So I spent most of the week trying to get the HamGNN + TB2J magnetocrystalline anisotropy energy predictor working. Only to finally learn that the outputs of the pre-trained HamGNN model I was using d
Phase diagram of Mn8Al8C; e_above_hull: 0.315834 eV/atom; predicted_stable: False
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -111.2538 eV; energy change = -38.8208 eV; symmetry: P4/m → P1
Crystal structure generated by GEPA optimization (iteration 2)
Phase diagram of Mn8Al8C; e_above_hull: 1.072304 eV/atom; predicted_stable: False
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -85.6817 eV; energy change = -99.9416 eV; symmetry: P4/mmm → P1
Crystal structure generated by GEPA optimization (iteration 1)
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -7.2183 eV; energy change = 2.4552 eV; symmetry: P4/mmm → P4/mmm
Crystal structure generated by GEPA optimization (iteration 12)
Phase diagram of Fe4CoSiB2; e_above_hull: 0.173824 eV/atom; predicted_stable: False
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -245.7428 eV; energy change = -455.6124 eV; symmetry: I4/mcm → P1
Crystal structure generated by GEPA optimization (iteration 11)
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -24.1489 eV; energy change = -0.9037 eV; symmetry: P4/mmm → P1
Crystal structure generated by GEPA optimization (iteration 10)
Phase diagram of Fe2B; e_above_hull: 0.000062 eV/atom; predicted_stable: True
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -98.1390 eV; energy change = -9.9987 eV; symmetry: I4/mcm → I4/mcm
Crystal structure generated by GEPA optimization (iteration 9)
Phase diagram of Fe2B; e_above_hull: 0.000000 eV/atom; predicted_stable: True
Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -98.1402 eV; energy change = -113.4863 eV; symmetry: I4/mcm → I4/mcm
Crystal structure generated by GEPA optimization (iteration 8)
Phase diagram of MnAlC2; e_above_hull: 5.778611 eV/atom; predicted_stable: False
Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present
Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present
Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present
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
Victor from Lila Sciences sent me this paper he co-authored after he saw some of the work we were doing on AI agents for materials discovery. Check out the paper here:
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
Sharing an idea for finding "adjacent crystals". Why? In the AI research agent I'm working on, we're trying to discover materials with a set of target properties. We do this by letting an agent genera
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
dear 'X" : the Partial, this time's, is back in-game
photo-sonic sequencing in tandem with material-Fq. synchronization - magically, its conjurable
sorry for all the spam! I'll make the AI scientist make stuff private by default and only publish the really good stuff.
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.
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.
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.
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.
Explanations on how MAE factors into a crucial permanent magnet property, coercivity, and how we can use calculated MAE values to get a good feel for which candidates have permanent magnet potential.
Going to spend this morning getting torch-sim to conform to more types. I'll hopefully have all the PRs up by the end of this week!
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
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
Learn how to play baccarat online with this complete beginner’s guide. Discover rules, strategies, and tips to start winning at Pusta88 Casino
data exploration and analysis of large scale mined data set of thermoelectric materials from publications
Interactive trajectory explorer with MatterViz
Welcome
Interactive browser visualizations for materials science, by @janosh
Welcome
Relax a crystal structure and create a post
Get a detailed description of a crystal structure
Generate CIF file from crystal structure description
Generate a crystal structure using GGen
Root
Get space groups compatible with a given chemical formula
Random bulk crystal generation with PyXtal and Orb v3
Relax a crystal structure with animation
Create interstitially doped structure
Generate a crystal structure with MatterGen
Generate a crystal structure with Chemeleon
Dataset powering the material cost calculator. Lists element's USD/kg and when the data was last updated and where it came from.
Calculate the estimated raw material cost per kg
Generate crystal structures with magnetic density and HHI score conditioning
Analyze Structure
Calculate Thermochemistry
List Supported Formats
Forecasts for Bitcoin Price with 12-period horizon
Forecasts for Oil Price with 12-period horizon
Forecasts for Gold Price with 12-period horizon
Forecasts for Gold Price with 52-period horizon
Forecasts for Bitcoin Price with 52-period horizon
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.
Forecasts for Gold Price with 52-period horizon
Forecasts for Copper Price with 52-period horizon
Observed and forecasted housing market data for April 2025. Includes monthly data and forecasts projecting 12 months into the future.
A collection of 5020 magnetic materials from Materials Project, with estimated magnetic density and predicted Curie temperatures.
Forecasted fred-cbbtcusd-festive-ride from 2025-04-12 to 2025-12-30
Dataset CBBTCUSD downloaded from fred: 2020-01-01 to present
Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present
Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present
Forecasted fred-cbbtcusd-tender-shirley from 2025-04-09 to 2025-12-30
Dataset CBBTCUSD downloaded from fred: 2020-01-01 to present