New Framework (NOS) for Industrial Scalability using Materials Project Data

Materials Project
1

Hello everyone,

I am Wilmer Espinoza, an independent researcher from Honduras. With over 20 years of hands-on experience in industrial precision machining (CNC), I’ve noticed a persistent “laboratory-to-factory gap” in materials discovery.

To address this, I’ve developed the Natural Occurrence Score (NOS). This framework integrates Materials Project data (thermodynamic stability) with mechanical performance and crustal abundance into a single ranking metric for intermetallic coatings.

Using this approach, I’ve identified

Al13Fe4Al13​Fe4​

and

Zn13FeZn13​Fe

as top candidates that are not only high-performing but also industrially scalable due to elemental abundance.

You can find the full work and the methodology here: https://doi.org/10.5281/zenodo.19593520

I would love to hear the community’s and the staff’s thoughts on incorporating abundance metrics to ensure that the materials we discover today can actually be manufactured at scale tomorrow.

Best regards,
Wilmer Gaspar Espinoza Castillo

2

Small update: I have now archived the NOS framework on Zenodo with a DOI and also shared the initial preprint on ResearchHub.

Zenodo DOI: https://doi.org/10.5281/zenodo.19593520
ResearchHub preprint: https://www.researchhub.com/post/32347/natural-occurrence-score-nos-a-unified-screening-framework-for-intermetallic-coating-candidates

My main question for the community is now more specific:

What public datasets or Materials Project-derived properties would be most appropriate to validate a screening framework that combines thermodynamic stability, mechanical descriptors, and abundance-related indicators for coating candidate selection?

Any suggestions on validation strategy, weighting schemes, or comparable existing approaches would be very helpful.

Best regards,
Wilmer Gaspar Espinoza Castillo

3

Hi Wilmer,

What public datasets or Materials Project-derived properties would be most appropriate to validate a screening framework that combines thermodynamic stability, mechanical descriptors, and abundance-related indicators for coating candidate selection?

Materials Project provides a wide range of material properties. I would highly recommend exploring the Properties section on the material details page to see the available data and analyses most relevant to your research goals. In particular, you may find the Phase Stability (Thermodynamic Stability) and Mechanical (Elastic Constants) sections useful, although some properties may not be available for every material. For example: Zn₁₃Fe and Al₁₃Fe₄.

For additional datasets, you may also want to explore the ICSD, which contains primarily experimental crystal structure data along with some theoretical data.

Any suggestions on validation strategy, weighting schemes, or comparable existing approaches would be very helpful.

I believe the answer to this question depends heavily on the specific application. For example, batteries and sensors may have very different requirements, even when considering the same coating material. The most relevant properties and evaluation criteria will vary depending on the intended use case.

4

Hi Min-Hsueh,

Thank you for the detailed response and for pointing me toward the Phase Stability, Mechanical, and ICSD resources. This is very helpful for narrowing the validation strategy.

To give a bit more context, my NOS framework combines four weighted components: thermodynamic stability, mechanical descriptors, abundance/scalability indicators, and a confidence score. For the mechanical component, I am currently considering bulk modulus, shear modulus, and the Pugh ratio. My primary use case is high-temperature, corrosion-resistant intermetallic coatings for energy applications.

Based on your suggestion, I plan to build a small reproducible validation demo with 20–50 intermetallic candidates from Materials Project, comparing three ranking approaches:

  1. stability-only ranking,

  2. mechanical-property-only ranking,

  3. combined NOS ranking including abundance/scalability and confidence terms.

I have two follow-up questions:

  1. For abundance/scalability, my current plan is to use external sources such as crustal abundance data and EU CRM criticality indicators. Is there any Materials Project-derived field you would recommend incorporating alongside these?

  2. Are you aware of any published benchmark dataset or related study for intermetallic coatings that combines stability, mechanical behavior, and scalability-type criteria?

The preprint and Zenodo DOI 10.5281/zenodo.19593520 are available if useful for context. I will also share the validation demo here once it is ready.

Best regards,
Wilmer Gaspar Espinoza Castillo

5

Thanks for sharing!

  1. We currently do not provide that property in our dataset, as our focus is primarily on the intrinsic properties of materials. For future reference, you can browse our API documentation to see the properties that are currently available.
  2. I only have a general idea at this point and don’t have a specific dataset or research paper to reference. Others in the community may be aware of relevant datasets or studies that could provide a more definitive answer.