Welcome back to the Deep Dive. We are.
We're doing something a little specialized today and frankly, I
think it's about time we did. I agree.
We're looking at a pretty fundamental pivot in how well
how humanity invents things, how we create new materials.
It's a huge topic and our source material for this is really
interesting. We're looking at a lecture from
Shiki Wang, right? It was delivered at the
University of Helsinki for Professor Pedro Camargo's
course. And the date on the material is
February 13, 2026. So yeah, we're getting a little
glimpse of the very near future. We're basically stepping into a
university classroom, which is a fun change of pace for us.
The title is, and it's a mouthful.
Development and application of computational methods on
catalysis research. Right.
And I know as soon as you say catalysis, you can almost hear
people's eyes glazing over. It sounds so, so high school
chemistry. It does, but The thing is, this
is actually about the engine of the entire modern world.
I mean literally everything. The fuel in your car, the
medicine you take, the plastic in your phone case, it all
exists because of catalysis. It's the secret ingredient that
makes things happen. Exactly.
And the computational part of that title, that's the real
story here. We are witnessing the end, or
maybe the beginning of the end of the shake and bake era of
chemistry. The shake and bake era.
I love that. That's the Edisonian method,
right? Trial and error 100.
I have a beaker. I have a hunch.
I mix some things together and I just, you know, I hope nothing
explodes. Precisely.
And for the last, what, 200 years?
That's genuinely how we found almost everything.
It's incredible to think about. From penicillin to Teflon, it
was largely based on educated guessing, a lot of serendipity,
and frankly, a whole lot of. Luck.
But Shiki Wang's lecture makes it painfully clear that that
method is, well, it's hitting a hard wall.
It is. It's just too slow, it's way too
expensive and honestly all the low hanging fruit has already
been picked. We can't rely on luck anymore
and the. Stats and the source material to
back this up are they're wild. I mean, they mentioned that
moving from that traditional trial and error to what they
call theory guided experiments. So using computers to guide you.
Exactly that simple shift cuts the development process by over
60%. 60% just stop and think about what that means in
practice. It's.
Huge. If it takes, say, 10 years to
bring a new battery material to market, which is pretty
standard, cutting that by 60% means you're doing it in four
years. You're getting it six years
earlier. That's the difference between
maybe solving a major part of the climate crisis and
completely missing the boat. It's a revolutionary speed up.
And then there's the cost metric, which let's be honest,
is probably the real clincher for the industry.
Oh, absolutely. It always comes down to the
money. The lecture notes state that a
single calculation using DfT, that's density functional
theory, and we will get deep into what that is.
It costs roughly one one thousandth of the equivalent
experimental expense. 1000 I mean, that's not an incremental
improvement. No, it's a phase shift.
It's the difference between buying a house and buying a cup
of coffee. It's just not on the same
planet. And that completely changes the
risk profile for research, doesn't it?
How so? Well, if I'm running a physical
lab, every single experiment burns cash.
You've got the chemicals, the waste disposal, the equipment,
time, the personnel. The energy costs, which are
massive. Right.
So I have to be conservative. I can really only afford to test
the things I'm pretty sure are going to work.
You stick to what you know. You make small changes to proven
formulas. Exactly.
But if the cost of an experiment drops by a factor of 1000.
Then you can test the crazy ideas.
You can test the things that shouldn't work just on the off
chance they do. You can explore the entire
chemical universe, as they call it, rather than just, you know,
the little neighborhood you're already familiar with.
It's like going from a map of your town to Google Earth.
That's a perfect analogy, and that leads us right into the
first big theme of this lecture, which is just the why.
Why are we doing all this? And aside from the speed and the
money, it's about overcoming our own human limitations.
The lecture highlights overcoming experimental limits
as a primary driver. This is so crucial.
There's a fundamental resolution problem in physical chemistry.
You know, we talk about atoms like the little Legos we can
move around, but in a real experiment, you can't just you
can't just look at a single atom and watch what it does.
No, you're always looking at averages.
Averages of trillions upon trillions of atoms all doing
something slightly different. Exactly.
