Finding Complex Metal Oxides for Technology Advancement
ai.googleblog.com
ai.googleblog.com
These numbers explode when you consider: https://en.m.wikipedia.org/wiki/High_entropy_alloys
And that's ignoring proteins (average molecular weight ~40000g/mol) and many bio-material applications. A strand of DNA has 4^Length permutations.
[0] https://onlinelibrary.wiley.com/doi/10.1002/(SICI)1098-1128(...
When things get combinatorial I'm not sure percentages really matter
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).
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.
> 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.
Stop doing research in this area?
Which money gets burned? Yours? Or do you think that this money would have been allocated differently/better?
[1] https://phys.org/news/2020-09-chitin-bioinspired-material-to...
Having read of lots of exciting uses for graphite, I was not expecting that sentence to end with pencils.