AI method rapidly speeds predictions of materials' thermal properties

This is slightly tangential but related.

The reason AI will do great things all levels of reality and different fields of studies (i.e. physics, biology, material science, climate, etc) is that these aspects operate from rules governing behaviour at the specific scale. These context specific rules derive from and summarise fundamental physics directly. This also explains why these methods will succeed in every domain. Analogy: Euler's rule is a topological invariance. So applies to both Spherical and Euclidean geometry.

The second, also called the Euler polyhedra formula, is a topological invariance (see topology) relating the number of faces, vertices, and edges of any polyhedron. It is written F + V = E + 2, where F is the number of faces, V the number of vertices, and E the number of edges.

Popular science articles always get this wrong. I did PhD on magnetic skyrmions and they're widely called 'particles' in articles. You also always see a lot about 'magnetic monopoles' where it's an emergent phenomena in crystal structures.

I don't think MIT and MIT's marketing department is really the same thing. It's not that the authors are writing this stuff.

It's crucial to distinguish

It’s fields all the way down; even thinking of atoms as particles isn’t accurate as far as we can understand matter.

They're a thing that if present will maybe make a detector go click (or beep, or light up, or whatever that detector does to say "yes something is here").

Doesn't that make them a particle? Or is there a better idea of what particles are that I should be using?

No, lots of energy goes into moving large things around. Energy that goes into doing work (as in the physical quantity work) doesn't become heat.

Eventually, yes, but I don't think that's what they meant. The universe is far from thermal equilibrium :)

If you push a stone up a hill, then some of the calories you burned went into the gravitational potential energy of the stone, and some up it was lost as heat.

If you compute the SHA hash of some string, then some of of the energy from the power supply went into switching the voltages in transistors, and some of it was lost as heat.

Fundamentally, yes. All motion does end up adding entropy to the environment. This is especially egregious for fossil fuels which are literally combusted/oxidized to move chunks of metal on wheels carrying different kinds of cargo. Electric vehicles change the dynamics somewhat but the motion of magnets still ends up contributing entropy to the environment at a somewhat reduced rate than fossil fuel vehicles.

To answer the question as literally as possible, if you reuse heat from a process in order to raise the temperature of something that needs heating, you are reducing waste heat.

Here's an example, unrelated honestly to the article but just your question -- you have a pan of water on the stove, and you are continually pumping room temperature water into it. You are also continually pumping heated water out of it. This is analogous to a lot of chemical reactors but to keep it simple.

If instead you run the hot water being pumped out next to the water being pumped in, the heat can be moved from the hot side to the cold side. That means less energy is needed to heat up the water coming in, because it is preheated.

So, in practice, you do indeed release less heat energy because less is required to keep that kettle hot. The hot water being removed gets cooled down some too, for better and worse (often this is good, who wants hot chemicals right?).

This has nothing to do with the article though, which is talking about using a neural network trained to help speed up predictions of phonon dispersion relations, which is great but maybe a little unclear. A phonon is sort of a quanta of heat if you like, so knowing how they travel in a material certainly is important and objectively cool, but the 70% thing does feel like a bit of a non-sequitur.

To be honest I suspect the first sentence and second are not as related as they are implied to be, the first is simply true and the second is also true but is hardly the reason the overall industrial base of humanity is only 30% efficient. Some stuff just can't be 100% efficient, like a Carnot engine. I'm shocked it hits 30% considering thermal power stations are a major thing [1]. But I also doubt materials limits make a thermal power station less efficient by much, there's just a limit on a thermal engine.

[1] To be clear, there is no citation in the press release to explain the source of this claim. Maybe it is only including after electricity generation, or maybe it only applies to industrial facilities, or maybe they're doing some weighted average of the efficiency of different electronics. I am assuming the 70% includes generation losses.

No, some of our energy generated gets converted into potential energy and stays that way. For instance in cement production when limestone is calcinated to produce calcium oxide, or when iron ores are reduced to pig iron.

Im not sure if all such processes amount to 30% of world energy consumption though.

No, not all of it ends up as heat.

Energy conversion efficiency[0] describes how much of energy type A is converted into energy type B to perform a useful activity, with the remainder being lost as wasted heat.

For instance, internal combustion engines convert chemical energy into kinetic energy with up to 50% efficiency, with the rest being dissipated and requiring a radiator in the vehicle to remove the excess [wasted] heat. Which is why your car engine becomes very hot after driving the car for a little while.

The same principle applies to other systems.

[0] https://en.m.wikipedia.org/wiki/Energy_conversion_efficiency

I guess some of the energy is turned into chemicals, eg nitrogen fertilizer.

When you drive a car, not all of the energy from the gasoline turns into heat. Some energy ends up as kinetic energy that makes the car move.

Imagine your computer has a knob for "release heat" and that setting it to zero on warm days did not reduce the amount of computation done.

Maybe heat is desirable in some contexts, but in most it is an undesirable waste product that takes too much space to eliminate. (Heat pipes, fans, and AC are what percent of data center costs?)

I don’t think this is _the_ problem, or even a problem. So what if we have a class of techniques that share some fundamental commonalities and label them all under the same broad category?