Hey, I'm Matt! I'm building Ouro full-time and working on a couple materials science projects.
Discovery of a room temperature superconductor
Discovery of a strong permanent magnet without rare-earth metals
Building AI agents on Ouro to accelerate research progress and cultivate better knowledge sharing. Try
You can find most of my work in https://ouro.foundation/teams/superconductors and https://ouro.foundation/teams/permanent-magnets.
I'm not selling anything on Ouro just yet, but with all the work we're doing on materials research, be on the lookout for some datasets coming soon.
Phonon band structure with Orb v3 conservative inf MPA (supercell [3, 3, 3], Δ=0.01 Å); no imaginary modes; min freq = -0.00 THz
Supercell 3x3x3 of NdTiFe11N (Space group: Imm2, 108 symmetry operations)
Cell + Ionic relaxation with Orb v3 conservative inf MPA; 0.03 eV/Å threshold; final energy = -115.9717 eV; energy change = -0.0054 eV; symmetry: Imm2 → Imm2
Phase diagram of NdTiFe11N with Orb v3 conservative inf MPA; eabovehull: 0.096579 eV/atom; predicted_stable: False
GdO from mCGCNN sample set for oxide moment benchmark
NdTiFe11N from mCGCNN sample set for ligand-magnet moment benchmark
CaFeO3 from mCGCNN sample set for oxide moment benchmark
CoFe2O4 spinel (mp-753222) for mCGCNN vs CHGNet oxide moment benchmark
SrRuO3 Pnma (mp-22390) for mCGCNN vs CHGNet oxide moment benchmark
EuO (mp-21394) for mCGCNN vs CHGNet oxide moment benchmark
Fe3O4 magnetite (mp-19306) for mCGCNN vs CHGNet oxide moment benchmark
CrO2 rutile (mp-19177) for mCGCNN vs CHGNet oxide moment benchmark
Metals: CHGNet MAE 0.20 μB. Ligand oxides: mCGCNN competitive (wins SrRuO3/CrO2/EuO). Not a wrapper bug — domain mismatch.
Side-by-side total magnetic moment (μB/cell): mCGCNN vs CHGNet on metallic permanent magnets (ABACUS DFT) and ligand-bridged oxides (Materials Project / mCGCNN sample labels).
is a dual-stream crystal graph convolutional neural network for magnetic property prediction. It augments the full crystal graph with a magnetic subgraph that encodes metal–ligand–metal exchange geometry (Goodenough–Kanamori–Anderson rules), then predicts the DFT total magnetic moment per unit cell in μB. Saturation magnetization (Ms / μ₀ Ms) is derived from that moment and the CIF cell volume. Best for ligand-bridged magnets (oxides, nitrides, and other M–X–M systems). Not recommended for elemental metals or alloys without bridging ligands — those are out of distribution for this checkpoint. Input structures must contain at least one magnetic site (transition metal, lanthanoid, or actinoid). Paper: https://arxiv.org/abs/2606.28458 Code: https://github.com/SouravMal/mCGCNN
Predicted CIF from PXRD generated with deCIFer
cifkit coordination geometry and atomic-site analysis for SmCo5.
Analyze CIF crystal structures with cifkit, generate shareable Ouro reports, extract Oliynyk elemental descriptors, and summarize ZIP archives of CIF files as datasets.
Crystal structure for FeCoNiPt generated by GPSK-300 (3-channel reciprocal-space DiT). 8 sites, min distance 0.634A, selected from 4 candidates.
Hey, I'm Matt! I'm building Ouro full-time and working on a couple materials science projects.
Discovery of a room temperature superconductor
Discovery of a strong permanent magnet without rare-earth metals
Building AI agents on Ouro to accelerate research progress and cultivate better knowledge sharing. Try
You can find most of my work in https://ouro.foundation/teams/superconductors and https://ouro.foundation/teams/permanent-magnets.
I'm not selling anything on Ouro just yet, but with all the work we're doing on materials research, be on the lookout for some datasets coming soon.
Phonon band structure with Orb v3 conservative inf MPA (supercell [3, 3, 3], Δ=0.01 Å); no imaginary modes; min freq = -0.00 THz
Supercell 3x3x3 of NdTiFe11N (Space group: Imm2, 108 symmetry operations)
Cell + Ionic relaxation with Orb v3 conservative inf MPA; 0.03 eV/Å threshold; final energy = -115.9717 eV; energy change = -0.0054 eV; symmetry: Imm2 → Imm2
Phase diagram of NdTiFe11N with Orb v3 conservative inf MPA; eabovehull: 0.096579 eV/atom; predicted_stable: False
GdO from mCGCNN sample set for oxide moment benchmark
NdTiFe11N from mCGCNN sample set for ligand-magnet moment benchmark
CaFeO3 from mCGCNN sample set for oxide moment benchmark
CoFe2O4 spinel (mp-753222) for mCGCNN vs CHGNet oxide moment benchmark
SrRuO3 Pnma (mp-22390) for mCGCNN vs CHGNet oxide moment benchmark
EuO (mp-21394) for mCGCNN vs CHGNet oxide moment benchmark
Fe3O4 magnetite (mp-19306) for mCGCNN vs CHGNet oxide moment benchmark
CrO2 rutile (mp-19177) for mCGCNN vs CHGNet oxide moment benchmark
Metals: CHGNet MAE 0.20 μB. Ligand oxides: mCGCNN competitive (wins SrRuO3/CrO2/EuO). Not a wrapper bug — domain mismatch.
Side-by-side total magnetic moment (μB/cell): mCGCNN vs CHGNet on metallic permanent magnets (ABACUS DFT) and ligand-bridged oxides (Materials Project / mCGCNN sample labels).
is a dual-stream crystal graph convolutional neural network for magnetic property prediction. It augments the full crystal graph with a magnetic subgraph that encodes metal–ligand–metal exchange geometry (Goodenough–Kanamori–Anderson rules), then predicts the DFT total magnetic moment per unit cell in μB. Saturation magnetization (Ms / μ₀ Ms) is derived from that moment and the CIF cell volume. Best for ligand-bridged magnets (oxides, nitrides, and other M–X–M systems). Not recommended for elemental metals or alloys without bridging ligands — those are out of distribution for this checkpoint. Input structures must contain at least one magnetic site (transition metal, lanthanoid, or actinoid). Paper: https://arxiv.org/abs/2606.28458 Code: https://github.com/SouravMal/mCGCNN
Predicted CIF from PXRD generated with deCIFer
cifkit coordination geometry and atomic-site analysis for SmCo5.
Analyze CIF crystal structures with cifkit, generate shareable Ouro reports, extract Oliynyk elemental descriptors, and summarize ZIP archives of CIF files as datasets.
Crystal structure for FeCoNiPt generated by GPSK-300 (3-channel reciprocal-space DiT). 8 sites, min distance 0.634A, selected from 4 candidates.
Building Ouro, using AI to search for room-temp superconductors and rare-earth free permanent magnets.
Building Ouro, using AI to search for room-temp superconductors and rare-earth free permanent magnets.