Currently working toward this at 38, but my goal is to start building a team of individuals to create a research / design firm that studies symbiotic relationships in nature in order to discover and pair natural additive processes (think spider producing webs as one of these additive process) starting with bespoke pieces such as a spider woven glove.
This would help create buzz and intrigue with the objective to attract top talent and essentially the seed money to self funded a hybrid medusa that is studying "organic 3D printers" with the objective of being the "Manhattan project" size of integrating nature into the manufacturing process.
These aren't protein crystal structures, they are metal-organic frameworks (MOFs), so AlphaFold probably wouldn't work well on these ones.
It would be really interesting to see an equivalent model trained to predict these structures. The physical chemistry of transition metal complexes, especially when multiple metals are in close proximity to each other and connected by shared ligands, is much more complicated than proteins. The reason is because of multireference effects - essentially the quantum entanglement of multiple possible electron configurations. These are exceedingly difficult calculations to perform - common approximations are O(n^8) or worse and require highly specialized knowledge to apply correctly - so an ML model that can efficiently make predictions in this space would be a major transformative breakthrough.
This would help create buzz and intrigue with the objective to attract top talent and essentially the seed money to self funded a hybrid medusa that is studying "organic 3D printers" with the objective of being the "Manhattan project" size of integrating nature into the manufacturing process.