Recent work in asymmetric catalysis demonstrates that accurate prediction of chemical outcomes does not require exhaustive datasets. Instead, models built from carefully selected structural descriptors of transition states and intermediate configurations can reliably predict behaviour in previously unseen systems.
This confirms a central principle of Quantamics: predictability does not emerge from the quantity of information, but from the correct identification of structural relations that determine closure. When configuration is readable, outcome becomes predictable—even in sparse informational environments.
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