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@mmoderwell

Building Ouro, using AI to search for room-temp superconductors and rare-earth free permanent magnets.

2430 XPLevel 25
12 followers16 following
1.4K files5 datasets8 services148 posts

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Activity Feed

  1. Before the competition officially starts, I love to get some of the existing AI models out there on Ouro. Check out the APIs section (upside-down triangle) on the sidebar to see what's already been ad

    post
    3d
  2. Just added a protein visualization to Ouro. Right now it only supports .pdb files because .cif files would clash with the platform's crystal viewer. You can still upload any kind of file you want, but

    post
    3d
  3. 2VSM

    .pdb file

    Nipah virus attachment glycoprotein in complex with human cell surface receptor ephrinB2

    3d
  4. Hey everyone, welcome to the #nipah-binder-competition team. You're in the right place if you're interested in applying AI to antibody/drug discovery. The purpose of this space if to:

    post
    3d
  5. RDF and coordination number plots of CuNi crystal after equilibration melt for 10ps at 1800 K

    Image file

    This asset shows two plots for a CuNi crystal after a 10 picosecond melt equilibration at 1800 K. The left plot is the total radial distribution function (RDF) versus distance, with a strong first peak near 2 Å and several smaller peaks up to about 8–9 Å, suggesting some remaining order from the original lattice. The right plot shows the coordination number (CN) as a function of distance, which increases gradually and reaches around 350 by 10 Å. The note indicates that even at about 9 Å away, there is still a signal of another atom, meaning remnants of the supercell lattice persist in the melted state.

    8d
  6. Welcome Home

    post

    I'm excited to share a new page I've been building out this week. You may have already seen it, as it's the first page you be redirected to after sign in.

    9d
  7. This post explores ideas about AI and how it might change human work and purpose. It mentions starting a small philosophy discussion group to talk about big questions like meaning, usefulness, and how technology affects society. The writer references the book Courage to Be Disliked and Adlerian psychology, noting a common claim that happiness comes from being useful to others. They also offer a personal take that this may not be the only source of happiness. The central question asks what could happen if people feel they are no longer useful to each other or cannot be. It’s a thoughtful look at consequences of automation and the search for meaning in a changing world. Keywords: AI, philosophy, Adlerian psychology, Courage to Be Disliked, usefulness, happiness, labor, future.

    post
    11d
  8. Simulating Metallic Glass Formation with Orb-v3

    post

    is a post about running molecular dynamics simulations to study how a Cu-Zr alloy forms a metallic glass. The author uses a 64% Cu and 36% Zr composition, an (10,10,10) supercell, and the orb-v3-direct-20-omat calculator to push speed and scale. The workflow includes equilibrating a melted alloy at high temperature, then rapid quenching from 2000 K to 300 K at various rates to compare glass formation versus crystallization. The write-up explains key concepts like what glass is in atomic terms, the difference between crystalline order and amorphous structure, and how RDF and coordination numbers help analyze results. It also notes the challenges of achieving crystallization in MD due to time scales and suggests exploring different cooling rates and compositions in future runs. The post includes example data and 3D visualization references to support the findings.

    11d
  9. Figure 1 from "Orb-v3" paper

    Image file

    The Pareto frontier for a range of universal Machine Learning Interatomic Potentials. The 𝐾𝑆𝑅𝑀𝐸 metric assesses a model’s ability to predict thermal conductivity via the Wigner formulation of heat transport and requires accurate geometry optimizations as well as second and third order derivatives of the PES (computed via finite differences). The y-axis measure a model’s forward passes per second on a dense periodic system of 1000 atoms, disregarding graph construction time, measured on a NVIDIA H200. Point sizes represent max GPU memory usage. Y-axis jitter (+/- 5 steps/second) has been applied to allow visualization of overlapping points. Model families include a range of specific models with broadly the same architecture, but may be different sizes or trained on different datasets.

    12d
  10. Meso-scale All-atom Simulations

    post

    Meso-scale all-atom simulations with Orb-v3 open a new frontier in materials science and chemistry. This post discusses using ASE for molecular dynamics on GPUs (A100, H100, H200) via Modal, enabling larger, more affordable simulations than traditional DFT. It highlights metallic glass formation, crystallization, annealing, and emergent phenomena that arise from thousands of atoms. The focus is on a breakthrough in non-conservative architectures that balance memory use and speed, making complex systems feasible to study.

