
Using Artificial Intelligence to Engineer Materials’ Properties - prostoalex
http://news.mit.edu/2019/artificial-intelligence-engineer-microchips-0211
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anton_tarasenko
Milton Friedman on doing this in the 1940s:[1]

> One of my problems was to provide statistical advice to the people who were
> developing metals to be used in the blades of turbines. I had an enormous
> amount of data, and I had to construct a regression with five or six
> different variables having to do with the chemical composition of the
> metals.

> We estimated that it would take us three months to solve this problem using
> our desk calculators. In the whole country there was only one calculator—one
> computer, if you want to call it that—which could do this problem more
> quickly.

> It was up at Harvard. It wasn’t electronic. It was a whole collection of IBM
> card sorters. It was in a big, air-conditioned gymnasium, a tremendous
> collection of sorters all linked by wires. It did our problem for us in
> forty hours.

As he mentioned elsewhere, it did not work as expected back then.

[1]
[https://miltonfriedman.hoover.org/friedman_images/Collection...](https://miltonfriedman.hoover.org/friedman_images/Collections/2016c21/Stanford_01_01_1996.pdf)

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ArtWomb
The prediction algorithm is cool. But call me a tad skeptical. Repeated
application of elastic strains results in creep occurring after 1000s of
cycles does it not?

AI research into the Materials Genome gets attention. But the problem of
discovery still seems secondary to the primary issue. Getting innovations out
of laboratories and into manufacturing.

~~~
rjsw
... and you won't get any funding unless you are working on something like the
Materials Genome.

Trying to get innovations into industry just isn't sexy enough.

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wenc
I don't know much about materials engineering, but it's not clear from the
article how Artificial Intelligence (which I'm assuming means Neural Networks)
is used. Is it to simulate different strains? Or to correlate a set of
parameters to final product properties?

I'm aware of efforts in statistical product design, where known mixtures,
manufacturing parameters, etc. are collected into a database, and design-of-
experiments is used to extrapolate into new products with properties that have
never been made before. But this is mostly accomplished using statistics and
design of experiments.

~~~
borkt
Agreed - this just seems to be an extension of the calculator story listed
above, except using machine learning. Why wouldn't you use state of the art
technology (including those labeled as AI) to help push any field forward?

~~~
sgt101
looking at the paper in PNAS I think that there is a clever arrangement and
application of deep nets in play here. As ever the trick is to couple deep
domain understanding to clever application of AI techniques.

~~~
wenc
From what I gathered from the paper -- again I'm not an expert on what they're
doing -- could the NN model have been replaced by any number of more
parsimonious nonlinear regressors? (Not that an NN isn't a valid choice -- it
is often used as a nonlinear regression model, but it is one of many valid
choices in this scenario.)

Which prompts the next question: does anything that uses NNs warrant the term
AI?

The use of NN here seems to be that of a model surrogate (i.e. model
reduction).

There's no perception problem, logic problem, decision problem, or anything
that we commonly associate with "intelligence".

~~~
sgt101
Well. Maybe it could be neater (in an information theoretic sense) but DNNs
are a bit fuzzy on that, and if you have modern compute, who cares? I think
that there's many questions about the validation of machine learning models
which mean that your question "could the NN model have been replaced by any
number of more parsimonious nonlinear regressors?" is open, in the sense - we
don't have a sharp way of deciding which is best, because one out of 100
million isn't informative for statistical reasoning.

The other question "does anything that uses NNs warrant the term AI?" is very,
very difficult. Because as Marvin Minskey said "intelligence is a portmanteau
term" by which he meant overloaded. It's full of meaning and non specific, so
I like to say that AI is about technology and capability and not an
association with human or animal cognition - which is the domain of Artificial
Intelligence proper.

~~~
wenc
To the first point, I believe it's a spectrum.

To a practitioner, "better" can be defined along well-known dimensions.
Suppose you know your data lies more or less on a straight line -- you could
fit a NN model or a run a linear regression. In this scenario, linear
regression would be the "better" choice for some widely-accepted measures of
"better" (interpretability, computational efficiency, parsimony,
regularizability, etc.).

One almost never chooses NN just for the sake of choosing NN... there are
well-understood trade-offs. For instance, it is widely known that NN's
generally require a larger amount of data than classical/statistical
algorithms for weights to converge -- mostly because it's fitting a more
general function than most classical ML algorithms. (The reason NNs have begun
to show the results they have (vs in the 1990s) isn't just because we have
more compute than before or that the theory has advanced significantly; it's
also because we have more a lot more data to fit the more general function
with.)

To the second point, I can see the point, but I wonder if the semantic meaning
of the term is eroded by being overly encompassing. We could say a calculator
implements AI.

~~~
sgt101
Agree about data. Do you really think theory hasadvanced?

~~~
wenc
In my opinion fundamental NN theory hasn’t really advanced significantly since
the 1990s. I guess it was phrased ambiguously in my comment

But there has been a great deal of new techniques that make NN work better in
practice like dropout, ReLU for vanishing gradients, CNNs and GANs for
specific problem types, transfer learning, etc. The recent work with Neural
ODEs show that the field is advancing in terms of ideas.

This is a good trajectory IMO. Many engineering fields work this way — find
out new ways of doing things that work and then take a step back to see if
there’s anything fundamental that links everything together. Practice precedes
theory.

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shoyer
Here's a link to the actual article in PNAS:
[https://www.pnas.org/content/early/2019/02/14/1818555116](https://www.pnas.org/content/early/2019/02/14/1818555116)

~~~
wenc
Ok, so here's where they use NN models:

 _" Although ab initio calculations such as those involving many-body
corrections can provide accurate energy-band results, the scope of such
calculations is somewhat limited to about 1,000 strain points because of high
computational cost. On the other hand, by discretizing ε with a regular grid
comprising 20 nodes separated at each 1% strain interval over the strain range
of −10 to +10%, the computational model would entail about 108 band
structures, up to five orders of magnitude higher computational requirement
than what can be reasonably achieved presently. To overcome these
difficulties, we present here a general method that combines machine learning
(ML) and ab initio calculations to identify pathways to ESE. This method
invokes artificial neural networks (NNs) to predict, to a reasonable degree of
accuracy, material properties as functions of the various input strain
combinations on the basis of only a limited amount of data."_

Further down:

 _" We aim to describe the electronic bandgap and band structure as functions
of strain by training ML models on first-principles density-functional theory
(DFT) data. This approach leads to reasonably accurate training with much
fewer computed data than fine-grid ab initio calculations and a fast
evaluation time."_

\--

If I'm reading this correctly, it sounds like they _already have a_ high-
fidelity 1st-principles model that is computationally intractable to solve at
scale, so they are using ML techniques to create an surrogate model that is
computationally more tractable -- the AI modeling is a model reduction
exercise.

~~~
leplen
That's a pretty good summary. There are many different sets of approximations
and simulation techniques that make different trade-offs of accuracy/scale.

They're essentially using outputs of a higher-fidelity model to tune the free
parameters of a lower fidelity model, and using ML to explore the parameter
space efficiently.

The higher fidelity model is itself still an approximation, but there's a lot
of interest in this approach and quite a few groups doing work like this
targeted towards various material properties, since there just isn't nanoscale
experimental data to train models that depend on nanoscale material features.

~~~
steve_musk
Agreed, although I would hesitate to call the PBE functional a “first
principles” model.

