NUS has secured 4 major projects underneath Singapore’s S$120 million AI-for-Science Initiative (AI4S), reinforcing its place as a world chief in AI-driven scientific research. This achievement underscores the University’s distinctive strengths in bridging superior AI capabilities with world-class experience throughout a number of scientific disciplines, similar to superior supplies, computing, genomics and agriculture.
On 16 June 2026, Singapore formally launched eight inaugural projects underneath AI4S, a landmark nationwide initiative spearheaded by the National Research Foundation to harness the facility of Artificial Intelligence (AI) to revolutionise scientific discovery. Announced by Professor Tan Chorh Chuan, Singapore’s Permanent Secretary (National Research and Development) on the AI4X Accelerate Conference 2026, these strategic research projects pair prime AI researchers with area specialists from main native and worldwide establishments to spur research innovation in areas of curiosity to Singapore. This bold effort goals to nurture a brand new technology of “bilingual” scientists, fluent in each AI and fields like life science, supplies science, and quantum science, in order to speed up the tempo of innovation.
Here, we define the 4 NUS projects which are among the many eight chosen underneath the AI4S initiative.
Materials Data Foundry: Accelerating Synthesis of Complex Materials for Future Applications
The Materials Data Foundry (MDF) is a joint challenge co-led by Professor Sir Konstantin Novoselov from the NUS Institute for Functional Intelligent Materials (I-FIM) and Professor Alán Aspuru-Guzik from the University of Toronto’s Acceleration Consortium to tackle the dearth of high-quality knowledge in supplies science. Using an open autonomous lab powered by AI and robotics, the MDF will create the world’s largest dataset linking synthesis protocols to real-world materials efficiency.
The lab will apply its platform to three testbeds: beyond-silicon and quantum-topological supplies, sturdy oxygen-evolution electrocatalysts and corrosion-resistant high-entropy alloy coatings. The challenge additionally contains industrial companions like Nvidia and VeChain to faucet on the cutting-edge digital options available on the market. The dataset developed will gas AI fashions to speed up the invention of recent supplies for electronics, clear power, and sustainable infrastructure, bridging the hole from thought to industrial use.
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AI for Program Reasoning
Co-led by Professor Abhik Roychoudhury from the Department of Computer Science in NUS School of Computing and Professor Cristian Cadar from the Imperial College London, and in collaboration with main specialists from the Singapore Management University, Massachusetts Institute of Technology, and ETH Zürich, this challenge addresses the pressing want to guarantee software program correctness and safety as AI-generated code turns into more and more prevalent.
The challenge will construct superior AI instruments to mechanically analyse, confirm, and show the correctness of pc applications to be certain that they’re secure, safe, and work as meant. Employing formal reasoning, which includes proving utilizing mathematical precision, and casual reasoning to perceive the behaviour of undocumented code, the challenge will check its instruments on essential methods like community protocols and elements of the Linux working system kernel. Ultimately, the objective is to create specialised AI brokers that may assist builders catch errors and reliably audit the huge quantities of code produced by different AIs.
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Accelerating Genomic Research with Artificial Intelligence: From Data to Discovery
A joint challenge led by Professor Cheng Ching-Yu from the NUS Yong Loo Lin School of Medicine and his collaborators at A*STAR Research Entities (ARES), this challenge addresses the problem of analysing huge and complicated genomic knowledge by growing MultiOmicsFM, a unified AI basis mannequin. Unlike present AI instruments that study DNA, RNA, and gene exercise in isolation, MultiOmicsFM can be designed to interpret them in unison, creating an built-in image of a person’s genetic make-up. By leveraging Singapore’s distinctive multi-ethnic genomic datasets, the challenge goals to expedite discoveries in illness danger prediction and mRNA remedy optimisation, positioning Singapore as a world chief in AI-driven precision drugs.
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KGAI4Ag: Advancing Knowledge-Guided AI to Develop Agricultural Digital Twins for Singapore’s Climate Resilience
Professor Roman Carrasco from the Department of Biological Sciences on the NUS Faculty of Science and his collaborators on the Illinois Advanced Research Center at Singapore Ltd. (Illinois ARCS) can be working collectively to deal with the specter of local weather change on Southeast Asia’s meals safety by constructing agricultural digital twins. These digital twins are digital replicas of farmland, powered by Knowledge-Guided AI (KGAI), which uniquely combines knowledge with established scientific ideas of crop development to create extra dependable and interpretable simulations. The platform will ship sensible forecasting and decision-support instruments to assist farmers and policymakers optimise planting methods, useful resource use, and provide chains, positioning Singapore as a regional hub for climate-resilient agricultural innovation.
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