Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
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.
The rapidly growing demand to share data more openly creates a need for secure and privacy-preserving sharing technologies. However, there are multiple challenges associated with the development of a universal privacy-preserving data sharing mechanism, and existing solutions still fall short of their promises.
