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Published equation contexts

P(deployed)=P(stable)×P(synthesizable∣stable)×P(scalable∣synthesizable)×P(supply-chain viable∣scalable)P(\text{deployed}) = P(\text{stable}) \times P(\text{synthesizable} \mid \text{stable}) \times P(\text{scalable} \mid \text{synthesizable}) \times P(\text{supply-chain viable} \mid \text{scalable})

Why this formula appears here

The evolutionary framing does real analytic work once the pipeline above is treated as a selection funnel rather than a single machine. A useful way to write down what a candidate has to survive to become a deployed material is a chain of conditional probabilities, each stage filtering out most of what entered it: P(deployed)=P(stable)×P(synthesizable∣stable)×P(scalable∣synthesizable)×P(supply-chain viable∣scalable)P(\text{deployed}) = P(\text{stable}) \times P(\text{synthesizable} \mid \text{stable}) \times P(\text{scalable} \mid \text{synthesizable}) \times P(\text{supply-chain viable} \mid \text{scalable}). GNoME and MatterGen attack only the first term, and the 2023-2024 dispute is essentially an argument about whether even that first term was measured honestly. But the more interesting evolutionary claim in this article’s premise is what happens to the later terms once the first one becomes cheap to compute at scale: selection pressure does not disappear, it…

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Published contexts (1)

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P(deployed)=P(stable)×P(synthesizable∣stable)×P(scalable∣synthesizable)×P(supply-chain viable∣scalable)P(\text{deployed}) = P(\text{stable}) \times P(\text{synthesizable} \mid \text{stable}) \times P(\text{scalable} \mid \text{synthesizable}) \times P(\text{supply-chain viable} \mid \text{scalable})

Equation 1 · Technological Evolution

Materials Discovery Is Becoming a Breeding Program: Selection, Synthesis, and the GNoME Dispute

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

The evolutionary framing does real analytic work once the pipeline above is treated as a selection funnel rather than a single machine. A useful way to write down what a candidate has to survive to become a deployed material is a chain of conditional probabilities, each stage filtering out most of what entered it: P(deployed)=P(stable)×P(synthesizable∣stable)×P(scalable∣synthesizable)×P(supply-chain viable∣scalable)P(\text{deployed}) = P(\text{stable}) \times P(\text{synthesizable} \mid \text{stable}) \times P(\text{scalable} \mid \text{synthesizable}) \times P(\text{supply-chain viable} \mid \text{scalable}). GNoME and MatterGen attack only the first term, and the 2023-2024 dispute is essentially an argument about whether even that first term was measured honestly. But the more interesting evolutionary claim in this article’s premise is what happens to the later terms once the first one becomes cheap to compute at scale: selection pressure does not disappear, it…

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