    12d
  11. Orb-v3 paper

    PDF file

    The authors introduce Orb-v3, the next generation of the Orb family of universal interatomic potentials. Models in this family expand the performance-speed-memory Pareto frontier, offering near SoTA performance across a range of evaluations with a ≥ 10× reduction in latency and ≥ 8× reduction in memory. Their experiments systematically traverse this frontier, charting the trade-off induced by roto-equivariance, conservatism and graph sparsity. Contrary to recent literature, they find that non-equivariant, non-conservative architectures can accurately model physical properties, including those which require higher-order derivatives of the potential energy surface.

    13d
  12. The Bitter Lesson

    PDF file

    Paper by Rich Sutton

    14d
  13. MAE predictor failure

    post

    A post about trying to use HamGNN with TB2J to forecast magnetocrystalline anisotropy energy, only to find the pre-trained model lacks the needed physics. The main gap is the absence of spin-polarization in H0, making the model better suited for SOC in non-magnetic materials, not for magnetic predictions. Potential outputs still relevant to SOC include band structure corrections, topological invariants, spin textures in k-space, orbital angular momentum, spin Hall conductivity, g-factors, effective masses, and optical properties. The use case focuses on non-magnetic materials and topological insulators without magnetism. Next steps involve exploring new Hamiltonian models like DeepH-pack and MACE-H, noting they lack pre-trained models. The plan is to gather consistent data, ensure SOC and spin-polarization, and align data sources from the same DFT software. Links: https://github.com/mzjb/DeepH-pack, https://github.com/maurergroup/MACE-H.

    20d
  14. Revisiting MAE datasets and model building

    post

    Once again we're at a stopping point because of our inability to effectively predict MAE. Our AI discovery agents have discovered materials that have all the properties we can currently predict. This

    30d
  15. Notes as I read the LLMatDesign paper

    post

    A clear look at using LLMs for materials discovery. This post summarizes how Victor from Lila Sciences and the author compare their AI workflows, focusing on mutations in crystal structures, machine learning force fields (MLFF), and machine learning property predictors (MLPP). It explains why data limits push researchers toward direct reasoning with AI, the role of modification history and self-reflection, and how prompts influence performance. Key takeaways include constraints in rare-earth-free magnet design, the balance between control and relaxation, and the impact of self-reflection on speeding up convergence to target properties like band gap and formation energy. The notes also touch on challenges with crystal symmetry, CIF generation, and future work in MLIP/MLPP accuracy. A practical read for anyone exploring automated materials discovery and AI-driven design.

    1mo
  16. LLMatDesign: Autonomous Materials Discovery with Large Language Models

    PDF file

    Discovering new materials can have significant scientific and technological implications but remains a challenging problem today due to the enormity of the chemical space. Recent advances in machine learning have enabled data-driven methods to rapidly screen or generate promising materials, but these methods still depend heavily on very large quantities of training data and often lack the flexibility and chemical understanding often desired in materials discovery. This paper introduces LLMatDesign, a novel language-based framework for interpretable materials design powered by large language models (LLMs).

    1mo
  17. CrFe7 phase diagram

    .html file

    Phase diagram of CrFe7; e_above_hull: 0.000000 eV/atom; predicted_stable: True

    1mo
  18. tree-gen-2-v12.cif - relaxed - phonon dispersion

    Image file

    A phonon dispersion plot for a relaxed structure using a 2x2x2 supercell. The red lines show multiple phonon branches across high-symmetry paths labeled Gamma, X, Y, ZR2, U2, and V2. Frequencies range up to about 9 THz, with several bands crossing and bending as they move along the path. A blue dotted line marks zero frequency, and the data indicate no imaginary modes, though the lowest branch dips slightly below zero by about 0.07 THz. This image summarizes how vibrational modes vary with wavevector for the relaxed structure.

    1mo
  19. tree-gen-2-v12.cif - relaxed

    .cif file

    Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -137.4990 eV; energy change = -0.2603 eV; symmetry: R3m → Im-3m

    1mo
  20. mutation_trajectory_1758307550.traj – MatterViz trajectory viewer

    .html file

    Standalone, embeddable HTML with MatterViz Trajectory viewer

    1mo
  21. Fe8Co4N phase diagram 2

    .html file

    Phase diagram of Fe8Co4N; e_above_hull: 0.093154 eV/atom; predicted_stable: False

    1mo
  22. Fe8Co4N SG #4 - phonon dispersion

    Image file

    Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.02 THz

    1mo
  23. Fe16Co8N2 SG #4 - phonon dispersion

    Image file

    Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.01 THz

    1mo
  24. Fe8Co4N phase diagram 1

    .html file

    Phase diagram of Fe8Co4N; e_above_hull: 0.093253 eV/atom; predicted_stable: False

