Aetheria orchestrates high-throughput DFT, active learning, molecular dynamics, and automated robotic synthesis into a single autonomous pipeline.
Every domain powered by real-time calculation & interactive physics. Hover & drag to inspect live simulations.
High-throughput crystal structure generation with automated symmetry analysis. Click and drag to rotate the lattice.
Lennard-Jones potentials and spring-bond interaction models. Hover over the box to excite thermal kinetics.
Kohn-Sham wave function orbital rendering. Move cursor horizontally to dynamically phase-switch s, p, and d orbitals.
Surrogate property prediction models with Bayesian acquisition. Hover to accelerate node signal flow.
Direct API integration with Opentrons and Hamilton liquid handling platforms. Hover to observe kinematic arm targeting.
Real-time vector field dynamics and charged particle acceleration. Move cursor to perturb the magnetic field lines.
Pharmacophore optimization and structural biophysics. Hover over the box to unwind and replicate double helix strands.
Self-directed LLM agents planning campaigns, launching DFT jobs, and iterating on experimental loops.
Real-time job scheduling across high-performance compute clusters and synthesis lab automation.
Define multi-stage computational workflows in clean Python. Aetheria handles caching, fault tolerance, cluster scheduling, and hardware execution automatically.
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 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 world-leading research teams discovering novel energy materials and therapeutics 10× faster with Aetheria.