[astro-ph.IM] The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the upcoming Ariel mission.
Machine learning
Posted inAI - Data - Apps - Cybernetics, Atmospheres, Climate, Weather, Biochemistry & Organic Chemistry, Brown Dwarfs, Gas Giants, Nomenclature, Systematics, Natural History, Status Report, Stellar Cartography
Spectral Classification Of Brown Dwarfs Using Machine Learning
Brown dwarfs are compact objects that do not reach temperatures high enough to produce sustained hydrogen fusion. Consequently, they cool over time, gradually evolving through later spectral types.
Posted inAI - Data - Apps - Cybernetics, Analog Studies, Arctic / Antarctic / Alpine, Away Teams & Field Reports, Biochemistry & Organic Chemistry, Biogeochemical Cycles & Geobiology, Biophysics, Biosignatures & Paleobiology, Caves, Craters, Mountains, Subsurface, Lava Tubes, Cryobiology, Desert Worlds, Extremeophiles and Extreme Environments, Fossils & Paleontology, Genomics, Proteomics, Bioinformatics, Habitable Zones, Icy Worlds, Mapping, Geodesy, Cartography, Bathymetry, Nomenclature, Systematics, Natural History, Status Report
Decoding Extremophiles: Insights From Bioinformatics, Machine Learning, And Data-driven Approaches
Life thrives in Earth’s most inhospitable environments, from boiling hydrothermal vents to hypersaline lakes and frozen polar deserts, thanks to the remarkable adaptations of extremophilic microorganisms.
Posted inAI - Data - Apps - Cybernetics, Arctic / Antarctic / Alpine, Away Teams & Field Reports, Biosignatures & Paleobiology, Exploration Gear & Tech, Imaging & Spectroscopy, Meteorites & Asteroids, Planetary Protection & Biosafety, Press Release, Robotics/Rovers/UAVs, Sample Return
A Cloud-Based Tool For Meteorite Recovery Using Drones And Machine Learning
We present a cloud-based tool that uses drones and machine learning to help recover instrumentally observed meteorite falls.
![Overview. (a) Composition of a bacterial cell by weight, with a breakdown of the chemical constituents [21]. About 40% of these compounds are not properly covered in existing datasets. (b) Multi-step workflow used to construct QCell, beginning with the selection of building blocks, followed by conformational sampling and fragment selection, pre-optimization with DFTB+MBD, and finally hybrid PBE0+MBD(-NL) calculations. (c) Coverage of molecular species at the PBE0+MBD(-NL) level of theory, including entries from existing databases and newly generated QCell data for nucleic acid fragments, lipids, sugars, solvated ions, and dimers. -- AI for Science](https://i0.wp.com/astrobiology.com/wp-content/uploads/2026/04/Mapping-The-Molecules-Of-Life.png?resize=1200%2C719&ssl=1)
Posted inAI - Data - Apps - Cybernetics, Astropharmacy / Biotechnology, Biochemistry & Organic Chemistry, Biophysics, Biosignatures & Paleobiology, Genomics, Proteomics, Bioinformatics, Status Report, Tricorders & Sensors
Mapping The Molecules Of Life: Expanding The Quantum-mechanical Foundation For Biomolecular AI
Machine learning force fields (MLFFs) are rapidly transforming molecular simulations by combining the accuracy of quantum mechanics with the speed of classical approaches. However, training reliable MLFFs for biological systems […]
Posted inAI - Data - Apps - Cybernetics, Astrobiology (general), Astrochemistry, Astrogeology, Astronomy & Telescopes, Habitable Zones, Imaging & Spectroscopy, Status Report
Machine Learning As A Transformative Tool for (Exo-)Planetary Science
The exploration of planetary bodies in our Solar system and beyond relies on the processing and interpretation of large, spatio-temporally inconsistent, and heterogeneous datasets.
Posted inAI - Data - Apps - Cybernetics, Biochemistry & Organic Chemistry, Biophysics, Biosignatures & Paleobiology, Genomics, Proteomics, Bioinformatics, Microbiology & Virology, Nanotechnology & SynBio, Origin & Evolution of Life, Status Report
Machine Learning For Evolutionary Genetics And Molecular Evolution
Over the past decade, the rapid expansion of large-scale data and advances in computational power have allowed machine learning (ML), especially deep learning, to reshape many areas of biological research. […]
Posted inBiosignatures & Paleobiology, Extremeophiles and Extreme Environments, Gaia - Planetary Perspectives, Genomics, Proteomics, Bioinformatics, Origin & Evolution of Life, Press Release
Do Extreme Environments Shape Microbial Genomes?
Evolutionary biologists have long known that DNA, the molecule that carries the genetic instructions for the development, functioning, growth and reproduction of all organisms, contains a record of ancestry. Indeed, […]
Posted inAI - Data - Apps - Cybernetics, Conferences and Meetings, Exoplanets, -moons, -comets, Press Release, Video, Audio, Webcasts, Podcasts
NASA Ames AI/ML Seminar Series: Machine Learning for Exoplanet Vetting and Validation
Everyone is invited to the monthly artificial intelligence and machine learning virtual seminar hosted by the Exobiology Branch at the NASA Ames Research Center. Previously recorded seminars can be found […]
Posted inAI - Data - Apps - Cybernetics, Astrochemistry, Biophysics, Biosignatures & Paleobiology, Imaging & Spectroscopy, Interstellar, Status Report
Machine Learning Exploration Of Binding Energy Distributions Of H2O At Astrochemically Relevant Dust Grain Surfaces
Binding energies (BEs) of adsorbates on interstellar dust grains critically control adsorption, desorption, diffusion, and surface reactivity, and therefore strongly influence astrochemical models of star- and planet-forming regions.