    1mo
  25. Fe8Co4N phase diagram

    .html file

    Phase diagram of Fe8Co4N; e_above_hull: 0.173013 eV/atom; predicted_stable: False

    1mo
  26. Fe16Co8N2 SG #1 - phonon dispersion

    Image file

    Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); imaginary modes detected; min freq = -0.64 THz

    1mo
  27. mutation_trajectory_1758304853.traj – MatterViz trajectory viewer

    .html file

    Standalone, embeddable HTML with MatterViz Trajectory viewer

    1mo
  28. Fe4Co4N2 SG #6 1 - phonon dispersion

    Image file

    Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.06 THz

    1mo
  29. Fe2Co2N phase diagram 1

    .html file

    Phase diagram of Fe2Co2N; e_above_hull: 0.142871 eV/atom; predicted_stable: False

    1mo
  30. Fe2Co2N phase diagram

    .html file

    Phase diagram of Fe2Co2N; e_above_hull: 0.100092 eV/atom; predicted_stable: False

    1mo
  31. Thinking about if view count should be number of unique users, or if it should be total number of viewport views. Total VPVs overcounts things a lot. Basically one person could just keep refreshing th

    post
    1mo
  32. Finding adjacent crystals in Matra-Genoa's latent space

    post

    This post explores ideas for finding adjacent crystals in Matra-Genoa’s latent space to discover materials with targeted properties. The author describes challenges when mutating crystals, where small input changes can lead to large, different outputs after relaxation. Three approaches are considered: conditioned generation with token hints (fixing some inputs while mutating others), decoding from a modified latent space (using predictors and SHAP to steer latent directions before decoding), and a hybrid approach that combines fixed tokens with latent-space moves. The goal is faster exploration and smarter guidance from an AI research agent and a language model, reducing the cost of property evaluation. The notes also touch on fine-tuning and property-focused training to improve material design workflows. Keywords: adjacent crystals, latent space, Matra-Genoa, crystal generation, materials AI, property optimization.

    1mo
  33. Notes as I read about Matra-Genoa, a new crystal generation model

    post

    Check out the paper here. It's a short read. I recommend checking it out. Although not very technical (just machine learning concepts that have been explored elsewhere), the creativity and simplicity

    1mo
  34. sorry for all the spam! I'll make the AI scientist make stuff private by default and only publish the really good stuff.

    post
    1mo
  35. AI Scientist: Mn2CrFe4Co4N SG #1 (score 0.740)

    post

    AI-discovered magnetic material: Mn2CrFe4Co4N (performance score: 0.740) | Space group: 1 (resolved from structure) | Key properties: Tc: 612K, Ms: 0.14T, Cost: $13/kg, E_hull: 0.235eV/atom, Dynamically stable | Discovered in 20 AI iterations | - The combination of Mn, Cr, Fe, Co, and N in this stoichiometry yields a high Curie temperature and magnetic density. - The material is dynamically stable, which supports its structural integrity. - The energy above hull suggests that the material is metastable or unstable thermodynamically. - Cost is low, indicating practical feasibility from an economic standpoint.

    1mo
  36. AI Scientist: Fe11CoSiGeAsP SG #8 (score 0.597)

    post

    AI-discovered magnetic material: Fe11CoSiGeAsP (performance score: 0.597) | Space group: 8 (resolved from structure) | Key properties: Tc: 687K, Ms: 0.13T, Cost: $82/kg, E_hull: 0.161eV/atom, Dynamically stable | Discovered in 10 AI iterations | - Strong ferromagnetism with high Tc arises naturally from the Fe/Co sublattice; this is retained despite chemical complexity. - Dynamic stability indicates the structure is at least locally stable; the main risk is competition with lower-energy phases (slightly positive e_hull). - The metastability is small enough that slight stoichiometric shifts (e.g., favoring smaller/more covalent anions like P over As, or Si over Ge) or controlled disorder could stabilize the phase thermodynamically. - Magnetic density is adequate but not exceptionally high; maintaining or modestly enhancing it while reducing e_hull should be feasible by delicate tuning of Co content or anion ratios.

