Grad students at Cal cost a professor ~100k a year, and then leave after 2-5 years with any optimizations they might have personally made. They also only work 6-12 hours a day, and having been said grad student, get mind numbingly bored after about 10-15 repetitive syntheses, spending lots of time on them, when the (only) interesting part, is the XRD pattern at the end... I would have absolutely advocated for such an arm if I was still there.

There are many reasons why wasting a grad student on this problem (rather than a tech) is bad. I say this with a lot of experience: I was that grad student and I was the guy automating the lab and the guy setting up the compute infrastructure.

I think core facilities are better candidate than individual professor labs.

We already do this with automated e.g. drug testing. I've worked with a couple different machines (and worked on the development of another) that existed specifically to rapidly do certain chemical and biological tests, to parse through computationally or AI suggested drugs. They run at about the same cost, with similar service contracts, and they're VERY common in the pharmaceutical industry. If your goal was to find a process to produce some precursor chemical necessary for material development (prior to heading to the foundry), it makes sense.

I work in pharma and most of the time when I visit labs, they don't run 24/7 and in fact run at about 10% or less of their total capacity.

Well, people are still buying them, otherwise I'd be out of job. My experience with the Tecan and Hamilton machines that I got to interact with was that the setup seemed tedious as hell, but once it was off and running, it would rapidly outpace even the best pipetters.

yeah they are great for employment insurance.

How much do a small number of well trained chemists cost to employ? I’d expect north of $100k a pop. Although I guess this doesn’t help if you still need a chemist to interpret the output of the arm.

Indeed, the x-ray diffraction interpretation wasn't completely automated. From the experimental paper: "When the automated refinement gives a poor fit, manual analysis is performed"

I think the novelty here is in the automation of it? If you (or let's be real, some eccentric billionaire) set up 100 of these and hooked them up to run 24/7, they could generate a stream of test results. If you can scale this up maybe you'd hit economies of scale?

Shame there's no eccentric billionaires that love shiny projects with little hope of success. /s

This is already happening all over the world across multiple industries. It's typically called lab-in-the-loop.

I have been involved in projects with eccentric billionaires to build such things. It's challenging to make forward progress in a meaningful way (IE, beyond a press-and-paper prototype), and often the reasons are entirely banal and provincial (many scientists in the field feel threatened by ML and automation; others just don't know how to work in a large-scale environment, others want to come up with the perfect experiment yet never actually run one, and even others want to use the automater as a quick-turn-around, not economy-of-scale tool. Further, just getting the necessary support infra to make the system run well can often be quite challenging.

You say it's challenging, are you implying there are actually any successful instantiations running at scale?