@mmoderwell

Bridging the gap between computational prediction and experimental synthesis. Can we make what we predict? Can we predict what we've made?

The past few weeks, I've had my home lab running near 100% utilization running https://github.com/ourofoundation/ggen, searching for an ideal rare-earth-free permanent magnet. Many decent candidates h

Determining whether low-energy P1 structures hide higher-symmetry configurations and if more sampling could find them. The team ran three experiments to see why many of the lowest-energy structures end up in P1 (triclinic) symmetry and whether better structures exist. They found that the main problem is not hidden symmetry in P1, but too little sampling: only about 15 trials per formula leaves much of the energy landscape unexplored. Overall, increasing trials and sampling breadth can reveal better, more stable phases.

Gold memberships are back on Ouro. This is just the starting point, and the perks will grow over time.

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.

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

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.