But all the interesting stuff, the chemistry, the reason a
reaction works or fails, it happens at the margins.
It happens with that one weird atom on the corner of a
nanoparticle. And you can't see that.
You can't. The source specifically mentions
resolving atomic scale dynamics, and they give the example of
dislocation motion. Now for the material scientists
listening, you know the dislocation motion is
everything. It's how metals bend instead of
snapping. It's how things fail.
But it. Happens at what?
The speed of sound inside a solid block of metal at the
scale of a nanometer? You can't exactly stick a GoPro
in there to see what's happening.
I really, really can't. But in a simulation in a
computer model, you're basically.
You control the universe. You can pause time, you can
rewind it, you can strip away the outer layers of the metal
and justice look at the core. You can see the exact moment a
bond breaks or a new one forms. That kind of visibility is
something no physical experiment on Earth can ever give you.
And the other limitation the lecture brings up is the
environment, what they call the dangerous factor.
Right, things that are too hot, too high pressure or too
radioactive to study easily. And the example they use is
predicting irradiation damage in nuclear reactors.
A classic problem, and a super important one if you want to
build a fusion reactor. The dream of clean energy.
You need to design materials that can withstand a constant
bombardment of high energy neutrons that would basically
disintegrate almost anything we can currently.
Make and you can't exactly build a test fusion reactor just to
see if your new wall material melts.
Well, you can, but it costs billions of dollars and takes a
decade or more. Or you use computational
modeling. You build the wall inside the
supercomputer. You simulate a single neutron
hitting the atomic lattice. You calculate the energy
transfer. You watch the cascade of damage
as that one neutron knocks other atoms out of place.
And you can fail 1000 times in the simulation.
A million times. And you do it without ever
creating a single speck of radioactive waste.
We're spending billions of dollars.
So it's safer, it sees the invisible, and it's
astronomically cheaper. That's a pretty compelling case.
But then there's the acceleration aspect.
Yes, the lecture talks about high throughput screening.
This is the brute force approach.
Right. It is brute force, but it's
smart brute force. I mean, think about it.
In the old days, a PhD student might synthesize and
characterize maybe maybe one new material a week.
If they were lucky and all the equipment was working and
nothing broke, right? And they didn't contaminate
their sample. High throughput screening is
more like a machine gun. You're not firing one bullet,
you're firing off thousands or 10s of thousands of calculations
simultaneously on a supercomputer cluster.
And the lecturer sites a specific MIT study that really
just it illustrates this perfectly.
They use this method to look for new electrolytes for lithium ion
batteries. And they didn't just find a few,
they identified 21 promising new candidates.
And these weren't just, you know, a 5% improvement.
The source says they found candidates that could lead to
100 times efficiency gains 100 X.
And here's the kicker, the part that really matters.
They likely found them in parts of the chemical space that
humans were completely ignoring. Why?
Because humans are biased. We have chemical intuition,
which is great, but it's also a blinder.
We tend to modify things we already know work.
We think, oh, this electrolyte works pretty well.
Let me just add a methyl group over here and see if it gets a
little better. You stick to your comfort zone,
the computer. The computer has no comfort
zone. It doesn't care about tradition
or intuition, it just methodically churns through all
the possibilities you give it. It looks in the weird corners of
the periodic table that no human would ever think to look.
Exactly. And that's where the
breakthroughs are hiding. Which leads us, I think, to the
biggest, most mind bending number in the entire fly deck.
The deep mind stat. Oh yeah, the 2.2 million
structure. It's just it's staggering.
Deep Mines AI, their material science model, predicted 2.2
million new stable crystal structures.
OK, put that in context for us. How many did we as a species
know about before that? In the entire history of
inorganic chemistry leading up to that point, humanity had
painstakingly identified and catalog maybe, maybe 50,000
stable inorganic crystals. So in one project, one AI
basically did what, 1000 years of human scientific work in an
afternoon? Essentially, yes.
It's a breathtaking expansion of our knowledge.
Now, we have to be careful here. Predicting a structure exists on
a computer is not the same as holding in your hand.
