W e are moving from an era of digital analysis to one of physical execution, as AI shifts from the operational dashboard into the design, control and composition of infrastructure. Driving that transition is the emergence of physical AI and more deeply, physics-infused AI where machine learning is combined with physics, chemistry and materials science to alter not just how infrastructure is optimised, but the physical assumptions on which it is designed.¹ Leaders across the technology and scientific landscape are already pointing in this direction. Jensen Huang, CEO of NVIDIA, has described the next frontier as AI that understands the laws of physics,² while Demis Hassabis, CEO of Google DeepMind, has positioned AI as a driver of scientific discovery.³ The value of AI is no longer confined to what it can generate on a screen, but what it can alter in the world. In the energy sector, these approaches are being used to design entirely new polymeric materials for hydrogen fuel cell membranes, computationally generating and screening thousands of candidate structures for proton exchange and gas separation performance beyond what existing materials can achieve. In semiconductors, machine learning–based interaction models are being used to simulate atomic-level diffusion behavior in next-generation lithography systems, compressing materials design cycles from months to weeks and producing atom-level insights that guide the creation of new chamber materials. These are not simply efficiency gains. They represent a shift toward invention, a signal that the frontier of AI is moving from the digital layer into the molecular one. The shift from optimisation to discovery For decades, progress in industrial chemistry and materials science has been constrained by the pace of physical testing. New materials and chemical pathways had to be discovered through slow, iterative laboratory work. That is beginning to change. Hybrid approaches that combine AI with physics-based simulation now allow researchers to explore possible designs computationally at speeds and levels of accuracy previously out of reach.
Google DeepMind’s GNoME system, for instance, identified millions of potential new crystal structures and offers a glimpse of how computational discovery can compress decades of materials research into scalable, model-driven workflows. This does not eliminate the need for testing, but it sharply narrows the search space for next-generation batteries, semiconductors and other high-performance materials.⁴ In energy storage, AI-driven digital twins are enabling the design of entirely new solid electrode materials for next- generation batteries, generating and evaluating candidate structures at a scale no conventional R&D process could match. In water treatment, the same approach is being applied to design novel sorbent materials for PFAS capture and removal — simulating thousands of candidate molecular structures for binding affinity rather than testing them sequentially in a laboratory.⁵ In each case, the starting point is not an existing material to be improved, but a performance requirement to be met. AI is working backwards through chemistry and physics to find structures that have never been synthesised before. Structural impacts on CAPEX and OPEX For those who design, finance and operate physical assets, this is an economic story as much as a technology one. Consider the energy sector. Industrial processes — from hydrogen production to sustainable fuels — remain constrained by the capital and operating costs of extreme heat, pressure and expensive catalysts. If AI can design catalysts that allow reactions to occur at lower temperatures with higher yields or under less extreme conditions, the effect is not marginal. It changes plant design, equipment requirements and in some cases, the commercial logic of the process itself.⁶ This marks a shift from optimising systems we inherited to engineering systems we can deliberately redesign. Over time, this could reshape supply chains as well, as raw inputs are redesigned for efficiency, availability and local suitability rather than remaining tied to the legacy chemistry of the last century. In that sense, the boundary of what is economically viable begins to move with the science.
Nexus Magazine | GHD | 5
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