TY - JOUR
T1 - The Attention Economy of Materials AI: How Model Focus Shapes Scientific Attention Allocation
AU - Michael Turner
AU - Sophia Nguyen
AU - David Clark
AU - Emma Wilson
JF - Journal of Artificial Intelligence for Materials Science
JO - J. Artif. Intell. Mater. Sci.
SN - 3149-8957
Y1 - 2022
VL - 1
IS - 1
SP - 96
N2 - In the expanding domain of artificial intelligence applied to materials science, computational models do not merely predict properties or accelerate screening; they function as subtle but powerful mechanisms that allocate finite scientific attention across an effectively infinite chemical space. By prioritizing certain compositional regions, structural motifs, or property axes while de-emphasizing others, these systems implicitly decide which questions will be asked, which hypotheses will be tested, and which materials classes will receive downstream experimental or theoretical investment. This position paper argues that Materials AI operates as an attention-allocation infrastructure whose architectural choices reshape the trajectory of discovery itself, transforming what was once an open-ended scientific exploration into a directed economy of focus. Drawing on the well-established “attention economy” metaphor from information systems and cognitive science, we introduce the parallel concept of scientific attention capital—the limited pool of researcher time, funding, instrumentation access, and collective curiosity that models now mediate and, in many cases, ration. Rather than viewing model-induced focus as a neutral technical artifact, we distinguish productive attention (focused investment that yields rapid, high-impact advances in targeted domains) from pathological attention (self-reinforcing loops that create blind spots, reward hacking, and representational injustice). The perspective developed here suggests that recognizing Materials AI as an attention-shaping force carries immediate implications for how the community designs and audits. It deploys these systems if the goal is to preserve the generative openness that has historically driven materials innovation. Ultimately, treating attention allocation as an explicit design variable rather than an incidental byproduct offers a conceptual framework for ensuring that the next generation of Materials AI expands, rather than contracts, the horizons of scientific possibility.
UR - https://iamrp.net/f182729739
ER -