What the AI was for
Candidate generation
Proposing new objects for later evaluation.
21articles
8Structural biology
11Materials & chemistry
2Astronomy
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Language models suggest ingredients and firing temperatures for inorganic materials recipes
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Neural network designs five hard metallic glasses, all confirmed by experiment
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Neural networks pick amino acid sequences for short peptides in protein binding sites
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Neural networks predict how zeolites take up carbon dioxide from structure alone
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Peptides designed by simulation and machine learning sit at condensate surfaces
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Repeating-coil proteins designed from random sequences using AlphaFold2 in an evolution loop
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Diffusion model generates synthetic images of the Sun sorted by flare strength
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Benchmarking AI tools that predict and design peptides for cell-surface receptors
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Diffusion model generates glass and silicon structures to order from target properties
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Random atoms settle into molecules and crystals on a learned energy surface
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Zero-padding lets one neural network encode crystals with differing element counts
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Two protein design models redesigned T cell receptor contact points on solved structures
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A compact neural network learns to generate shape-shifting protein structures
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Neural network scores candidate composite microstructures in a strength–toughness design loop
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Machine learning predicts marine steel corrosion from seawater conditions and alloy make-up
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Deep-learning model designs petrol blends from octane and soot targets
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Genetic algorithm trims chemical reaction lists for hot Jupiter atmosphere models
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Statistical model of 5,441 perovskite solar cells predicts efficiency and suggests recipes
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Neural network writes amino acid sequences to fit fixed protein backbones
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Robotic lab with machine learning proposes and runs 353 inorganic synthesis experiments
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