It is unique because everything is quantized. I've never used these tools but I am assuming you could give it some level of randomness but as someone who has performed and recorded a non-quantized performance is not random. So sure, it's super easy to quantize in your daw but it is a tool to be applied when needed, not something that is on all the time by default.
yes exactly, and when I say "quantization of every dimension of composition" I mean an application of quantization to every aspect of composition not just pitch and rhythm.
Quantization and repetition are what some genres depend on. It won't be the right instrument for a Rock ballad, but for a Techno track you need this kind of "everything being quantized". That said, in loopmaster you can add swing and noise to the note offsets to humanize a sequence, a lot is left to the imagination and ability of the creator.
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.
