This could be the first true look at a new methodology for just about everything in physical science. At a speed of femtoseconds, the actual movement of electrons in chemical processes creating new chemical bonds.
Stanford’s Linac Coherent Light Source (LCLS) ”takes X-ray snapshots of atoms and molecules at work, providing atomic resolution detail on ultrafast timescales to reveal fundamental processes in materials, technology and living things.”
If that rather dry description seems a little vague, the translation is “a movie of how just about everything happens”.
Living things? Does that sound useful?
This process also instantly overturned computer simulations and has added levels of complexity to predictive models.
That’s just the start. A blow-by-blow version of chemical reactivity really is a new unexplored, and truly gigantic scientific ballpark.
According to the Stanford Report, this includes the making and breaking of chemical bonds.
We need to get a bit technical here.
In the abstract of the paper Stanford published in Nature, the description gets a bit more demanding:
“… its comprehensive description challenges our current theoretical and computational capabilities as it requires advanced treatment of electron correlation and nonadiabatic dynamics beyond the Born–Oppenheimer approximation.”
The Born–Oppenheimer approximation just happens to be “a fundamental mathematical model in quantum chemistry and molecular physics stating that the motion of atomic nuclei and electrons within a molecule can be separate” according to Gemini’s definition.
There’s the real breakthrough. Simulations are, to put it politely, the scientific equivalent of time-consuming ditch-digging. A simulation has to survive torturous checks to be viable.
The Stanford X-ray lasers have accurately visualized the reality of these relationships and actual electron movement. The new horizons are now visible.
What can this tech do? Pretty much everything.
It’s impossible to overstate the depth of value and the importance of this method in just about any application of real physical science. It couldn’t possibly be more useful for all levels of chemistry and materials science.
Imagine what the ability to check chemical reactions could do for anything from organic chemistry to biology to pharmacology to “designer” materials.
It’s historically very good timing, too, with the constant demands on materials science reaching new levels for accuracy and hard information regarding physical processes.
Consider the demands on microprocessors alone in their trillions, base materials, related materials, and the nano structures needed for so many new processes. This isn’t just the cutting edge. It’s the future cutting edge for materials doing things that have never been done before. The microprocessors alone require anything up to billions of models in simulations.
The Stanford LCLS method is likely the very beginning of a whole new science and it will deliver value from baseline chemistry to abstruse exotic new materials. The many decades of laborious theoretical models and brutally turgid patient research now have some backup.
“Productivity”, you say? Yes. By the omnibyte.
Moving on from Sales Pitch For The Obvious to the very hard science, the scale of information involved is exponential. Electrons aren’t famous for their cooperation with observation, and there are a lot of them.
The average electron has an infinite life expectancy longer than the universe. This is their first real appearance in a feature film, and it might be worth getting to know them a bit better.
They also generate so much information per femtosecond that “omnibyte” is perhaps too appropriate a metric. An “omnibyte” could be called a net all-inclusive unit of information related to a specific case. A distinct identity for a discrete mathematical set, if you like.
Per electron? Maybe. What if one electron does something unexpected? What if a whole new process hinges on pinning down electron behaviour in specific conditions. Cryogenic or plasma temperature? In the presence of what?
This won’t reduce the mental workload, in fact, it’s likely to increase it, but it will make it far more efficient in terms of managing information. You’ve now got a Yes/No response to work with in sims and models. That’s “productive” in anyone’s language.
Now the hard work begins
LCLS has a long track record in “snapshots” and is well-known for its adaptability in 3D molecular structuring. This level of information is where a lot of the data you see in research starts. It’s foundational.
The movie is a narrative. It’s the story of processes. It’s the continuum with qualifiers. It’s exactly what’s needed.
This is also a way of getting at the difficult stuff that reality keeps throwing at the sciences. Good science has been making discoveries that generate more questions than answers for millennia.
If you know the history of science bumping into those answers, you might agree that being able to at least explain them could be very helpful. It’d be downright nice to know why things happen.
Now, add AI processing values and the ability to test and verify on that scale. This is core business for all the sciences. Underselling the possibilities isn’t an option here.
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Disclaimer
The opinions expressed in this Op-Ed are those of the author. They do not purport to reflect the opinions or views of the Digital Journal or its members.
Paul Wallis
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