explains how he approached the conflict.

Data analysis relies heavily on computation, and algorithms have grown more demanding in terms of hardware and energy. Monitoring their environmental impacts is and will continue to be an essential part of sustainable research. Here, we provide guidance on how to do so accurately and with limited overheads.

Machine learning has been used to represent small-scale processes, such as clouds, in atmospheric models but this can lead to instability in simulations of climate. Here, the authors demonstrate a use of machine learning in an atmospheric model that leads to stable simulations of climate at a range of grid spacings.

Photon-induced charge separation phenomena are at the heart of light-harvesting applications but challenging to be described by quantum mechanical models. Here the authors illustrate the potential of machine-learning approaches towards understanding the fundamental processes governing electronic excitations.

Computational sustainability harnesses computing and artificial intelligence for human well-being and the protection of our planet. Materials science is central to many sustainability challenges. Exploiting synergies between computational sustainability and materials science advances both fields, furthering the ultimate goal of establishing a sustainable future.

Machine learning using big data can enhance environmental law monitoring. Applied to the US Clean Water Act, such methods can help public agencies to increase the likelihood of inspecting non-compliant facilities up to sevenfold.

A reliable charging infrastructure is critical to wider adoption of electric cars. With large-scale social data and machine intelligence, this study shows the importance of the quality, not just the quantity, of charging stations to consumers, suggesting policy design should include consumer data.

Artificial intelligence methods can help biodiversity conservation planning in a rapidly evolving world. A framework based on reinforcement learning quantifies the trade-off between the costs and benefits of area and biodiversity protection and achieves better solutions with empirical data than alternative methods.

Diversified renewable energy sources can enable the sustainable operation of multisector resource systems. An artificial intelligence-assisted multi-objective design framework, applied in Ghana, explores optimized management and investment strategies balancing hydropower, bioenergy, solar and wind energies, and their impacts.

Machine learning and satellite images are used to identify intensive animal agricultural facilities in the United States, which are otherwise difficult to track. This can facilitate monitoring their compliance with environmental law.

An artificial intelligence-based method may infill gaps in historical temperature data more effectively than conventional techniques. Application of this method reveals a stronger global warming trend between 1850 and 2018 than estimated previously.

Making large datasets findable, accessible, interoperable and reusable could accelerate technology development. Now, Jacobsson et al. present an approach to build an open-access database and analysis tool for perovskite solar cells.

To meet climate goals, electric utilities should be decarbonizing their power production, but historical analyses of this process are scarce. Using machine learning and data from more than 3,000 utilities globally, Galina Alova shows that even utilities that prioritize renewable energy continue to grow their fossil fuelled generation capacity.