Catalysts: search a vast design space computationally, then test the best options. A*STAR and Imperial’s S$10 million programme will build a Surface Science Foundation Model and cloud platform to predict catalyst properties and synthesis conditions for cleaner fuels, CO₂-derived feedstocks and bio-based chemicals. The primary programme description states a target 500-fold acceleration in computational screening; claims of “nearly five times faster and cheaper” should therefore be treated cautiously unless they refer to a separate downstream development metric.

Trustworthy AI-generated code. NUS and Imperial are developing program-reasoning tools that automatically analyse, test, formally verify and prove software correctness, while using less-formal reasoning to interpret undocumented code. They will test the tools on consequential software such as network protocols and Linux-kernel components, with the aim of enabling AI agents to audit code written by other AI systems.

Biomedicine: integrate biological data rather than treat each layer separately. NUS and A*STAR’s MultiOmicsFM is planned as a foundation model that jointly interprets DNA, RNA and gene-activity data from Singapore’s multi-ethnic datasets, supporting disease-risk research and optimisation of mRNA therapies. Separately, the BloodCounts! project will train a DeepCBC model on the full raw parameters of complete blood-count tests plus blood-smear images, to extract more diagnostic value from a common test and support prediction of conditions such as stroke and cancer risk.

Food and climate resilience. NUS and Illinois ARCS will build knowledge-guided agricultural digital twins—virtual representations of Southeast Asian farmland that combine observed data with crop-science principles. The intended outputs are interpretable forecasts and decision tools for planting, resource use and supply chains, rather than opaque predictions alone.

Other discovery domains. The eight-project portfolio also includes AI-designed interfaces to improve energy transport, alongside the materials, catalysis, genomics, software, agriculture and blood-analysis efforts. Together, these projects span materials, computing, life science, energy and agriculture rather than treating AI4S as a single-sector initiative.

Catalysts: search a vast design space computationally, then test the best options. A*STAR and Imperial’s S$10 million programme will build a Surface Science Foundation Model and cloud platform to predict catalyst properties and synthesis conditions for cleaner fuels, CO₂-derived feedstocks and bio-based chemicals. The primary programme description states a target 500-fold acceleration in computational screening; claims of “nearly five times faster and cheaper” should therefore be treated cautiously unless they refer to a separate downstream development metric.

Trustworthy AI-generated code. NUS and Imperial are developing program-reasoning tools that automatically analyse, test, formally verify and prove software correctness, while using less-formal reasoning to interpret undocumented code. They will test the tools on consequential software such as network protocols and Linux-kernel components, with the aim of enabling AI agents to audit code written by other AI systems.

Biomedicine: integrate biological data rather than treat each layer separately. NUS and A*STAR’s MultiOmicsFM is planned as a foundation model that jointly interprets DNA, RNA and gene-activity data from Singapore’s multi-ethnic datasets, supporting disease-risk research and optimisation of mRNA therapies. Separately, the BloodCounts! project will train a DeepCBC model on the full raw parameters of complete blood-count tests plus blood-smear images, to extract more diagnostic value from a common test and support prediction of conditions such as stroke and cancer risk.

Food and climate resilience. NUS and Illinois ARCS will build knowledge-guided agricultural digital twins—virtual representations of Southeast Asian farmland that combine observed data with crop-science principles. The intended outputs are interpretable forecasts and decision tools for planting, resource use and supply chains, rather than opaque predictions alone.

Other discovery domains. The eight-project portfolio also includes AI-designed interfaces to improve energy transport, alongside the materials, catalysis, genomics, software, agriculture and blood-analysis efforts. Together, these projects span materials, computing, life science, energy and agriculture rather than treating AI4S as a single-sector initiative.