There's still a lot of work to do to synthesize them, of
course, but it gives us a map and almost complete map of
what's possible. It turns us from explorers
hacking our way through the jungle with a machete into
navigators with a full blown GPS.
And this concept of inverse design that the lecture
mentions, that's the GPS, right? That's the navigation system.
That's exactly traditional science.
The shake and bake method is direct design.
You have a material and you measure its properties to see if
it's any good. Yep.
Does this rock conduct electricity?
No. OK, how about this rock?
No, you're just testing what you find.
Inverse design completely flips the script.
It does. You start with the answer.
You go to the computer and you say I want a material that is
transparent, conducts electricity like a metal, is
flexible and cost less than $5 a kilogram.
So you define the properties you need first.
And the computer searches that massive database of 2.2 million
structures. It works backward to tell you
OK. To get those properties you need
to make strontium, titanium and oxygen in this precise crystal
structure. It's the ultimate shortcut to
innovation. It's designing with intent
instead of by accident. That's the goal.
But let's let's get under the hood here, because I don't want
to just hype the results. We need to understand the tools
that make this possible. The lecture uses this wonderful
phrase from Newton to Schrodinger, which I think is a
great way to visualize the hierarchy of methods.
It's the scale adder, and this is probably the most critical
concept for anyone trying to get their head around computational
chemistry. There is no single master
algorithm that simulates everything from an electron to
an airplane. It just doesn't exist.
Right, you can't use the same math to model a single electron
that you use to model, say, a bridge.
Exactly. The physics that matters changes
depending on the size of the thing you're looking at and the
speed at which it's moving. The lecture breaks this down
into 4 main tiers. OK, so let's start at the
bottom. The absolute basement of
reality. Tier 1 quantum mechanical
methods. This is the electronic scale.
We are dealing with length scales of angstroms, that's .1
nanometers and time scales of femtoseconds.
We're talking about the behavior of individual electrons.
And the boss down here, the governing law is Schrodinger,
the. Schrodinger equation, yeah, It's
the fundamental equation of quantum mechanics.
It describes the wave function of a system.
And if you can solve it, you know literally everything there
is to know about that system, the energy, the position of
electrons, how it will react. Everything but, and this is a
big but, there's a catch. There's a huge catch.
It's called the Many Body Problem.
The Schrodinger equation is actually pretty easy to solve if
you only have one electron and one proton, like a hydrogen
atom. But as soon as you have two
electrons. Even just two, like in a helium
atom, it becomes incredibly difficult because now you don't
just have the electrons attracted to the nucleus, you
have the two electrons propelling each other.
And as you add more and more electrons, the number of these
interactions just explodes in complexity.
So for a real material with trillions of electrons, it's
it's just not possible. It's mathematically practically
unsolvable in its pure form. So we have to cheat.
Or to be more polite, we approximate.
And this is where DfT comes in. Density functional theory.
It's all over these lecture slides for a reason.
It's the workhorse. It is the absolute workhorse of
modern computational chemistry and material science.
It won the Nobel Prize in Chemistry in 1998, and it
deserved. It So how does it cheat?
What's the trick? The trick is genius.
Instead of trying to track the position and momentum of every
single electron, which is a nightmare of complexity, Anybody
problem, right, DfT says let's just look at the overall
electron density. OK, so instead of tracking every
single car on the highway, you're just looking at the
overall flow of traffic. The traffic jam itself is a kind
of fluid. That is a fantastic analogy.
By focusing on the density, a property that only depends on
three spatial coordinates XY and Z, you reduce the complexity
from a three end dimensional problem where N is the number of
electrons down to just three dimensions.
And that makes the impossible possible.
It makes it possible on a supercomputer.
The lecture lists the software heavyweights here, VISP, Quantum
Espresso, Cast Step, and Gaussian.
Are they all just different flavors of the same thing?
Not quite. They're distinct tools for
different jobs. VSP, Quantum Espresso, and
Casket are typically used for solid-state physics.
So metals, semiconductors, crystals.
Exactly things that are periodic that repeat in space.
They use something called plane waves to describe the electrons.
