Aetheria orchestrates high-throughput DFT, active learning, molecular dynamics, and automated robotic synthesis into a single autonomous pipeline.
Every domain powered by real-time computation. Hover to interact with each simulation.
High-throughput crystal structure generation with automated symmetry analysis. Drag to rotate the lattice.
Classical MD with Lennard-Jones potentials and spring-bond models. Hover to heat up the system.
Kohn-Sham orbital rendering. Move your mouse left/right to switch between s, p, d orbitals live.
GNN surrogate models with Bayesian acquisition. Hover to accelerate the data flow through the network.
Direct hardware integration with Opentrons and Hamilton liquid handlers. Hover to watch the robot arm move.
Electromagnetic field visualization and charged particle dynamics. Move your mouse to apply an external field.
Protein-ligand docking and pharmacophore modeling. Hover to watch the DNA helix unwind and replicate.
Self-directed LLM-powered agents that plan, execute, and iterate on experimental campaigns without human input.
Define complex multi-stage computational workflows in clean Python. Aetheria handles caching, fault tolerance, and automatic cluster dispatch.
import aetheria as ae
@ae.pipeline(target="cloud_gpu")
def optimize_material(structure):
# High-throughput relaxation
relaxed = ae.simulate(
structure,
functional="HSE06",
precision="high"
)
# Store in the data warehouse
ae.store("materials_db", relaxed)
# Trigger robotic synthesis
if relaxed.bandgap > 1.5:
ae.dispatch_robot(relaxed.protocol)
return relaxed
result = optimize_material("CsPbI3")Join the teams already discovering novel materials 10× faster with Aetheria.