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Human mobility research intersects with various disciplines, with profound implications for urban planning, transportation engineering, public health, disaster management, and economic analysis. Here, we discuss the urgent need for open and standardized datasets in the field, including current challenges and lessons from other computational science domains, and propose collaborative efforts to enhance the validity and reproducibility of human mobility research.
Software is much more than just code. It is time to confront the complexity of licenses, uses, governance, infrastructure and other facets of software in science. Their influence is ubiquitous yet overlooked.
As machine learning models are becoming mainstream tools for molecular and materials research, there is an urgent need to improve the nature, quality, and accessibility of atomistic data. In turn, there are opportunities for a new generation of generally applicable datasets and distillable models.
Morphing soft matter, which is capable of changing its shape and function in response to stimuli, has wide-ranging applications in robotics, medicine and biology. Recently, computational models have accelerated its development. Here, we highlight advances and challenges in developing computational techniques, and explore the potential applications enabled by such models.
Dr Kelly Ruggles, associate professor at New York University Langone Health, discusses with Nature Computational Science how she uses computational approaches to gain insights into cancer, inflammation and cardiovascular disease, as well as the importance of mentorship.
We highlight the vibrant discussions on quantum computing and quantum algorithms that took place at the 2024 American Physical Society March Meeting and invite submissions that notably drive the field of quantum information science forward.
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.
This issue of Nature Computational Science includes a Focus that highlights recent advancements, challenges, and opportunities in the development and use of digital twins across different domains.
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.
Dr Zhimei Sun – professor of Materials Science and Engineering at Beihang University – talks to Nature Computational Science about her career trajectory, her research on computational materials science and materials informatics, as well as her advice to young women scientists in these fields.
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.