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Wildfires have increased in frequency and intensity due to climate change and have had severe impacts on the built environment worldwide. Moving forward, models should take inspiration from epidemic network modeling to predict damage to individual buildings and understand the impact of different mitigations on the community vulnerability in a network setting.

Digital twins hold immense promise in accelerating scientific discovery, but the publicity currently outweighs the evidence base of success. We summarize key research opportunities in the computational sciences to enable digital twin technologies, as identified by a recent National Academies of Sciences, Engineering, and Medicine consensus study report.

Digital twins of Earth have the capability to offer versatile access to detailed information on our changing world, helping societies to adapt to climate change and to manage the effects of local impacts, globally. Nevertheless, human interaction with digital twins requires advances in computational science, particularly where complex geophysical data is turned into information to support decision making.

Urban digital twins hold immense promise as live computational models of cities, synthesizing diverse knowledge, streaming data, and supporting decisions towards more inclusive planning and policy. The size, heterogeneity, and open-ended character of cities, however, pose many difficult questions, at the frontiers of what is currently possible in computational science. Overcoming these challenges provides pathways for fundamental progress in the field and a proving ground for its economic value and social relevance.

We highlight the challenges and opportunities in organic redox flow battery research, underscoring the need for collaborative research efforts. The synergy between computation and experimentation holds the potential to expedite progress in this field and can have far-reaching impacts beyond energy storage applications.

Machine learning interatomic potentials (MLIPs) enable materials simulations at extended length and time scales with near-ab initio accuracy. They have broad applications in the study and design of materials. Here, we discuss recent advances, challenges, and the outlook for MLIPs.

The electrocatalytic nitrogen reduction reaction is a promising alternative to the Haber–Bosch process. However, the reproducibility and reliability of this process suffer from the persistence of false positives. Computational tools have the potential to alleviate this issue but several challenges must be addressed.

Artificial intelligence (AI) drives innovation across society, economies and science. We argue for the importance of building AI technology according to open-source principles to foster accessibility, collaboration, responsibility and interoperability.

Progress towards universal access to safe drinking water and nutritious food has been moving forward at a slower than desired rate. Computational tools can help accelerate progress towards these goals, but solutions need to be open source, and designed, developed and implemented in a participatory manner.

Rapid urban expansion presents a major challenge to delivering the United Nations Sustainable Development Goals. Urban populations are forecast to increase by 2.2 billion by 2050, and business as usual will condemn many of these new citizens to lives dominated by disaster risk. This need not be the case. Computational science can help urban planners and decision-makers to turn this threat into a time-limited opportunity to reduce disaster risk for hundreds of millions of people.

Social media and other internet platforms are making it even harder for researchers to investigate their effects on society. One way forward is user-sourced data collection of data to be shared among many researchers, using robust ethics tools to protect the interests of research participants and society.

The prediction of stable crystal structures is an important part of designing solid-state crystalline materials with desired properties. Recent advances in structural feature representations and generative neural networks promise the ability to efficiently create new stable structures to use for inverse design and to search for materials with tailored functionalities.

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.

As artificial intelligence (AI) proliferates, synthetic chemistry stands to benefit from its progress. Despite hidden variables and ‘unknown unknowns’ in datasets that may impede the realization of a digital twin for the laboratory flask, there are many opportunities to leverage AI and large datasets to advance synthesis science.

Nonadiabatic molecular dynamics is the method of choice for modeling a wide range of excited-state phenomena. Although much progress has been made in improving the usability and efficiency of ground-state calculations, there are still challenges in translating this advance to the excited state.

The focus on quantum materials has raised questions on the fitness of density functional theory for the description of the basic physics of such strongly correlated systems. Recent studies point to another possibility: the perceived limitations are often not a failure of the density functional theory per se, but rather a failure to break symmetry.

CRISPR has revolutionized biomedical and bioengineering research as a programmable genome engineering technology. Computational tools have been integral throughout the discovery of CRISPR biology and the development of CRISPR-based technologies that hold promise to solve many challenges ahead.

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.

Gender inequality has been the unspoken truth, rampant for centuries. Although a deep-rooted cultural mindset, the inequality has reverse-translated from society into the way we study and practice science, and more currently, into the computational modeling world.

Normative modeling is considered one of the most promising avenues towards personalized medicine. The integration of multimodal, mechanistic and lifespan modeling will play an essential role, but significant challenges need to be overcome before this promise can be turned into reality.