What the AI was for
Property prediction
Predicting a measurable property of an existing object.
aix-00218
Sparse regression fits crystal force constants to model heat flow and vibrations
aix-00219
Neural network supplies the sideways flows needed to measure the quiet Sun's magnetic energy
aix-00220
Neural network predicts how titanium alloy grains round off during annealing
aix-00221
Neural network measures rotation periods for Kepler's main-sequence stars
aix-00222
Neural networks trained on molecule pairs predict light absorption in stacks of fifty
aix-00223
Neural networks weigh cluster X-ray maps to estimate galaxy cluster masses
aix-00216
Neural network models simulate how lead titanate loses its electrical polarisation on heating
aix-00213
Neural networks predict how much ultrasound energy drives magnesia dissolution
aix-00254
Neural networks read a fast radio burst's dispersion straight from its spectrum
aix-00210
Language models suggest ingredients and firing temperatures for inorganic materials recipes
aix-00209
Neural network turns Kaguya camera images into global maps of lunar surface chemistry
aix-00208
Neural network predicts atomic charges from bond lists, without atomic coordinates
aix-00206
Neural network designs five hard metallic glasses, all confirmed by experiment
aix-00205
Machine learning fitted to flexible molecules predicts oxidation potentials and hydration energies
aix-00159
Machine learning assigns atomic charges to size up polarisation in bismuth vanadate conductors
aix-00158
Structure predictions place an uncharacterised human protein in the BRICHOS family
aix-00157
Microscopy and a trained classifier map which E. coli proteins clump together
aix-00155
Neural network predicts crystal density of candidate explosives from molecular structure
aix-00154
AlphaFold models used to map membrane-crossing segments in human proteins
aix-00150
Deep learning sorts crystal structures for single-molecule magnet behaviour
aix-00147
Testing whether symmetry-aware neural networks can tell apart atomic arrangements in perovskites
aix-00160
Neural networks predict how zeolites take up carbon dioxide from structure alone
aix-00163
Peptides designed by simulation and machine learning sit at condensate surfaces
aix-00169
AlphaFold's confidence scores used to predict which disordered protein segments bind LC8
aix-00170
Machine learning predicts strength of PLA plastic filled with boron nitride flakes
aix-00172
Machine learning tested against three definitions of protein shape-shifting
aix-00146
Machine learning ranks stable materials by predicted superconducting temperature
aix-00139
Robot laser-heats thin films while software picks each next heating condition
aix-00173
Machine learning predicts nanofibre thickness from electrospinning settings, tested against new scaffolds
aix-00178
Machine learning picked catalyst recipes for a loop of 44 lab cycles
aix-00133
Regression models predict how graphene loading changes aluminium's electron emission
aix-00132
Five codes measured galaxy shapes in simulated Euclid images, one using neural networks
aix-00131
Machine-learned potential predicts how chromium sulfide layers rearrange during exfoliation
aix-00179
Neural network predicts protein pair binding strength from simplified molecular models
aix-00180
Teaching protein language models to rank mutants from a few dozen lab measurements
aix-00183
Neural network predicts nanostructure optics to design a two-colour laser collimator
aix-00186
Language model trained on protein sequences predicts shape and stability of disordered proteins
aix-00187
Machine-learned force field used to model two crystal forms of formamide
aix-00191
Neural network stands in for slow spectral modelling to read 64 stars' chemistry
aix-00129
Neural networks sort supernovae and estimate their distances from brightness alone
aix-00124
Software predictors compare protein floppiness across retinal disease protein sets
aix-00123
Neural networks swap places with black hole image simulations, both directions
aix-00117
Deep learning model predicts polymer heat-softening across 48,208 designed candidates
aix-00114
A compact way to describe molecules speeds up machine learning of their properties
aix-00111
Zero-padding lets one neural network encode crystals with differing element counts
aix-00110
Deep learning trained on robotic peptide mapping predicts where antibody proteins degrade
aix-00108
Oxidation potentials for 15,238 molecules assembled from quantum calculations and lab measurements
aix-00103
Neural network predicts yield of chitosan nanoparticles grown with olive leaf extract
aix-00101
Simulations map three sodium–bismuth compounds, then models predict their heat-to-electricity performance
aix-00099
Weighing the Milky Way's disk reveals a mass excess along the Local Arm
aix-00097
Neural networks speed up forecasts of what gravitational wave detections LISA will catch
aix-00095
Neural networks estimate dark matter halo masses from a galaxy's neighbours
aix-00090
Machine learning reads planet-forming disk masses from archival ALMA observations
aix-00089
Neural networks trained on atomic surroundings score how mutations change proteins
aix-00088
Neural network retrained to predict how crystals absorb light, from few examples
aix-00084
Neural networks read simulated galaxy cluster catalogues to estimate cosmological parameters
aix-00080
Machine learning sifts three-colour infrared survey data for cold brown dwarfs
aix-00077
Neural networks sift 812,118 quasar spectra for hidden gravitational lenses
aix-00074
Software predictions map floppy stretches of touch-sensing PIEZO channels
aix-00071
Simulations map how surface atoms rearrange in alloys when molecules attach
aix-00070
Neural network scores candidate composite microstructures in a strength–toughness design loop
aix-00069
Neural networks rebuild cosmic expansion history to calibrate gamma-ray burst energies
aix-00068
Machine learning predicts marine steel corrosion from seawater conditions and alloy make-up
aix-00065
Model predicts how fast an enzyme works on a substrate, with uncertainty attached
aix-00063
Adding a term for the unfolded protein sharpens AI stability predictions
aix-00058
Deep-learning model designs petrol blends from octane and soot targets
aix-00056
Deep learning tool predicts how single mutations weaken protein partnerships
aix-00051
Ultrasound-treated clay bleaches cooking oil, with machine learning picking the settings
aix-00046
Two neural networks predict how heat shrinks the band gap of silver-based crystals
aix-00045
Neural network reads simulated quasar light curves to estimate black hole properties
aix-00044
Machine-learnt electron densities drive molecular dynamics of a gold–saltwater interface
aix-00040
Statistical model of 5,441 perovskite solar cells predicts efficiency and suggests recipes
aix-00035
GPT-4 reads band gap values from paper sentences to train better predictors
aix-00033
Neural network built from physics equations extracts solder deformation coefficients
aix-00024
Machine learning picked which engineered enzymes to build for fatty alcohol production
aix-00021
Neural network reads a million star spectra to measure temperature and chemistry
aix-00016
Machine learning picked which shape-memory alloys to make next, round by round
aix-00015
Transformer model predicts how gas-storing crystals take up gases, replacing slow simulations
aix-00011
A learning algorithm ran a synchrotron beamline to find a phase-change material
aix-00010
Robotic lab with machine learning proposes and runs 353 inorganic synthesis experiments
aix-00009
Gaussian process models pick light-sensitive ion channels that reach mammalian cell membranes
aix-00007
Neural network predicts protein atom positions from amino acid sequence alone
aix-00004
Robot lab and learning agent redesign an enzyme to survive higher temperatures
aix-00002
Deep learning turns raw NMR spectra into protein structures without human intervention
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