Considering there are estimated to be on the order 10^60 "small" organic molecules (molecular weight of under 500g/mol) that are stable under some "reasonable" conditions [0], even that may be a pretty big understatement. Just consider all of the combinations of polymers possible from those molecules. For reference: ~10^80 particles in the observed universe.
And that's ignoring proteins (average molecular weight ~40000g/mol) and many bio-material applications. A strand of DNA has 4^Length permutations.
That just means it is unlikely we would understand other cultures immediately if we could time travel:)
Maybe the difference with materials is that there are almost certainly hidden gems that could improve the lives of humanity, e.g. by helping contain fusion reactors (stars on earth).
High throughput material discovery is one of the biggest fad in academia that has been draining money for decades. They started with high throughput experiments, then combined it with physics based modelling and now ML.
However not a single industrial relevant material has been created in decades but hundreds of millions of dollars have been wasted.
The trouble is most of them cannot be manufactured at scale because they are some crazy metastable state that can only be achieved at lab scale. Further, when manufacturing at scale, materials will have impurities, defects and the properties of such exotic complex oxides are highly sensitive to such changes.
As a result hundreds to thousands of papers are published every year and nothing happens except the professors getting rich and graduate students graduating either unemployed or going to become software engineers. Another reason to remove the incentives to create PhD from slave labour.
I worked on projects in this area and honestly this isn’t far off the mark. Industry is desperate to get new materials to market faster, but after seeing the entire development chain, the scale-up is a far greater bottleneck than the generation of promising leads. You can even see some of it buried at the end of this paper - they’re using some optical property as a surrogate for catalytic activity, acknowledging that
> Catalyst testing for water oxidation is far more expensive than the optical screening from our high throughput workflow, and even though there is no known connection between the optical properties and the catalytic properties, we use the analysis of optical properties to select a small number of compositions for catalyst testing
I’m not saying these high throughput screening techniques don’t have value - they do and they have their place. They get overhyped because software companies see a potential ML application and throw their weight behind it. However the ML-based search algorithm is usually the lowest value-add to the process. Automating the characterization (the part the Caltech people did.. built a custom microscope) is the biggest de-bottleneck in this workflow.
Ofcourse it's my tax dollars that's getting burnt through this useless endeavour. Academic funding must be for bold ideas that are more likely to fail but push the knowledge boundaries further. There is nothing being learnt by mindless number crunching that is done in such work. Infact this is worse than buying lottery tickets hoping to hit a jackpot.
Combining traditionally inorganic material science, e.g. metals or their oxides together with proteins and polysaccharides could produce even more interesting materials with unusual properties, that might find applications in prosthetics, building, and aerospace. See, for example, the idea to use chitin for building Martian bases[1].
> For example, silicon’s crystal structure allows it to be widely used in the semiconductor industry, whereas graphite’s soft, layered structure makes for great pencils.
Having read of lots of exciting uses for graphite, I was not expecting that sentence to end with pencils.
These numbers explode when you consider: https://en.m.wikipedia.org/wiki/High_entropy_alloys