@mmoderwell

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.

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.

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.

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

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.

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.

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.

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

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