Two More Nobel Laureates!

It may be a little irreverent, but I can't resist a joke — the biggest winner of this year's Nobel Prizes turns out to be Gaoshan Academy! 🤣

Among the 2025 Nobel laureates, three have taught us in the past three years — Professor Shimon Sakaguchi won the Nobel Prize in Physiology or Medicine, and Professors Susumu Kitagawa and Omar Yaghi shared the Nobel Prize in Chemistry.

What a magical organization this is.

Getting to meet these professors "before the prize" is a stroke of luck we owe to Gaoshan's academic eye and its ability to connect.

Here, listening to lectures by Nobel laureates has happened many times. Every one of these professors can explain the most cutting-edge science in a way that is deep yet accessible, letting us see how science truly changes the world.

Every time, I'm struck by it: masters really are different — how do they make such complex science clear enough even for me to understand?

But honestly — taking the class first and seeing the prize come later feels far better than simply attending a Nobel laureate's lecture! Because it means we witnessed, with our own eyes, "greatness in the making."

Even better, right after his lecture this April, I was certain: Professor Omar Yaghi would definitely win a Nobel Prize.

I just didn't expect this "prophecy" to come true so quickly.

Out of heartfelt pride, I'd like to try to capture the scientific spirit of Professor Yaghi's class for everyone who loves science.

Professor Yaghi's story: a love of molecules at first sight, for a lifetime

"When I was ten, I happened to open a book in the library with drawings of molecular structures — one glance, and it was forever. From then on, my life was devoted to molecules."

Background and original motivation: a Jordanian/Palestinian family, short of water since childhood; went to the U.S. alone at 15.

Pioneering a field: Reticular Chemistry — using strong bonds to stitch "molecular building blocks" into crystalline, programmable 2D/3D materials, with MOFs (containing metal nodes) and COFs (all-organic frameworks) as the classic examples.

The shock of MOF-5: a coin-sized sample, thanks to its enormous internal channels, can have a surface area on the scale of "a football field"; the pores act like "parking lots," allowing CO₂, H₂O, H₂ and so on to be adsorbed and released in an orderly way.

Harvesting water from air: materials represented by MOF-303 absorb water at night and release it during the day; according to data shared in class, 200 g of MOF can produce ~5 L of ultrapure water a day and can be cycled for 6–7 years; engineering designs from 2 kg/120 L per day up to 1 tonne/60,000 L per day have all had their concepts validated.

Carbon capture and scale-up: COFs/MOFs adsorb CO₂ with high selectivity through "channels like parking spaces," and have the potential for cyclic regeneration and scale-up.

The periodic table of materials, "reinvented": the "combinatorial universe" of different metal nodes and organic linkers is like a "periodic table of reticular chemistry," and every cell is worth a lifetime of exploration.

Molecular weaving: building "the toughness of cloth" into materials

In class, Yaghi used a brilliant analogy of "weaving cloth": when molecules "interlace like warp and weft" in angle and topology, the material becomes as tough as cloth, and can even be made into 3D interlocking structures that remain stable under stress. This is the power of "ordered networks" — once a structure is precisely designed, its properties can be precisely tuned.

AI × materials: from 2–3 years to 1 month

Integrating data and experiments: using literature mining + reaction image recognition + robotic synthesis + Bayesian optimization to digitize the traditional bottleneck of "crystallization conditions."

Doubling the discovery rate: on a dataset of 8,000+ ZIF reactions, AI raised the success rate by ~30%; across 2,688 experiments it achieved a discovery rate about 2x that of random exploration and faster topology classification.

The intelligent research group: 7 agents dividing up the work (planning, retrieval, modeling, robotics, 3D printing, safety, data analysis), compressing "a 35-year road" into "1 month."

"Two years ago I didn't believe it myself, until a student from Beijing said to me: why don't we try using AI? Now I believe it — AI will absolutely change our research."

My requirement: if you want to join us, you must know how to use AI.

Why people are slow to adopt AI: Lack of understanding: many people don't fully understand how artificial intelligence works or how it can benefit them, which leads to hesitation. Fear of job loss: AI is often associated with automation and replacing human jobs, which creates anxiety and resistance. High costs: implementing AI can require substantial investment in technology, infrastructure and technical staff — a barrier especially for small businesses. Data privacy concerns: AI systems rely heavily on data, and people are increasingly worried about how data is collected, used and protected. Ethics and bias concerns: people worry about AI making biased or unfair decisions, especially in sensitive areas such as hiring, healthcare or law enforcement. Regulatory uncertainty: with no clear rules or standards, some organizations wait before investing heavily in AI. Change management: adopting AI often means rethinking workflows and company culture, which can be difficult and time-consuming.

The future we can already see

Water from air: day-night cycles, low energy use, helping arid and off-grid regions achieve "water freedom."

Carbon capture and utilization: from cement/chemical exhaust to direct air capture (DAC); the adsorption–regeneration–scale-up path is clear.

Clean energy: significant gains in the safety and efficiency of storing hydrogen/methane/ammonia (high-density hydrogen storage at room temperature still awaits a key breakthrough).

Green separation: replacing "heating/distillation" with "channels–gating–interactions," cutting chemical-industry energy use "by big chunks."

Medicine and sensing: from low-invasiveness detection of proteins such as PSA to early warning of hazardous gases.

Circular materials: disassemblable and renewable, moving toward a new paradigm of "materials as a service."

Professor Yaghi's scientific spirit and concern for the world

1. Don't overthink it — DO THE EXPERIMENT, FOR GOD'S SAKE! There will always be people saying a direction won't work, that it can't be done. I'm rather contrarian — I don't accept that. Why not just try it! The people I want say: "This idea is fresh, I've never heard of it, let's try it" — not people who push back with every kind of excuse. DO THE EXPERIMENT FOR GOD'S SAKE❗️

2. If I were a billionaire, I would create an institution where everyone in the world could freely come together — not for their own country, but for all of humanity.

3. Every major discovery in my lab was made by Chinese students. Collaboration: at the company level it's still possible. Research: on the research side it's harder. Chinese students' creativity comes from being in a very diverse environment. Think about where I come from — I come from Jordan. That's a very different way of thinking.

Whatever we do, we must bring together people from different cultures and different ways of thinking!

4. AI for science — this is an era of witnessing transformation. It saves money and time, and makes students less frustrated — the frustration of experiments that don't work.

In closing: when disorder becomes order — from molecules to civilization, the thread running through all three masters: turning "disorder" into "order"

Ordering life science (Shimon Sakaguchi): the order of immune regulation in living systems determines the boundary between health and disease.

Ordering materials science (Yaghi & Kitagawa): making atoms/molecules designable and weavable like "warp and weft," so that a material's channels, degree of order and stability can be generated on demand, ultimately serving real-world challenges such as carbon capture, energy storage, harvesting water from air, precise separation and sensing.

This thread is not only a scientific method but also an organizational one: when we can structure disordered resources, relationships and information into systems that are "verifiable, reusable and scalable," civilization moves forward a small step.

Don't overthink it — go do the experiment! Once you take the first step, the road ahead will reveal itself.

— Written in Gaoshan Academy's "big Nobel year"

For every one of you who remains curious, humble and ready to act in the world.