@mmoderwell

, a ferrimagnetic material from Materials Project. https://next-gen.materialsproject.org/materials/mp-21666

CIF file for ZrFe12Si2B, a ferrimagnetic materials from Materials Project. https://next-gen.materialsproject.org/materials/mp-653838

A 3D interactive scatter plot of magnetic materials from Materials Project. Points are colored by the materials estimated magnetic density and poitioned by UMAP reduction of Orb v2 model latent space

Room-temperature ferromagnets are high-value targets for discovery given the ease by which they could be embedded within magnetic devices. However, the multitude of potential interactions among magnetic ions and their surrounding environments renders the prediction of thermally stable magnetic properties challenging. Therefore, it is vital to explore methods that can effectively screen potential candidates to expedite the discovery of novel ferromagnetic materials within highly intricate feature spaces. To this end, the authors explore machine-learning (ML) methods as a means to predict the Curie temperature (Tc) of ferromagnetic materials by discerning patterns within materials databases.

Atomistic modelling of magnetic materials provides unprecedented detail about the underlying physical processes that govern their macroscopic properties, and allows the simulation of complex effects such as surface anisotropy, ultrafast laser-induced spin dynamics, exchange bias, and microstructural effects. Here the authors present the key methods used in atomistic spin models which are then applied to a range of magnetic problems. They detail the parallelization strategies used which enable the routine simulation of extended systems with full atomistic resolution.

This paper presents MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints. To enable this, the authors introduce a new diffusion-based generative process that produces crystalline structures by gradually refining atom types, coordinates, and the periodic lattice.

This paper introduces LLaDA, a diffusion model trained from scratch under the pre-training and supervised finetuning (SFT) paradigm. LLaDA models distributions through a forward data masking process and a reverse process, parameterized by a vanilla Transformer to predict masked tokens. https://arxiv.org/abs/2502.09992

Here we showcase a few key latent features and their relationships to each other, points colored by their Tc. We animate from 0 K to ~130 K.

Generated image from "A hairy frog" using DALL-E 3 from OpenAI.

Generated model from an image using the StabilityAI API.

Generated image from "A marble sculpture of a human male with white background" using the StabilityAI API.

Interactive plot of predicted vs. true Tc on the evaluation set.

Visualizing the counts of materials in the training and evaluation dataset by their Tc. First bin is non-superconductors, the rest are ranges of 20 K increments.

Using the 256 dimensional latent space output from the Orb model, we visualize the 3DSC(MP) dataset using UMAP with direction from Tc labels. Hover a point to see Tc, formula, and Material Project identifier.

Using the 256 dimensional latent space output from the Orb model, we visualize the 3DSC(MP) dataset using t-SNE and UMAP. The UMAP projection has been given the target for learning a manifold that keeps similar Tc materials close together.

Authors introduce Orb, a family of universal interatomic potentials for atomistic modeling of materials. Orb models are 3-6 times faster than existing universal potentials, stable under simulation for a range of out of distribution materials and, upon release, represented a 31% reduction in error over other methods on the Matbench Discovery benchmark. https://arxiv.org/abs/2410.22570

Authors present MatterSim, a deep learning model actively learned from large-scale first-principles computations, for efficient atomistic simulations at first-principles level and accurate prediction of broad material properties across the periodic table, spanning temperatures from 0 to 5000 K and pressures up to 1000 GPa. https://arxiv.org/abs/2405.04967

Not exactly the most rigorous test, but this side-by-side comparison shows the difference between running MD locally (M2 Macbook Air) and on a proper server (g4dn.2xl with T4 GPU). Each log is actually 100 simulation steps too.

Molecular dynamics simulation temperature ramping NaCl 3x3x3 supercell from 0 K to 300 K

Molecular dynamics simulation temperature ramping H2O 3x3x3 supercell from 0 K to 300 K