arXiv (All)
open access
AI-ready design of realistic 2D materials and interfaces with Mat3ra-2D
Artificial intelligence (AI) and machine learning (ML) models in materials science are predominantly trained on ideal bulk crystals, limiting their transferability to real-world applications where surfaces, interfaces, and defects dominate. We present Mat3ra-2D, an open-source framework for the rapi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers#796422
- Machine learning for molecular and materials science#686317
- OxiGen: Oxidation-State-Aware Crystal Generation#1218031
- Closed-Loop Solid-State Synthesis Planning for Materials Discovery With Large Language Models.#1374133
- Probabilistic Deep Learning Framework for Phase Transformation Forecasting aided by In Situ High temperature Microscopy#1123350
Record · ID 1489384
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive