Volume 4 Issue 3, March 2024

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.

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.

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.

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.

Determining what guest can effectively bind in a host, or the reverse, is a central challenge in chemistry. To address this, an electron-density-based transformer method of generating and optimizing host–guest binders is proposed, applied to two different host systems and validated by experiment.

A neural network-based method for advancing orbital-free density functional theory (OFDFT) is developed, which reaches DFT accuracy and maintains lower cost complexity.

A recent study introduces a machine learning approach to investigate the effects of mutations on protein sensors commonly employed in fluorescence microscopy, thus enabling the discovery of high-performance sensors.

We present SCORPION, a computational tool to model gene regulatory networks based on single-cell transcriptomic data and prior knowledge of gene regulation. SCORPION networks can be modeled for specific cell types in individual samples, and are therefore suitable for conducting comparisons between experimental groups.

The application of digital twins in industry has become increasingly common, but not without important challenges to be addressed by the research community.

While there is a clear opportunity for digital twins to bring value in mechanical and aerospace engineering, they must be considered as an asset in their own right so that their full potential can be realized.

The digital twin concept, while initially formulated and developed in industry and engineering, has compelling potential applications in medicine. There are, however, major challenges that need to be overcome to fully embrace digital twin technology in the medical context.

Although digital twins first originated as models of physical systems, they are rapidly being applied to social systems, such as cities. This Perspective discusses the development and use of digital twins for urban planning.

An optimization algorithm is used to discover guest molecules based on knowing only the structure of the host. The molecules are represented as 3D volumes, optimized to improve host–guest interaction and converted into SMILES using a transformer model.

M-OFDFT is a deep learning implementation of orbital-free density functional theory (OFDFT) that achieves DFT-level accuracy on molecular systems with lower cost complexity, and can extrapolate to much larger molecules than those seen during training.

Andre Berndt and colleagues introduce a machine learning approach to enhance the biophysical characteristics of genetically encoded fluorescent indicators, deriving and testing in vitro new GCaMP mutations that surpass the performance of existing fast GCaMP indicators.

SCORPION is an algorithm to model gene regulatory networks based on single-cell data. The authors show that SCORPION outperforms other methods, accurately detects transcription factor activity and can potentially help with the discovery of disease markers.