Because in a metal the electrons are delocalized.
They act like waves moving through the entire solid.
Whereas Gaussian is different. Gaussian is the undisputed king
of molecular and organic chemistry.
So if you're designing a new drug molecule, for example.
Right. It uses what are called
localized basis sets. It treats electrons as being
more or less attached to specific atoms, which is a much
better description for individual molecules.
So if you're designing a new pharmaceutical, you're probably
using Gaussian. If you're designing a new
battery cathode, you're probably using VSP.
But even with the brilliant cheat that DfT uses, we still
have limits. The lecture notes say these
methods are usually limited to less than 1000 atoms.
And even 1000 atoms is pushing it for a routine calculation.
A standard DfT job might handle a few 100 atoms.
The problem is that the computational cost still scales
badly, usually with the cube or even the 4th power of the number
of electrons. So if you double the size of
your system. The calculation takes 8 or 16
times longer to run. It gets expensive very, very
fast. So you can model a very accurate
picture of the, say, the active site of a catalyst.
The most important part, Yes. But you can't model the whole
nanoparticle it sits on, let alone the whole reactor.
Not with this method. For that you have to climb up
the ladder to Tier 2 atomistic simulation.
And this is where we say goodbye to Schrodinger and quantum
mechanics, and we say hello to good old Isaac Newton.
We do. We're in the world of classical
mechanics now. The main tool here is molecular
dynamics or MD. OK, so what's the big
simplification we're making here?
The big simplification is that we completely stop caring about
individual electrons. We just ignore them.
We treat the atom as a single hard sphere, a little ball like
a billiard ball. We're essentially dumbing the
system down. So how did the balls know how to
interact with each other? We tell them we use what's
called a force field. It's basically a set a simple
mathematical rules. Like what?
Like if ball A and ball B get too close, push them apart with
this much force. If they're connected by a
chemical bond, treat that bond like a spring and pull them
together. It's all just simple springs and
repulsions. And because we're not solving
these incredibly complex quantum equations anymore, this must be
way, way faster. Orders of magnitude faster.
It's not even a comparison with MD.
You can simulate systems with millions or even billions of
atoms. You can watch proteins fold in
water. You can watch polymers entangled
to form plastics. The lecture lists the big
software names here to LAMP, PS, Gromax, Amber.
Right, and again, they have their specialties.
Gromax and Amber are very famous in the biochemistry world for
simulating proteins in DNA. LAMP PS is more of a general
purpose tool, very powerful for material science.
There has to be a huge trade off.
If we're ignoring electrons, how can we possibly do chemistry?
Chemical reactions are electrons moving around, bonds breaking
and forming. If I just have little balls
connected by springs, I can't break the spring.
And that is the classic fundamental limitation of
standard classical MD. It is non reactive so.
It's great for physical processes.
Fantastic melting, boiling, diffusion.
How a material responds to being stretched.
All physical things, but it's terrible for modeling chemical
reactions. So it can't model a fire.
No, but the lecture mentions a bridge, a clever way to get
around this called Reax FF. Reactive force fields.
This is a really brilliant hybrid method.
It's still a classical method at its core.
It's not solving Schrodinger's equation, but the force field
itself. The rules for the springs is
much more sophisticated. It's been trained on a vast
amount of quantum data from DfT calculations.
So it learns how bonds should behave.
Exactly. It has a concept of bond order.
It knows that if you pull 2 atoms apart, the bond between
them gets weaker and weaker until at a certain point it
snaps and it knows how to form new bonds.
O with Rex FF you can simulate a fire.
You can. You can simulate combustion, or
an explosion, or a battery material degrading overtime, all
these complex chemical events involving millions of atoms
which you could never ever hope to do with pure DfT.
It sits right in that sweet spot between the two tiers.
It's an incredibly powerful tool for bridging that gap.
OK, let's keep climbing. We're moving up the ladder to
Tier 3, the mesoscale. This is the middle ground,
right? We're zooming out again, and
here we start to blur the atoms together.
The main technique is called coarse graining.
It sounds like pixelating an image.
