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In this post I'll share some of the work I've been doing on a Curie temperature prediction model. I finally found a decent dataset to work with. More on that here:
Sharing some notes as I read this paper. I uploaded it here for reference. I came across it looking for a Curie temperature dataset and so far this has been the best I've found so far.
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
This paper was published in August 2024, so it's quite fresh.
I've been looking all over for magnetic property data and it's been quite limited, so I was pretty excited when I found this dataset. Unfortunately, it's chemical formula -> Curie temperature and there are duplicate formula entries with different temperatures. It doesn't look like there's a proper way to resolve formula to crystal structure nor decide how to dedupe or match which temperatures to which structures.
I'll share some of my work on this in another post but we really need some thing "good enough". A Curie temperature model would serve as a candidate screener and eliminate any materials with low Tc so that we don't waste further resources evaluating the with other models or simulations.
A magnetic material experiences a loss of collective magnetic order at a certain temperature. For ferromagnetic materials, this phase-transition temperature is known as the Curie temperature (Tc). Their ordered magnetic properties cease at Tc or above, where only paramagnetic effects are observed; these have limited utility.
A permanent magnet needs to stay a permanent magnet in the range of temperatures you're using it! Room temperature is really the bare minimum, as high-performance use cases like car motors or wind turbines generate heat.
The exploration of magnetic materials has primarily been steered by experimental research efforts that rely on trial-and-error methods. Such research is very time-intensive, incurs significant operational costs and necessitates a substantial and sustained level of specialist human capital since a rich amount of domain knowledge is critical to research progress. Therefore, the realm of magnetic materials discovery stands to gain from data-driven methodologies that facilitate the targeted design of novel materials based on a desired property, particularly through the application of machine learning (ML).
Yep! That's why we're here.
The dataset is what I was most curious about but I'm afraid they didn't give much. We have it and that's great, but I would like to know more about it.
We herein collate a database comprising ca. 35,000 Tc values from the scientific literature. This database is divided into two segments. The first segment (Data set 1) includes Tc values sourced from a variety of publications,30−36 predominantly being experimental values reported by Nelson et al.26 and Belot et al.27 Data set 1 serves as the foundation for conducting a regression analysis of Tc values, showcasing the effectiveness of our GBFS workflow within this domain. Moreover, it facilitates an equitable comparison of our modeling approach with state-of-the-art models developed using a comparable data set. We broaden our analysis to the second data set (Data set 2) by incorporating Tc values from AtomWork37 into a blind-test scenario, exploring potential applications of our ML models beyond a conventional benchmarking evaluation against the state-of-the-art models
AtomWork comes from Inorganic Material Database from National Institute for Materials Science (NIMS), Japan.
I'm not really going to focus on the modeling all that much because it's a fundamentally different problem they're working with. We're working with crystal structure while they are working with chemical formula. Still, there are some useful tidbits.
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).
The model yearns for embeddings! It's funny how magnetic moment and an understanding of the elements is what is predictive. No surprises to us. Going with the MLIP latent vector approach seems just right because this is exactly what those features are doing.
Makes me think we should include magnetic moments if we can. Average or sum them so that we can keep the dimensionality right. It's technically encoded in the latent space but there's the whole decoder head that we're passing over when we grab just the material latent vector.
As you might be able to tell, I'm reading this paper and at the same time working on my own model.
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