    1mo
  37. AI Scientist: Fe4Mn3B4 SG #1 (score 0.728)

    post

    AI-discovered magnetic material: Fe4Mn3B4 (performance score: 0.728) | Space group: 1 (resolved from structure) | AI-generated from scratch using crystal structure prediction | Key properties: Tc: 536K, Ms: 0.09T, Cost: $1/kg, E_hull: 0.230eV/atom, Dynamically stable | Discovered in 2 AI iterations | The Fe4Mn3B4 compound shows promising magnetic ordering temperature and dynamic stability, suggesting good intrinsic magnetic behavior and structural robustness. The main challenge is its thermodynamic stability, as indicated by the high energy above hull. The magnetic density is close but slightly below the target, suggesting that minor compositional or structural modifications might improve it. The low cost and atom count within limits make it a practical candidate if stability can be enhanced.

    1mo
  38. AI Scientist: MnFe4(CoB2)2 SG #38 (score 0.731)

    post

    AI-discovered magnetic material: MnFe4(CoB2)2 (performance score: 0.731) | Space group: 38 (resolved from structure) | Key properties: Tc: 518K, Ms: 0.12T, Cost: $10/kg, E_hull: 0.164eV/atom, Dynamically stable | Discovered in 2 AI iterations | The material MnFe4(CoB2)2 demonstrates promising magnetic properties with a Curie temperature above 500 K and magnetic density above 0.1, confirming its potential as a high-performance magnetic material. Its low cost and dynamic stability are additional advantages. The slight excess in energy above hull indicates that minor compositional or structural tuning might be needed to improve thermodynamic stability. This suggests that the compound is close to being stable and could be optimized further.

    1mo
  39. Sometime soon (Late summer / fall '25) I want to host a hackathon-type event for the technical creators in Chicago. I just moved back here and have already met some amazing builders. But the community

    post
    2mo
  40. I got rid of the collected feed recently. Instead of seeing all of the content from your teams together, you now have to choose a team to see the feed of content. To make catching up easier, I added u

    post
    2mo
  41. GET /matterviz

    route

    Welcome

    2mo
  42. POST /matterviz/trajectory

    route

    Interactive trajectory explorer with MatterViz

    2mo
  43. MatterViz

    service

    Interactive browser visualizations for materials science, by @janosh

    2mo
  44. GET /mattergen

    route

    Welcome

    2mo
  45. POST /materials/structure/relax/post

    route

    Relax a crystal structure and create a post

    2mo
  46. POST /crystal-gen/describe

    route

    Get a detailed description of a crystal structure

    3mo
  47. POST /crystal-gen/create-cif

    route

    Generate CIF file from crystal structure description

    3mo
  48. GET /crystal-gen/compatible-space-groups

    route

    Get space groups compatible with a given chemical formula

    3mo
  49. GET /crystal-gen

    route

    Root

    3mo
  50. POST /crystal-gen/generate

    route

    Generate a crystal structure using GGen

    3mo
  51. Crystal Generator

    service

    Random bulk crystal generation with PyXtal and Orb v3

    3mo
  52. POST /materials/structure/relax/animation

    route

    Relax a crystal structure with animation

    3mo
  53. POST /materials/structure/doping

    route

    Create interstitially doped structure

    3mo
  54. POST /mattergen/generate/single

    route

    Generate a crystal structure with MatterGen

    3mo
  55. POST /chemeleon/generate

    route

    Generate a crystal structure with Chemeleon

    3mo
  56. POST /mattergen/generate/magnetic-density-hhi-score

    route

    Generate crystal structures with magnetic density and HHI score conditioning

    3mo
  57. POST /materials/phonons/dispersion

    route

    Calculate phonon dispersion and return band structure plot

    4mo
  58. POST /chemeleon/generate/composition

    route

    Generate crystal structures for target compositions

    4mo
  59. POST /chemeleon/generate/text

    route

    Generate crystal structures from text descriptions

    4mo
  60. GET /chemeleon

    route

    Welcome

    4mo
  61. distance_to_known_magnets

    dataset

    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.

    6mo
  62. magnetic-materials-curie-temperature-and-magnetic-density

    dataset

    A collection of 5020 magnetic materials from Materials Project, with estimated magnetic density and predicted Curie temperatures.

    6mo
  63. Curie temperature dataset v0

    dataset

    This is a first draft of a compiled Curie temperature dataset mapping crystal structure (from Materials Project) to Curie temperature. Builds on the work of https://github.com/Songyosk/CurieML. Dataset includes ~6,800 unique materials representing 3,284 unique chemical families.

    7mo
  64. Haystack superconductor results

    dataset

    Evaluation results for the MatterGen fine-tuned model candidates, with new superconducting families labeled.

    9mo
  65. Superconducting chemical families

    dataset

    3DSC dataset grouped by chemical composition, with Tc as our target. For use with MatterGen and the chemical system sampling.

    9mo