#MaterialsInformatics

Materials Project, AFLOW, OQMD, or JARVIS-DFT: which one do you query first?

The answer depends on what you're measuring. We compared all four on coverage, DFT settings, and update cadence.

Full comparison → alloybase.app/blog/posts/m...

#MaterialsInformatics #ComputationalMaterialsScience #DFT

Alloybase is now in public beta.

Query 13+ OPTIMADE-compliant materials databases in a single request. Unified schema, source attribution on every row.

alloybase.app

#MaterialsInformatics #OPTIMADE #MaterialsScience

2026年03月23日に日本表面真空学会 #JVSS が【表面科学セミナー2026 実践！インフォマティクスと自律計測の基礎と応用】を開催。講演3件と個別相談会 (現地参加者のみ)。東京都大田区・大田区産業プラザPiOおよびオンラインにて。案内(PDF)は buff.ly/RhK6Wka に、詳細は buff.ly/CLo03jA に。
#Seminar #SurfaceScience #MaterialsInformatics

31 citations: Neural ODE framework infers internal material states from observable quantities using Coleman-Gurtin theory. Dual networks respect thermodynamic laws through constrained weights.

📖 www.dl.begellhouse.com/journals/558...

#PhysicsGuidedML #MaterialsInformatics

#NFDI #FAIRmat #NOMAD #FAIRdata
#MaterialsScience #OpenData #ResearchDataManagement #RDM #DataDrivenScience #MaterialsInformatics

Deep learning in-depth analysis of crystal graph convolutional neural networks:

This review evaluates #CGCNN in #MaterialsInformatics, detailing architecture, limitations, and integration with #GenerativeModels, while outlining benchmarking and strategies to advance data‑driven #MaterialsDiscovery.

#OpenAccess in Nanotechnology Reviews: doi.org/10.1515/ntre...

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Quick Knowledge Drop — Trend
🔍 What is #MaterialsInformatics?
➡️ Applying machine learning to discover, design & optimise materials
✅ Faster experimentation
✅ Less costly prototyping
✅ Smarter insights from massive datasets
Innovation starts here! 🚀
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