That's a great way to think about it.
Imagine you're trying to model a cell membrane which is made of
millions of lipid molecules. You don't need to know the exact
position of every single hydrogen atom on every lipid
tail. It's just too much detail, you'd
never be able to simulate it for long enough to see anything
interesting. Exactly.
So with coarse graining, you might say, OK, let's treat the
head group of a lipid as one big bead, and we'll treat the tail
as two or three other beads connected by a string.
You're simplifying the representation.
Drastically. And by doing that you can
suddenly simulate much larger systems for much longer times.
Instead of nanoseconds, you can watch things happen over
microseconds or even milliseconds.
This is how people study things like protein folding or how
polymers self assemble. And the lecture also mentions
the phase field method here. That sounds a bit different.
It is. This is huge for metallurgy and
material science. Imagine a pool of molten metal
that's cooling down and starting to solidify.
Crystals begin to form. Right, little crystals, what we
call grains, start to grow. They bump into each other and
form boundaries. The Hase field method is a
mathematical way to track the evolution of those boundaries an
the shapes of those grains overtime O.
It's not tracking atoms at all anymore.
Not at all. It's tracking a continuous field
that just tells you this region is solid crystal A, this region
is solid crystal B, and this region is still liquid.
It's another level of abstraction up.
And that leads us to the top of the ladder, Tier 4, the macro
scale or continuum methods. This is the world we actually
see and live in. This is engineering.
This is where the material becomes just a solid uniform
block with certain properties like strength and conductivity.
We use tools like the Finite Element Method or FEM, or
Computational Fluid Dynamics CFD.
This is what an aerospace company uses to design A wing.
Exactly. They are not simulating the
individual iron and aluminum atoms in the wing.
They've smeared all that detail out.
They're solving equations for stress, strain, heat, flow and
things like that. And for CFD it's airflow.
Right, they're solving the Navier Stokes equations for how
air flows over the wing. The software here are giants
like COMSOL and NSYS. So the ultimate goal, and I
think the lecture is really emphatic about this point, is to
link all of these tiers together.
Yes, that's the dream. To take the quantum data about
how 2 atoms bond, feed that information into the force field
for your molecular model. Use the results of the molecular
model to parameterize your mesoscale model, and finally use
that to tell the engineer what the bulk properties of the
bridge will be. That whole process is called
multi scale modeling. It is the Holy Grail of this
entire field. It's incredibly difficult to do
seamlessly to pass information between the scales without
losing accuracy, but it's where everything is headed.
OK, so we have the toolkit. We understand the latter of
complexity. Now let's apply it.
The lecture focuses very specifically on the field of
catalysis. The absolute heart of the
chemical industry, something like 90% of all chemical
products have seen a catalyst at some point in their production.
So if you can improve catalysts, you improve, well, everything
pretty much. And the core question the
lecture poses is what can computational methods do for
catalysis? And from what I can see, it
seems to boil down to understanding and improving 3
key pillars, Activity, Kinetics and Selectivity.
Right, let's break those down because this is where the theory
meets the real world. This is where the money is made.
Let's. Start with the first one
Activity. The slides say this is all about
the active site structure. The active site is the specific
special arrangement of atoms on the catalyst surface where the
chemical reaction actually happens.
You can think of it as a perfectly shaped parking spot
for a molecule. If the molecule parks there, the
reaction happens. If it parks anywhere else,
nothing happens. So a more active catalyst has
more of these special parking spots or better ones.
Exactly, and computation lets us design them.
The lecture mentions heteroatom doping is 1 strategy.
OK, what is that? So imagine you have a surface of
pure carbon, like a sheet of graphene.
It's pretty inert. The carbon atoms are all happy.
They don't really want to react with much.
But what if you use a computer to simulate kicking out one of
those carbon atoms and replacing it with a nitrogen atom?
That's heteroatom doping. You're introducing an impurity
on. Purpose a strategic impurity,
and you've completely changed the electronic landscape right
at that spot. Nitrogen has a different number
of electrons than carbon. It creates a little hot spot on
the surface. Suddenly, that one spot becomes
incredibly attractive to, say, an oxygen molecule in the air.
