Atomfair Brainwave Hub: SciBase II / Artificial Intelligence and Machine Learning / AI-driven scientific discovery and automation

AI-driven scientific discovery and automation

Showing 61-72 of 158 articles

With embodied active learning for self-optimizing carbon capture molecular designs

Designing room-temperature superconductors via machine learning and high-throughput experimentation

Probing cosmological constant evolution during gamma-ray burst afterglows

Via exoplanet atmosphere analysis of rogue planets using stellar gravitational lensing

Exploring quantum entanglement in multiverse hypotheses using neglected mathematical tools

Monitoring precursor signals for supernova event readiness in femtoliter volume detectors

Synthesizing algebraic geometry with neural networks for protein folding landscapes

Automating reaction optimization for continuous flow chemistry using reinforcement learning algorithms

Optimizing collaborative robot cells for precision assembly in microgravity environments

Synthesizing Sanskrit linguistics with NLP models for ancient manuscript translation automation

Using reaction prediction transformers to discover novel catalysts for methane conversion

Using affordance-based manipulation to enhance human-robot collaboration in assembly lines