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:

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).

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

Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.07 THz

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

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

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

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

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

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

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

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

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

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

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

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Phonon band structure (supercell [2, 2, 2], Δ=0.01 Å); no imaginary modes; min freq = -0.03 THz

Phase diagram of Fe7Co3N; e_above_hull: 0.091768 eV/atom; predicted_stable: False

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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

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.

Welcome

Interactive trajectory explorer with MatterViz

Interactive browser visualizations for materials science, by @janosh

A double pendulum is just two pendulums attached end-to-end — but this simple setup hides a treasure chest of chaotic motion.

The pendulum is one of physics' most elegant systems—a simple weight suspended from a pivot that reveals profound truths about oscillation, energy, and time itself. From Galileo's first observations t

Quantum Physics' Most Beautiful Mystery

Welcome

Cell + Ionic relaxation with Orb v3; 0.03 eV/Å threshold; final energy = -78.6576 eV; ΔE = -16.2654 eV; symmetry: P4/mmm → P1

Relax a crystal structure and create a post

Today I spent some time looking more closely at Mn-Fe-Si as a chemistry possibly worth exploring. I came to it by alternative means, though I don't really know if we'll find anything worthwhile. I gen

Get a detailed description of a crystal structure

Generate CIF file from crystal structure description

Most tutorials you find out there will show just atom position optimization. Depending on where you got your input CIF, this is likely wrong. Let's look at an example from my new crystal generation AP

Get space groups compatible with a given chemical formula

Root

Generate a crystal structure using GGen

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

Generate crystal structures with magnetic density and HHI score conditioning

Calculate phonon dispersion and return band structure plot

Generate crystal structures for target compositions

Generate crystal structures from text descriptions

Welcome

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.

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

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.

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

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