NJmat 2.0: User Instructions of Data-Driven Machine Learning Interface for Materials Science
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Abstract
NJmat is a user-friendly, data-driven machine learning interface designed for materials design and analysis. The platform integrates advanced computational techniques, including natural language processing (NLP), large langua... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (22)
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1. MatKG: an autonomously generated knowledge graph in material science10.1038/s41597-024-03039-z ↗
2. ChatGPT in the material design: selected case studies to assess the potential of ChatGPT10.1021/acs.jcim.3c01702 ↗
3. MatGPT: a vane of materials informatics from past, present, to future10.1002/adma.202306733 ↗
4. PDGPT: a large language model for acquiring phase diagram information in magnesium alloys10.1002/mgea.77 ↗
5. Wang · Mater Genome Eng Adv · 2024 · 2 · e5610.1002/mgea.56 ↗
6. Commentary: the materials project: a materials genome approach to accelerating materials innovation10.1063/1.4812323 ↗
7. Machine learning for molecular and materials science10.1038/s41586-018-0337-2 ↗
8. Data-driven exploration and first-principles analysis of perovskite material10.20517/jmi.2024.20 ↗
9. Huang · ACS Appl Mater Interfaces · 2024 · 16 · 5159510.1021/acsami.4c06276 ↗
10. Wang · Natl Sci Rev · 2023 · 10 · nwad12810.1093/nsr/nwad128 ↗
11. Toward learning steelmaking—a review on machine learning for basic oxygen furnace process10.1002/mgea.6 ↗
12. Multi-model integration accelerates Al-Zn-Mg-Cu alloy screening10.20517/jmi.2024.34 ↗
13. Large language models enabled intelligent microstructure optimization and defects classification of welded titanium alloys
14. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties10.1103/PhysRevLett.120.145301 ↗
15. Crystal structure generation with autoregressive large language modeling10.1038/s41467-024-54639-7 ↗
16. Machine learning of reactive potentials10.1146/annurev-physchem-062123-024417 ↗
17. Modelling chemical processes in explicit solvents with machine learning potentials10.1038/s41467-024-50418-6 ↗
18. NJmat: data-driven machine learning interface to accelerate material design10.1021/acs.jcim.4c00493 ↗
19. Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science10.1016/j.patter.2022.100488 ↗
20. Overcoming language barrier for scientific studies via unsupervised literature learning: case study on solar cell materials prediction10.1002/solr.202301079 ↗
21. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. arXiv:1301.3781. 2013.
22. CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling10.1038/s42256-023-00716-3 ↗