News & Comment

We celebrate the fifth anniversary of Nature Computational Science and reflect on how we have engaged with the research community.

Digital twins are evolving into self-learning, autonomous systems that link models, data and human interaction. Realizing their full potential depends on interoperability, standardization and the integration of artificial intelligence and advanced computational reasoning across sectors.

As quantum mechanics marks its centennial, this issue of Nature Computational Science features a Focus that outlines the impact of quantum mechanics in advancing computing technologies, while discussing the challenges and opportunities that lie ahead.

Quantum machine learning is being actively explored to assess whether quantum resources can enhance learning and inference, yet major obstacles remain. Here, we discuss pressing challenges and outline potential pathways toward future practical applications.

Data-center operators try to recycle retired hardware, but a broken global recycling infrastructure stands in the way.

We provide recommendations on how to write an effective point-by-point response document.

Technical metrics used to evaluate medical artificial intelligence tools often fail to predict their clinical impact. We characterize this discordance and propose a framework of study designs to guide the translational process for clinical artificial intelligence tools, acknowledging their diversity and specific validation requirements.

In order to realize the potential of mental health AI applications to deliver improved care, a multipronged approach is needed, including representative AI datasets, research practices that reflect and anticipate potential sources of bias, stakeholder engagement, and equitable design practices.

Nature Computational Science presents a Focus that explores the field of computational psychiatry and its key challenges, from privacy concerns to the ethical use of artificial intelligence, offering new insights into the future of mental health care.

Computational psychiatry is increasingly delivering causal evidence by focusing on interventions research and clinical trials. Causal evidence could improve patient outcomes through improved precision, repurposing, novel interventions, scaling of psychotherapy and better translation to the clinic.

Self-driving laboratories that integrate robotic production with artificial intelligence have the potential to accelerate innovation in biotechnology. Because self-driving labs can be complex and not universally applicable, it is useful to consider their suitable use cases for successful integration into discovery workflows. Here, we review strategies for assessing the suitability of self-driving labs for biochemical design problems.

This issue of Nature Computational Science features a Focus that highlights both the promises and perils of large language models, their emerging applications across diverse scientific domains, and the opportunities to overcome the challenges that lie ahead.

Strong barriers remain between neuromorphic engineering and machine learning, especially with regard to recent large language models (LLMs) and transformers. This Comment makes the case that neuromorphic engineering may hold the keys to more efficient inference with transformer-like models.

Large language models (LLMs) are already transforming the study of individual cognition, but their application to studying collective cognition has been underexplored. We lay out how LLMs may be able to address the complexity that has hindered the study of collectives and raise possible risks that warrant new methods.

While leading tech companies race to build ever-larger models, researchers in Brazil, India and Africa are using clever tricks to remix big labs’ LLMs to bring AI to billions of users.

We reflect on ongoing topics and discussions at conferences in the field of computational chemistry.

The adoption of generative artificial intelligence (AI) code assistants in scientific software development is promising, but user studies across an array of programming contexts suggest that programmers are at risk of over-reliance on these tools, leading them to accept undetected errors in generated code. Scientific software may be particularly vulnerable to such errors because most research code is untested and scientists are undertrained in software development skills. This Comment outlines the factors that place scientific code at risk and suggests directions for research groups, educators, publishers and funders to counter these liabilities.