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    AI Is Unsafe Until It Learns to Stabilize

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    Old-school spycraft meets AI: How digital surveillance is remaking espionage

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Teaching AI to see 3D crystal structures—and predict their properties

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MIT researchers unveiled an AI system that fuses 3D crystal structures with natural-language “synthetic narratives” to overcome text-only models’ blind spots in materials science. The multimodal approach, detailed in Nature Communications, better linked geometry to properties and processing, and surfaced promising candidates for solid-state lithium-ion battery electrolytes—an area of high commercial interest. Backed by DTRA, DARPA and Korea’s NRF, the work draws comparisons to AlphaFold for its potential to accelerate discovery across catalysts, semiconductors and energy storage. The promise is speed and breadth; the hurdles are industrial validation and continued expansion of high-quality structural data.

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Crystal Graph Convolutional Neural Networks for accurate, interpretable prediction of material properties
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
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