It activates it it. Activates it.
The reaction can now happen at that site, and with computation
we can screen the entire periodic table to predict
exactly which element to dope with and precisely where to put
it to get the maximum possible activity.
The lecture also mentions defect engineering.
I love this concept because in most parts of life, defects are
bad. You want a perfect diamond, a
flawless crystal. But in catalysis, perfection is
actually failure. It's so true.
A perfectly flat, perfectly ordered crystal surface is often
a terrible catalyst. The atoms are all happy and
comfortable and have the right number of neighbors.
They are low energy. They don't want to bond with
anything else. So you need to break the
surface. You need to introduce
imperfection. You need to rough it up, but in
a very controlled way. You need to create steps, edges,
corners and vacancies where you've plucked an atom out.
And why are those spots so active?
It's because the atoms at those defect sites have missing
neighbors. We call them low coordination
sites, and because they're missing neighbors, their
remaining electrons are sort of dangling, unsatisfied.
They are desperate to bond with something to become stable.
So they'll grab on to passing molecules.
You'll grab on tight and help them react.
Computers can tell us if you create a step edge with this
specific geometry and your nanoparticle, your reaction rate
will increase by 1000%. We can design the defects.
OK, that's activity. The second pillar is kinetics.
This is about movement, about speed, right?
The lecture frames this as support architecture.
It doesn't matter how amazing your active site is if the
reactive molecules can't get to it and the product molecules
can't get away. This is a mass transport
problem, a diffusion problem. And the lecture talks about
designing hierarchical pores. This is a beautiful concept
borrowed from nature. Think about a city's road
network. You need huge highways.
Those are the large pores to get a massive amount of traffic into
the city center quickly. But you can't park on a highway.
Exactly. So then you need the main
streets, the medium sized pores to distribute that traffic
throughout the neighborhoods. And finally, you need the little
driveways, the small pores to get to the individual houses
which are the active sites. If you only had tiny driveways
from the countryside, you'd have a permanent traffic jam.
If you only had highways, you could never stop.
Precisely. You need all three scales
working together. Nature does this brilliantly.
Look at the structure of your lungs or the veins in a leaf.
We use computations, specifically methods like CFD,
to design these hierarchical pore structures in catalyst
supports to maximize the flow of molecules in and out.
So you're engineering the Catalyst's plumbing system?
You're being a civil engineer, yes.
And that brings us to the third pillar, selectivity.
This is the really subtle one, the one that prevents waste.
This is arguably the most important one for green
chemistry. In many complex chemical
reactions, you have a choice. Your reactive molecule A could
turn into product B, which is the valuable pharmaceutical you
want, or could turn into product C, which is a useless toxic
sludge. And traditionally you just get a
mix of both and then have to spend a ton of energy and money
separating the good stuff from the bad stuff later on.
Right Computational catalysis allows us to tune the reaction
to be more selective, to force it down the path we want.
The lecture mentions field effects as a powerful way to do
this. Strain electric fields, Magnetic
fields. Take strain for example.
If you take your catalyst surface and you simulate
stretching it, literally pulling the atoms apart by just a few
percent, you change the exact distance between the binding
sites on the surface. So maybe the molecule that leads
to the good product, product B fits perfectly on that stretch
surface now, but the one that leads to the bad product,
product C doesn't fit anymore. That's exactly the idea.
You are physically tuning the lock so that only the right key
will fit. It's an incredible level of
control. Or you could apply an external
electric field, and the simulation can show you how that
field stabilizes the transition state for the reaction you want,
making it happen much faster than the one you don't want.
And the lecture also mentions using light photocatalysis.
This is a huge area, especially for green energy.
Think about splitting water into clean hydrogen fuel using only
sunlight. The simulation can track what
happens when a photon of light hits an electron in the
catalyst. It enters an excited state.
And you need that excited electron to go to the right
place. You do.
Does it jump to the right place on the surface to drive the
water splitting reaction or does it just fall back down and waste
that energy as heat? We can use quantum mechanics,
specifically time dependent DfT, to design materials that Channel
that light energy exactly where we need it to go.
