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Equation 5 · Part 2 · AI for Science and Medicine in Practice: An Advanced Technical Guide

Symbol E

EI(x)=E ⁣[max⁡(f(x)−f+, 0)],\mathrm{EI}(x) = \mathbb{E}\!\left[\max(f(x) - f^{+},\ 0)\right],
E\mathbb{E}

What this part means

The expected value operator: the probability-weighted average of the quantity inside its brackets.

Its job in the formula

E appears in the objective or constraint used by the optimization on the right.

The passage around this formula

The acquisition function must trade off predicted value against uncertainty, not just rank by predicted value. A pure exploitation policy — always synthesise the top-ranked candidate — collapses quickly onto whatever region of chemical or materials space the initial training data already covered well, and stops finding anything the model did not already believe. The standard fix is an acquisition function such as expected improvement, which for a candidate x with predictive mean μ(x)\mu(x) , predictive standard deviation σ(x)\sigma(x) , and current best observed value f+f^{+} is EI(x)=E ⁣[max⁡(f(x)−f+, 0)]\mathrm{EI}(x) = \mathbb{E}\!\left[\max(f(x) - f^{+},\ 0)\right]. a quantity that rewards both a high predicted mean and high predictive uncertainty. In practice this…

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Learn the underlying idea

Probability assigns a number from 0 to 1 to an event under a stated model. Zero means impossible within that model; one means certain.

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See this notation across published equations →

Sources cited in the article section

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