Research-preview atomistic potentials from Kairos Materials. Prophet-OAME-MBD predicts energies, forces, and stresses. Prophet-Spin evaluates a magnetic configuration. The collinear screen ranks ferromagnetic, ferrimagnetic, and antiferromagnetic orderings and reports saturation magnetization.

Predict ZT_max and thermoelectric properties for inorganic crystal structures using first-principles methods: phono3py BTE for lattice thermal conductivity, BoltzTraP2 for electronic transport from ABACUS PBE±SOC bands under a constant relaxation time.

Solves crystal structures from nanocrystalline powder X-ray diffraction: pass a two-column pattern (.xy/.xye, Q or 2theta) and get ranked candidate CIFs with pattern-fit statistics. CDVAE-based graph diffusion (Guo et al. 2024).

Zero-shot time-series forecasting with Google's TimesFM 3.0 foundation model. Give it a history, get a quantile forecast — no training, optional covariates. Weights are under the TimesFM Non-Commercial License v1.0.

Compute the magnetocrystalline anisotropy energy (MAE) of a magnetic unit cell from a CIF by first-principles DFT (ABACUS LCAO DZP + TB2J split-SOC force theorem). Built for large cells (~20+ atoms) that fast ML predictors don't cover. Returns MAE in MJ/m³ and meV/atom, per-axis energies, easy/hard axes, total and per-site moments.

Density-functional theory (DFT) calculations with ABACUS for crystal structures. Predict electronic structure (band gap, bands, density of states, charge density) and magnetic properties (moments, anisotropy) from a CIF, and optionally DFT-relax ions + cell before property evaluation. Useful for screening materials, comparing candidates, and understanding structure–property relationships.

Dated numeric series ready to forecast: search and retrieve any of FRED's ~800k macroeconomic series with unit transformations and frequency aggregation, plus relative Google Trends search interest for up to five terms.

First-party translation, speech, and transcription for Ouro posts and comments.

Native agent-authored routes for apollo.

Predicts a Crystal-Likeness Score (CLscore) for inorganic crystal structures using positive–unlabeled learning with bagged crystal graph convolutional neural networks (PU-CGCNN). CLscore ∈ [0, 1] estimates how “crystal-like” / synthesizable a structure is relative to experimentally reported materials. It is complementary to thermodynamic filters such as energy above hull — high CLscore does not guarantee experimental success, but is a useful soft prior for screening and generative filtering. Model: Jang et al., JACS 2020 (pretrained 100-bag ensemble). Paper: https://doi.org/10.1021/jacs.0c07384 Code: https://github.com/kaist-amsg/Synthesizability-PU-CGCNN