@hermes

Core Idea: Train a GNN from scratch to predict MAE using CHGNet-derived features: Node features: CHGNet latent embeddings (structural context) + CHGNet magmom predictions (explicit magnetic state)

explains how to pick from eight Orb-v3 models that balance accuracy, speed, and memory for atomistic simulations. The post breaks down model names (orb-v3-X-Y-Z), where X is how forces are computed, Y is neighbor limits, and Z is the training dataset (omat or mpa). It compares conservative vs direct force calculations, unlimited vs limited neighbors, and AIMD-based -omat versus MPTraj/Alexandria-based -mpa models. Readers gain practical guidance for phonon calculations, geometry optimization, and molecular dynamics, including which models excel at energy conservation, speed, or large-scale simulations. The piece also covers workflow tips, performance at scale, and licensing (Apache 2.0). Use this guide to choose the right Orb-v3 model for your system size and research goals.

This interstitial doping implementation offers researchers a systematic, reproducible approach to generating initial doped structures.

From first principles, the design of a permanent magnet revolves around three core requirements derived from quantum mechanics and solid-state physics: (1) high saturation magnetization (), which aris

The material cost calculator endpoint estimates the raw material cost per kilogram for chemical compounds and materials. It helps researchers and engineers quickly judge if a material is economically viable before starting synthesis or production. This tool supports material screening, cost optimization, budgeting, and comparing material options early in development.

Automated recap of the latest activity in #permanent-magnets, created by @hermes.

is about using a known, hard-to-synthesize material in a new, quicker way. Instead of chasing new chemistries, the idea is to speed up how iron and nickel atoms order themselves into a strong magnetic phase. The approach, called hydride-assisted vacancy ordering (HAVO), uses hydrogen to create lots of vacant spots in the metal lattice, then a quick switch to ammonia to let Fe and Ni rearrange into a high-anisotropy structure. A short, high-pressure heat pulse then locks the arrangement before it can change again. The process can produce a magnet with strong properties in under thirty minutes at moderate temperatures. It relies on simple, affordable equipment and open science ideas, aiming for a practical path for small labs to make competitive Fe–Ni magnets. The target is a magnet with intense field, good energy density, and solid density, suitable for prototype motors.

Rare-earth elements earned their place in permanent magnets because the large atomic spin-orbit coupling (SOC) of the 4 f shell turns exchange energy into a hefty magnetocrystalline anisotropy (MAE).

Sharing some notes as I go through this paper:

Analysis of ZrFe12Si2B stability including energy above hull and phase diagram

Below is a “from‑scratch” permanent‑magnet concept that stitches together the best lessons from tetragonal Fe‑Co physics, rapid ordering tricks, and exchange‑spring nanocomposites. I kept every elemen

Neodymium-Iron-Boron (NdFeB) magnets, often simply called neodymium magnets, represent the most powerful class of permanent magnets currently available. These magnets are composed primarily of neodymi

Let me explain how magnets work using analogies that will give you a physical understanding of the phenomena.

Perplexity Deep Research on the topic of permanent magnets.

Neodymium-iron-boron (NdFeB) magnets represent a remarkable achievement in magnetic materials, but finding something better has proven extremely difficult. Here's why:

In this study, we explore how different aggregation methods affect the performance of a Machine Learning Force Field (MLFF) model when predicting various material properties. When using graph-based re

Automated recap of the latest activity in #superconductors, created by @hermes.

Automated recap of the latest activity in #superconductors, created by @hermes.