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Comment by kragen

4 years ago

It's a good question; unusually, that doesn't mean I don't have a good answer.

Steel is so far from being a more or less uniform substance that it's not even funny. There are four major phases that play roles even in the commonest carbon steel (ferrite, cementite, austenite, and martensite), plus others that can form at times like graphite, which plays an important role in cast irons. Ferrite and cementite can form nanolaminated microstructures called pearlite and bainite which have a major influence on the properties of the steel, and there are other microstructures that form depending on cooling speed, heat treatment, and cold working. So even the simplest steel is a nanostructured composite of metal and ceramic whose properties are hard to model computationally, though great strides have been made in recent decades.

Then, once you add other alloying elements besides those two (intentionally or not), steel stops being so simple. You can find phase diagrams for most of the binary systems (vanadium-carbon, for example, or vanadium-iron) but most of the ternary systems probably include compounds that haven't been identified yet. In theory you could find them computationally, I think. Even when you have a phase diagram, though, that doesn't tell you how fast the phase transitions happen, which depends on things like the crystal structures of intermediate unstable phases.

I don't know anything about this stuff, I just read about it. Recommended! Start with https://www.tf.uni-kiel.de/matwis/amat/generalinfo_en/guided...

That's still only, what, twenty or thirty dimensions? I guess it's hard enough to gather data that it's not as simple as feeding it a big black-box optimizer, something like SageMaker or Vizier that's designed to tune ML models with week-long training times and dozens of hyperparameters, but that'd still be quite a bit more powerful than the manual search that the author talks about.

  • Questions about prime numbers are asked in one dimension.

    The manual search is guided by a lot of very rigorous theory-of-experiment. It's not just trial and error, it's quite a bit more.

Naive question: Has anyone tried to whack these questions with the machine learning hammer? I figure if we can do protein folding [0], we should be able to do knives.

[0] https://deepmind.com/blog/article/alphafold-a-solution-to-a-...

  • Folks are certainly working on it from many dimensions, but it’s a pretty hard problem since getting ground truth data involves making and testing materials, which is a very different problem to automate than training machine learning models. You need a fairly cross disciplinary team to make progress. As an example of folks doing good work: https://a3md.utoronto.ca/

  • I don’t believe we have a good way to compute various macro properties of something like steel. We can compute density and what not, but how much it holds its sharpness or something is something I haven’t seen.

    So I am not sure how to get the training data needed for ML.

    (Computational chemist, but not computational materials scientist. So could be wrong!)