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.
The accurate modeling of spin-orbit coupling (SOC) effects in diverse complex systems remains a significant challenge due to the high computational demands of density functional theory (DFT) and the limited transferability of existing machine-learning frameworks. This study addresses these limitations by introducing Uni-HamGNN, a universal SOC Hamiltonian graph neural network that is applicable across the periodic table. By decomposing the SOC Hamiltonian into spin-independent and SOC correction terms, our approach preserves SU(2) symmetry while significantly reducing parameter requirements. Based on this decomposition, we propose a delta-learning strategy to separately fit the two components, thereby addressing the training difficulties caused by magnitude discrepancies between them and enabling efficient training. The model achieves remarkable accuracy (mean absolute error of 0.0025 meV for the SOC-related component) and demonstrates broad applicability through high-throughput screening of the GNoME dataset for topological insulators, as well as precise predictions for 2D valleytronic materials and transition metal dichalcogenide (TMD) heterostructures. This breakthrough eliminates the need for system-specific retraining and costly SOC-DFT calculations, paving the way for rapid discovery of quantum materials. https://arxiv.org/abs/2504.19586
~1900 materials collected from NovoMag and Novamag datasets, cleaned CSV with CIF file and MAE value
Like our work on Curie temperature, the effort here is to build a machine learning model that can take a crystal structure and predict its magnetocrystalline anisotropy energy. Relevant for permanent
This paper describes the open Novamag database that has been developed for the design of novel Rare-Earth free/lean permanent magnets. The database software technologies, its friendly graphical user interface, advanced search tools and available data are explained in detail. Following the philosophy and standards of Materials Genome Initiative, it contains significant results of novel magnetic phases with high magnetocrystalline anisotropy obtained by three computational high-throughput screening approaches based on a crystal structure prediction method using an Adaptive Genetic Algorithm, tetragonally distortion of cubic phases and tuning known phases by doping. https://arxiv.org/abs/1902.05241
Working on cleaning the data we have available and seeing what we've got for a MAE prediction model. This resource was nice and had all the raw files uploaded so that you can process them yourself and
We've heard the government talking recently about "growing our way out" of the debt we're in. https://x.com/SecScottBessent/status/1925910800394232082 No one really knows what "growing our way out" me
Also known as the Magnetic Materials Database. I came to this database looking for magnetocrystalline anisotropy energy data for permanent magnet design. After scraping the data from the app, which is
Came across this dataset of thermolectric data while searching for some permanent magnet data. They use LLMs to parse papers and extract a structured database. https://arxiv.org/abs/2501.00564 From th
We're setting MAE as our next predictive model target. So far we have Curie temperature and magnetic moment. MAE makes sense as a next step because understanding this value is absolutely essential to
This dataset includes 3819 materials scraped from https://magmat.herokuapp.com/, the Magnetic Materials Database. See https://www.novomag.physics.iastate.edu/structure-database for citations and more resources. I've cleaned the dataset to include the available magnetic materials (in CIF format) and their properties: magnetic_ordering, total_magnetic_moment [μ_B/cell], averaged_magnetic_moment [μ_B/atom], magnetic_polarization [T], formation_energy_(vs._elemental_phases) [meV/atom], formation_energy_above_hull [meV/atom], magnetic_curie_temperature [K], magnetic_anisotropy_constant,_k_^a-c [MJ/m^3], magnetic_easy_axis, magnetic_hardness_parameter,_κ, magnetic_anisotropy_constant,_k_^b-c [MJ/m^3], magnetic_anisotropy_constant,_k_^b-a [MJ/m^3], magnetic_anisotropy_constant,_k_^d-a [MJ/m^3]
: A Computational High-Throughput Study Lorenzo A. Mariano, Vu Ha Anh Nguyen, Valerio Briganti, and Alessandro Lunghi Journal of the American Chemical Society 2024 146 (49), 34158-34166 DOI: 10.1021/jacs.4c14076
About 250mb when unzipped. Columns are mp-id and Orb v2 features of the material at ground state. Generated with https://github.com/ourofoundation/materials/blob/main/experiments/magnetism/dim-red/features_dataset.py
Big updates to share with everyone:
I'm integrating Bitcoin into Ouro today. Stripe has been okay but I've had way too many problems caused by the fiat money system. More on that later. spark.money is the way.
Last week we introduced a few new routes to the Materials Science API from . This work is part of a broader effort to create a suite of tools that eventually can be commanded by an AI agent for automa
Hey everyone, quick update on what I've been working on this month. April 2025 marks one year of working full-time on Ouro! We made several important improvements to enhance your experience:
, a ferrimagnetic material from Materials Project. https://next-gen.materialsproject.org/materials/mp-21666
CIF file for ZrFe12Si2B, a ferrimagnetic materials from Materials Project. https://next-gen.materialsproject.org/materials/mp-653838
This dataset has a set of 34,000 ferro/ferrimagnetic materials from Materials Project, their formula, if they include rare earth elements, magnetic moment, volume, magnetic density, a predicted Curie temperature, and cosine distances to some known permanent magnets like NdFeB. Distances are based on a 256 dimension embedding from Orb v2 latent space.