What the AI was for
Experimental design
Choosing which experiment or measurement to perform next.
21articles
6Structural biology
12Materials & chemistry
3Astronomy
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Peptides designed by simulation and machine learning sit at condensate surfaces
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Machine-learned potentials trained to simulate copper ion conductor Cu7PS6 faster
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Robot laser-heats thin films while software picks each next heating condition
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Neural network potentials trained by repeatedly checking where models disagree on hydrogen sticking to copper
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Machine learning picked catalyst recipes for a loop of 44 lab cycles
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Teaching protein language models to rank mutants from a few dozen lab measurements
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Pretrained potentials give a structure search a head start in finding atomic arrangements
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Model predicts how fast an enzyme works on a substrate, with uncertainty attached
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Ultrasound-treated clay bleaches cooking oil, with machine learning picking the settings
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Statistical model of 5,441 perovskite solar cells predicts efficiency and suggests recipes
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Software spotted a nearby supernova and booked a telescope within minutes
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Algorithm picks starting powders and temperatures for making inorganic materials
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Machine learning picked which engineered enzymes to build for fatty alcohol production
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Telescope targets chosen nightly by a classifier learning from its own spectra
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Neural network picks bright exploding stars from sky survey alerts and books telescope time
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Machine learning picked which shape-memory alloys to make next, round by round
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Machine-learned force model simulates silicon-oxygen structures from glass surfaces to monoxide grains
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A learning algorithm ran a synchrotron beamline to find a phase-change material
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Robotic lab with machine learning proposes and runs 353 inorganic synthesis experiments
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Gaussian process models pick light-sensitive ion channels that reach mammalian cell membranes
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