Phase diagram of Zr8Fe2Bi; eabovehull: 0.081061 eV/atom; predicted_stable: False

Supercell 3x3x3 of Zr8Fe2Bi (Space group: P422, 216 symmetry operations)

Phase diagram of Nb9Co19Bi; eabovehull: 0.194784 eV/atom; predicted_stable: False

Funny, the ones that are most stable have Bi as far as possible from Co/Fe. Not great for SOC!

Phase diagram of Nb4Co14Bi; eabovehull: 0.277027 eV/atom; predicted_stable: False

Supercell 2x2x2 of Nb4Co14Bi (Space group: P4/m, 64 symmetry operations)

Phonon band structure (supercell [3, 3, 3], Δ=0.01 Å); imaginary modes detected; min freq = -7.37 THz

high above the hull

Phase diagram of Nb5Co25Bi2; eabovehull: 0.178932 eV/atom; predicted_stable: False

Phonon band structure (supercell [3, 3, 3], Δ=0.01 Å); imaginary modes detected; min freq = -8.58 THz

Phase diagram of Nb3Co6Bi; eabovehull: 0.190533 eV/atom; predicted_stable: False

74meV above the hull

Phonon band structure (supercell [3, 3, 3], Δ=0.01 Å); no imaginary modes; min freq = -0.23 THz

71 meV above hull

Phonon band structure (supercell [3, 3, 3], Δ=0.01 Å); no imaginary modes; min freq = -0.16 THz

Phase diagram of ZrFe7Bi; eabovehull: 0.179574 eV/atom; predicted_stable: False

Supercell 2x2x2 of ZrFe7Bi (Space group: I4mm, 64 symmetry operations)

132 meV above hull

Rare-earth-free permanent magnet candidate system. WIP.

Rare-earth-free permanent magnet candidate system. WIP

Rare-earth-free permanent magnet candidate system. WIP Mostly giving up on this system. It doesn't seem like it has what we're looking for given the few I've tested and the stability of the symmetries

Rare-earth-free permanent magnet candidate system. WIP

I've got a small sample of experimental MAE values to compare against our calculator. While nowhere near sufficient, it should give us a bit of grounding against real world data and how trustworthy ou

A post that gathers casual, anecdotal ideas and some research about curing autoimmune conditions, with a focus on rheumatoid arthritis (RA). It describes personal motives to help a friend and to search for non-traditional approaches found on the internet. The content mixes diet ideas (Paleo, AIP, Clean Keto, Mediterranean pattern), gut health concepts like leaky gut and microbiome, and a range of potential strategies such as omega-3s, green tea, vitamin D and vitamin E, prebiotics, and probiotics. It also mentions gentler options like vagus nerve stimulation through breathing or humming, as well as supplements like berberine, and notes that results can be mixed. The piece emphasizes that much of this is not medical advice and should be read as personal exploration of what might help alongside conventional treatment. It links to several papers and online posts for further reading.

Interstitial Doping is a tool that helps place extra atoms inside crystal structures. It uses a physics-informed approach to find likely interstitial sites with Voronoi tessellation, and then ranks these sites by how well they fit the dopant atom and how favorable the surrounding chemistry is. The method works in periodic crystals by expanding the cell into a small supercell, performing the analysis, and then mapping the results back to the original structure. It characterizes each potential site by void size, coordination, geometry, and nearby atoms, and it scores them to guide dopant placement. Dopants are added one by one while maintaining minimum distances to hosts and to other dopants. This is designed for fast, high‑throughput screening and does not perform energy calculations or structural relaxations; users should relax all structures with DFT afterward.

AI-discovered magnetic material: Mn1Fe3Co1 (performance score: 0.900) | Space group: 8 (resolved) | Generated from scratch | Properties: Tc: 645K, Ms: 0.19T, $7/kg | Discovered in 10 iterations

AI-discovered magnetic material: Fe2CoMnW (performance score: 0.810) | Space group: 156 (resolved from structure) | AI-generated from scratch using crystal structure prediction | Key properties: Tc: 555K, Ms: 0.11T, MAE: 5.50mJ/m^3, Cost: $21/kg, E_hull: 0.262eV/atom, Dynamically stable | Discovered in 3 AI iterations | This material demonstrates that high magnetic performance can be achieved with relatively low cost and a small unit cell size. The high Curie temperature and magnetic anisotropy energy suggest potential for magnetic applications requiring thermal stability and strong anisotropy. The dynamic stability is a positive sign for synthesis feasibility. However, the elevated energy above hull suggests that further optimization or doping might be needed to improve thermodynamic stability. This insight highlights a trade-off between achieving strong magnetic properties and maintaining low energy above hull in this chemical composition and structure.

Calculate Magnetic Anisotropy Energy (MAE) using DFT

A new API is available to calculate magnetic anisotropy energy (MAE) using first-principles DFT with ABACUS. It’s designed for researchers who need accurate MAE values and are willing to run longer calculations. Expect 30 minutes to 2 hours per job, depending on system size and convergence. The service runs on an A100 GPU and is priced as a paid API.

Updated how we do route names. Previously, route names were the route's method (GET, POST, etc.) and the route path. This was limiting and unnecessary. We still store method and path, and now you are

API for first-principles calculations and properties

describes the latest run using a new first-principles DFT calculator to measure magnetocrystalline anisotropy energy via the total energy difference method. More results will follow.

This post explains how to run VASP with GPU acceleration inside Modal. It uses VASP version 6.3.0 and should work for other 6.x.x builds. The idea is to create a Modal Image that has an OpenACC-enabled GPU workflow, based on NVIDIA’s HPC SDK. The result is a self-contained image that can run GPU-accelerated VASP calculations in a serverless Modal environment.

A simple guide for compiling ABACUS to run with GPU acceleration in Modal. The post explains how to build ABACUS with CUDA support and run DFT calculations in a serverless environment. It covers why Modal’s on‑demand GPUs (like A100) can help, and which ABACUS setup (plane waves with basistype pw and kssolver bpcg) tends to work best on GPUs in version 3.9.0.

This post shares progress on calculating magnetocrystalline anisotropy energy (MAE) using density functional theory (DFT). The author hoped to use machine learning, but data limits make that unlikely for now, so DFT remains the focus. They emphasize how sensitive MAE is to convergence and accurate electronic structure, a common concern in the field. Two calculation methods are explored: the force-theorem and total energy difference. The force-theorem aims for a balance between speed and accuracy but isn’t fully working yet; issues include needing a specific spin setup and changes in the Fermi level when magnetization directions change. The total energy difference method is simpler and more reliable but far more computationally demanding, requiring several full SCF runs with spin-orbit coupling. Key parameters like k-point spacing, smearing, basis type, and ks_solver influence results and performance. The post notes GPU acceleration and the practical trade-offs, and promises more metrics and a public API later.

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.

Welcome

Interactive browser visualizations for materials science, by @janosh

Random bulk crystal generation with PyXtal and Orb v3

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.