Description
Scientists and policymakers have learned how to make long-term strategic investments in singular physical infrastructures. Particle accelerators, fusion reactors and space telescopes are conceived, built and operated through coordinated programmes spanning decades. Such investments are essential to scientific health, whether they are curiosity-driven or their societal returns lie far in the future.
We have not been systematic in another class of research infrastructures: the shared computational capabilities on which much of modern science depends. I will focus on electronic-structure theory and simulation, whose scientific reach is unusually broad and measurable. In Nature’s 2014 census of the most-cited papers ever, 12 of the top 100 across all fields of science, medicine, and engineering were on electronic-structure simulations, including two of the top ten; in the 2025 update, one more paper moved in the top ten. Citation counts are not measures of societal value, but they provide striking evidence of the field’s pervasive and enabling role.
Electronic-structure methods combine theoretical depth with societal impact. They empower materials discovery and characterisation across academia, national laboratories and industry, and provide data, physical priors and validation for AI-enabled science. Computational tools, data and simulation were central to the 2011 US Materials Genome Initiative and are embedded in the 2025 Genesis Mission.
Their economic model differs fundamentally from that of a physical facility. Open software, curated data and shared workflows can be replicated worldwide at the flick of a switch, with great synergies for everyone. Yet creating, validating, maintaining and extending these capabilities requires sustained human expertise. The underfunded infrastructure is therefore not hardware, but people: the physics, software, and data scientists working on purposes and timescales broader than a conventional grant.
The UK has developed notable exceptions, but worldwide support for the development of scientific software and the theories that underpin it, for the production and curation of data, and for verification-and-validation efforts remains fragmented, project-based and short-lived, despite its genuinely modest costs and truly exceptional leverage and impact. My simple and simply bewildering question is: why?