O We're designing the activity, the plumbing, and the final
outcome. It's total control.
In theory, yes, that's the goal. OK, section five of our
discussion here brings us to what the lecture calls the
Trinity diagram. It's a flow chart that connects
insights, computations and guidelines specifically for the
task of Electro catalyst discovery.
This is basically the modern workflow.
It shows how a computational chemistry lab actually operates
day-to-day. It starts with insights on the
left, things like understanding the activity origin, reaction
mechanisms and active sites. So this is the human part, the
scientists intuition and existing knowledge.
What do we think is making this catalyst work?
Then that feeds into the middle box computations.
This is the engine this. Is the supercomputer doing the
heavy lifting? We've been talking about renting
the DfT, the MD, all of it. And that produces the output on
the right guidelines for the experimentalist and there are
three types listed, rational design, high throughput
screening and machine learning. Right, so rational design is the
classic approach. The computer tells you the
binding strength of oxygen is too strong.
Try adding a little bit of gold to your platinum catalyst to
weaken it. It's a specific logical
suggestion. High throughput screening is
what we talked about before, testing thousands of options
virtually to find the top candidates.
And machine learning is the new frontier, using AI and neural
networks to find patterns in the data that a human might miss.
I want to focus on the battery application here because the
lecture notes mention computational material science
in the context of anodes, cathodes, and electrolytes.
Everyone listening has a lithium ion battery in their pocket or
on their desk right now. What is the computer actually
calculating inside that battery? One of the most important
properties it's calculating is the migration barrier.
OK, unpack that term for us. A battery works by moving ions,
in this case lithium ions, from one electrode to the other.
To do that, a lithium ion has to hop from one empty spot in the
crystal lattice of the electrode material to the next available
spot. And that hop isn't free.
It's not free. It cost a certain amount of
energy to squeeze through the other atoms.
That energy cost is a migration barrier.
And if that barrier, that energy cost is too high?
The lithium ion moves very slowly, it struggles to make the
hop and that means your battery takes forever to charge and
can't deliver power very quickly.
But if the barrier is very low. You get fast charging,
supercharging. The lithium ions can just glide
through. The material.
Computation allows us to map out the entire energy landscape
inside a crystal and find the pathways of least resistance.
Yeah, we can design materials where the lithium just zips
through like it's on a slip and slide.
Amazing. The other term listed is
absorption energy. That's basically how sticky the
surface of the material is for the lithium ions.
You want the lithium to stick to the material.
That's how it stores energy, but not so much that it gets stuck
forever and can't leave. It's the Goldilocks principle
again. Not too hot, not too cold.
Exactly. And the final one here is phase
transformation. This is the swelling problem,
right? The reason?
Old phone batteries sometimes puff up.
That's it. When you shove a bunch of
lithium ions into a material like graphite or silicon, the
material has to expand to make room for them.
It physically changes its structure, its phase.
And that repeated swelling and shrinking with every charge
cycle causes stress. It causes immense stress which
leads to tiny cracks which grow overtime and actually kill the
battery. We can use simulations to
predict how much a material will expand and if it's likely to
crack, all before we ever have to build a single physical
prototype. It's just incredible how much
violent physics is happening inside that little quiet black
brick in our phones. It is a dynamic and frankly a
pretty violent environment at the atomic scale, and we are
finally learning how to tame it with mathematics.
So let's look forward. We're heading into the future
now with Section 6, the future landscape.
The lecture actually references papers from 2024, 2025, which in
the context of our 2026 source is the very recent past.
What is the cutting edge? The cutting edge is the
convergence of all these tools. The 2024 Advanced Materials
paper mentioned in the slides talk specifically about merging
first principles. That's our high accuracy DfT
with machine learning. And this brings us back to that
really important shift you mentioned earlier, the move from
descriptive science to predictive science.
That is the single most important take away from this
whole discussion. For decades, computation and
chemistry was largely descriptive.
An experimentalist would run a reaction, something weird and
unexpected would happen, and they would go to the
computational chemist and ask them to explain it.
