Reviews & Analysis

Multi-messenger astronomy offers promises for exploring Universe events in distance. Nevertheless, there are numerous computational challenges when analyzing the massive heterogeneous messenger data from various detectors, creating research opportunities to the community, such as developing multimodal machine learning.

Quantum embedding theory promises the simulation of realistic materials in quantum computers. In this Perspective, challenges and opportunities of applying different embedding frameworks to calculate solid materials properties are discussed, with a focus on electronic structures of spin defects.

Machine learning has been used to accelerate the simulation of fluid dynamics. However, despite the recent developments in this field, there are still challenges to be addressed by the community, a fact that creates research opportunities.

There is still a wide variety of challenges that restrict the rapid growth of neuromorphic algorithmic and application development. Addressing these challenges is essential for the research community to be able to effectively use neuromorphic computers in the future.

Multi-omics studies have been increasingly used to better understand biological samples and infer molecular interactions. Nevertheless, a number of challenges must still be addressed to take full advantage of multi-omics data and to avoid reaching potentially incorrect conclusions.

Development in digital-twin technology has been rapidly growing across a range of industries and disciplines. However, to ensure a wider and more robust adoption of such technology, various challenges must be addressed by the computational science community.

The field of biomolecular modeling has thrived by exploiting state-of-the-art technological advances. In this Perspective, the role of software and hardware advances, and the disparity and synergy between knowledge-based and physics-based methods are discussed and explored.

Quantum computing has the potential to assist with myriad tasks in science. In this Perspective, the applicability and promising directions of quantum computing in computational biology, genetics and bioinformatics is evaluated and discussed.

There have been substantial developments in weather and climate prediction over the past few decades, attributable to advances in computational science. The rise of new technologies poses challenges to these developments, but also brings opportunities for new progress in the field.

While estimating causality from observational data is challenging, quasi-experiments provide causal inference methods with plausible assumptions that can be practical to a range of real-world problems.