Datasets · Files

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.

R-squared value of 0.89, we look expected vs. predicted temperatures for a test set of ~1200 materials.

Looking at the 13,000 unique chemical families, we take the average Curie temperature and look at a histogram of those temperatures.

The crystal structure of a neodymium magnet. It is a permanent magnet made from an alloy of neodymium, iron, and boron to form the Nd2Fe14B tetragonal crystalline structure. They are the most widely used type of rare-earth magnet.

Top 20 features selected for the regression analysis of Tc, where (a) is without and (b) is with MEGNet element embeddings, along with the realized total loss reduction (i.e., the relevance score).

https://github.com/MDIL-SNU/SevenNet

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. https://pubs.acs.org/doi/10.1021/acs.jcim.4c00947

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. https://iopscience.iop.org/article/10.1088/0953-8984/26/10/103202/meta

Forecasted fred-cbbtcusd-nervous-feynman from 2025-03-14 to 2025-12-30

Dataset CBBTCUSD downloaded from fred: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

Dataset BTC-USD downloaded from yfinance: 2020-01-01 to present

Generated image from "Crowded dance floor seen from above, with clusters of dancers all performing identical synchronized movements within their groups. The dance moves are visibly spreading from dancer to dancer like a wave, with clear boundaries between different dance styles." using DALL-E 3 from OpenAI.

Generated image from "A time-lapse of a stadium doing increasingly energetic waves. In the first frame, a perfect grid of glowing points shows almost perfect alignment. As the wave intensifies in subsequent frames, the points become increasingly chaotic and misaligned, eventually showing completely random orientations at the height of the wave's energy." using DALL-E 3 from OpenAI.

Generated image from "A bookshelf with various books - thin paperbacks laying flat, tall encyclopedias standing upright, and a few books precariously balanced on their edges or covers. An invisible force appears to be trying to rotate the books, with the encyclopedias strongly resisting the rotation while the paperbacks easily change orientation." using DALL-E 3 from OpenAI.

Generated image from "A political map showing a country divided into distinct districts, each colored either red or blue. Some areas show large unified blocks of a single color, while boundaries between differently colored regions are clearly visible. A giant hand is holding a magnet above the map, causing more districts to align to the same color" using DALL-E 3 from OpenAI.

Generated image from "Only visualize this idea. No text. Imagine a dance floor with a simple rule: dancers (electrons) with the same moves (spins) need more space between them due to social etiquette (Pauli exclusion principle). In ferromagnetic materials: When two dancers meet, it's energetically favorable for them to dance the same way (parallel spins) As one dancer starts doing a specific move, nearby dancers naturally follow along This creates "dance neighborhoods" (magnetic domains) where everyone is synchronized The "dance style" spreads from one dancer to the next - this propagation is the exchange interaction. Some dance floors (crystal structures) naturally encourage everyone to dance the same way, creating strong magnets." using DALL-E 3 from OpenAI.

Generated image from "A stadium filled with people, each holding a flashlight. In a magnet, something special happens - everyone agrees to point their flashlights in the same direction. Suddenly, that side of the stadium becomes brilliantly bright. This coordinated alignment is what creates a magnet's strength. Each flashlight is like an electron's magnetic moment, and when aligned, they create a powerful cumulative effect." using DALL-E 3 from OpenAI.