It was a post mortem. Why did my catalyst turn blue
and stop working? Exactly.
It was forensic science. Now we are firmly moving into
the predictive era. The computer is no longer the
last resort for explanation, it's the first step in design.
The computational chemist tells the experimentalist what to
build before they go into a lab. If you put a single cobalt atom
right here surrounded by 4 nitrogen atoms, it will work.
And the surrogate models that are mentioned in the lecture.
This is a fascinating idea. This is the solution to the
scaling problem we talked about. You see, even though DfT is a
million times cheaper than an experiment, it's still too slow
to test, say, all 2.2 million of those deep mind structures.
It would still take forever. So a surrogate model is an AI.
You take a few thousand of those structures, a representative
sample, and you run the full expensive DfT calculation on
them. Then you feed all that data, the
inputs and the outputs to a machine learning model.
So the AI learns the underlying laws of physics from the DfT
data. It learns an approximation of
the physics. It doesn't understand
Schrodinger's equation, but it learns the pattern.
It learns to recognize oh when the atoms are arranged in this
particular way, the binding energy is usually X.
And once it learns that pattern. It can predict the properties of
a brand new structure it's never seen before in a matter of
milliseconds, instead of the hours or days it would take to
run a full DfT calculation. It's a virtual virtual
experiment, a simulation of the simulation.
You have a perfect description. You can use this cheap, fast
surrogate model to scan billions of potential compounds in a
single day. Now it's less accurate for sure,
but it acts as a massive filter. To find the needles in the
haystack. Exactly.
You use the AI surrogate to scan the billion options and find the
top say 10,000 most promising ones.
Then you use the more accurate DfT to check those top 10,000
and narrow it down to the top 100, and then you go into the
lab and synthesize the top 10. That is the funnel.
That is the modern pipeline for scientific discovery.
It's the future and it's happening right now.
It really makes you wonder about the changing role of the
scientist in all of this. Let's try to synthesize this
deep dive now as we wrap up. We started with the old way, the
slow, expensive luck based trial and error method.
The shake and bake. Era.
Then we walk through the modern computational toolkit, the whole
ladder from Schrodinger's quantum equation for electrons
all the way up to massive fluid dynamics for engineering.
And we saw how applying these tools creates better catalysts,
which leads to cleaner energy, more efficient industry, and
things like faster charging, longer lasting batteries and.
We ended on this incredible idea of AI surrogates learning the
ways of physics to do most of the heavy lifting.
It is a complete, undenied paradigm shift in how we invent
the future. I want to leave our listener
with a final provocative thought here.
The lecture mentions inverse design and this AI generated
library of 2.2 million materials that have never existed before.
If we have computers that can predict a material that has
never been seen on Earth, and then simulate its properties
down to the individual electron, and then verify that prediction
with a trained AI, are we rapidly approaching a point
where the lab is entirely digital?
It's a question of the century, isn't it?
If the simulation becomes perfect or near perfect, does
the physical experiment even matter anymore except as a final
confirmation? Right.
I mean, if the computer model, which is validated against
decades of data says this new battery design will last for
10,000 charge cycles, do I really need to sit in a lab for
three years cycling it 10,000 times to believe it?
Today, yes, you absolutely do. Verification is still king and
our models still have flaws and approximations.
But 50 years from now, when we have fault tolerant quantum
computers running these simulations with near perfect
accuracy. What happens then?
Then the physical world might just be from manufacturing.
All the discovery, all the R&D, might happen entirely in the
cloud. The Eureka moment moves
permanently from the lab bench to the server room.
For many researchers, it already has.
It certainly has. Cheeky Wang's lecture is a clear
road map to that future. It's a dense, highly technical
field, but the impact of this revolution is going to be felt
by every single person on the planet.
Absolutely no question. Thank you for joining us on this
deep dive into the atomic scale. Next time you plug in your phone
to charge, just take a second to remember there is a whole
universe of mathematics and quantum physics going on inside
that little battery. And chances are, a computer
choreographed that entire dance. Thanks for listening, We'll see
you next time.