AI Capabilities
AI that generates, screens, and hunts for new materials
Three research papers and one autonomous search system — here's what each one does.
149,419
Candidates generated
8,800
Flagged likely metallic
3
Published research methods
MOF Generation
A fine-tuned potential drives site-substitution search across QMOF prototypes.
Metallic Screening
A SchNet regressor prioritizes candidates before anyone runs DFT.
Agentic Search
Autonomous agents propose, test, and refine candidates on their own.
Qubit Characterization
A CNN reads spectroscopy images to extract two-level-system defect parameters.
Paper 1
Generating magnetic metal-organic frameworks
Metal-organic frameworks (MOFs) could replace rare-earth magnets, but their large unit cells make first-principles simulation too slow to search at scale. We fine-tuned a machine-learned interatomic potential (CHGNet) on 15,000+ first-principles computations from our Database, then used it to drive a site-substitution search across structural prototypes from the QMOF database — generating novel, highly magnetic MOF candidates far faster than DFT alone.
15,000+
DFT computations used to fine-tune CHGNet
149,419
Novel magnetic MOF candidates generated
12,500
DFT band gaps used to train the SchNet regressor
8,800
(5.9%)
Candidates flagged as likely metallic
Paper 2
Screening for metallic character
A magnetic MOF is only useful for spintronics if it also conducts. Generating candidates is one problem; knowing which ones are metallic is another. We screened all 149,419 candidates from the companion generation study with a SchNet regressor trained on 12,500 DFT band gaps, flagging 8,800 (5.9%) as likely metallic — a prioritization tool that narrows the field before anyone runs an expensive DFT calculation.
Agentic AI
An autonomous system that searches for materials on its own
We run agentic AI — autonomous systems that explore, act, and learn on their own — to search for new materials. Point it at a target property, and a swarm of AI agents proposes, tests, and refines candidates on its own. Today that loop is focused on finding a rare-earth-free permanent magnet, but the same system adapts to whatever material problem you bring us.
Every candidate still has to earn its place — validated against real physics before it's ever considered real.
Paper 3
Characterizing qubit defects with machine learning
Superconducting qubits lose coherence to two-level system (TLS) defects buried in their building blocks, and finding them one by one with traditional fitting is slow — especially across a chip with hundreds of qubits. We trained a ResNet-based convolutional neural network on simulated two-tone spectroscopy images to read off all four TLS parameters — frequency, coupling strength, and both decay times — directly from the image, outperforming standard perturbation-theory fitting and accelerating the loop of designing better quantum chips.
4
TLS parameters predicted per scan — frequency, coupling, and both decay times
ResNet CNN
Trained on simulated two-tone spectroscopy images
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