![Hands-on session notebooks (later)
Natural Language Processing [44,45]
1. ChemNLP example (Part I)
2. ChemNLP example (Part II)
JARVIS-Leaderboard [5]
Analyzing benchmarks in the JARVIS-Leaderboard](https://image.slidesharecdn.com/jarvis-polymergroup-230713153156-57148694/85/ChemNLP-A-Natural-Language-Processing-based-Library-for-Materials-Chemistry-Text-Data-26-320.jpg)
ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
- 1. ChemNLP A Natural Language Processing based Library for Materials Chemistry Text Data Kamal Choudhary https://jarvis.nist.gov/ NIST, Gaithersburg, MD, USA Polymer group 7/13/2023 1 Joint Automated Repository for Various Integrated Simulations
- 2. Outline 2 • Introduction • AI for Materials • JARVIS • NLP basics • ChemNLP • Datasets • TextClassification • TokenClassification • WebApp • TextSummarization • TextGeneration • Integrating DFT database • JARVIS-Leaderboard/benchmarking • Hands-on • Summary Electronic structure DFT,DMFT, TB,QMC Quantum Computation AtomQC Force-Field JARVIS-FF ALIGNN-FF AI/ML CFID ALIGNN AtomVision ChemNLP
- 3. AI for Materials Science 3
- 4. Established: January 2017 Published: >40 articles Users: >20000+ users worldwide Materials: >80000, millions of properties Events: • Quantum Matters in Materials Science (QMMS) • Artificial Intelligence for Materials Science (AIMS) • JARVIS-School User-comments: • “There are many different theoretical levels on which you can approach the field. JARVIS is unusual in that it spans more levels than other databases.” • “A pure gold-mine for the data-quality effort…” • Thanks for your generous sharing. Your works inspire me a lot. • “You guys are doing something really beneficial…” • “I find JARVIS-DFT very useful for my research…” JARVIS: Databases, Tools, Events, Outreach 4 https://jarvis.nist.gov Requires login credentials, free registration
- 5. Updates • 80,000 materials • QMC, tight binding, ALIGNN, ALIGNN-FF, • AtomVision, ChemNLP, JARVIS-Leaderboard • Quantum Computation algorithms • Superconductors (bulk and 2D), magnetic topological mats. Recent Updates to JARVIS
- 6. Tools Used for hands-on session!
- 7. ChemNLP
- 8. Text classification & Token classification
- 9. Text summarization & Text-generation
- 11. Transformers & “Attention Is All You Need” Much better than RNN, LSTM etc. Attention: extremely long-term memory https://towardsdatascience.com/illustrated-guide-to-transformers-step-by-step-explanation-f74876522bc0
- 12. ChemNLP Datasets Exploratory data analysis (EDA)
- 15. Named Entity Recognition/Token classification 87 % F1 score
- 16. ChemNLP Webpage for Composition Search https://jarvis.nist.gov/jarvischemnlp/
- 17. ChemNLP Abstractive summarization (Abstract to Title) Google’s T5-225million transformer model ROGUE-1 score: 46.5 % Text generation (Title to Abstract) GPT2-medium LLM model ROGUE-1 score: 32 % Without fine tuning: 26 % ROUGE: Recall-Oriented Understudy for Gisting Evaluation T5: Text-to-Text Transfer Transformer
- 18. Other test cases
- 19. 19 ChemNLP for supercondutors Confusion matrix for text classification (137927 articles) • arXiv cond-mat.supr-con and JARVIS-SuperconDB • Venn diagram for chemical formula ChatGPT response
- 20. JARVIS-Leaderboard: Large Scale Benchmark Challenges in materials science community: • Reproducibility • Transparency • Validation • Fidelity • Data vs. metadata • What is the ground truth/reference to compare our models to? How does this change depending on the model? • Synergy of computational and experimental databases Community effort to tackle challenges: https://pages.nist.gov/jarvis_leaderboard/
- 21. JARVIS-Leaderboard: Contributors • Growing list of collaborators • Multi-institutional effort • Contributions are welcomed and encouraged from community!
- 22. JARVIS-Leaderboard: Methods and Data Types of Data: • Atomic structure (Molecule, Crystal) • Material Property (Bandgap, bulk modulus) • Images (Microscopy: SEM, TEM, STM) • Spectra (Diffraction: X-ray, Neutron, PL) • Text (Research articles, notebooks, blogs) • Eigensolver (Quantum Computation algorithms) 1) Electronic Structure 2) Artificial Intelligence 3) Force Field 4) Quantum Computation 5) Experiment
- 23. JARVIS-Leaderboard: Benchmarks Contributions 1) Electronic Structure 2) Artificial Intelligence 3) Force Field 4) Quantum Computation 5) Experiment Benchmarks (reference point) 1) Experiment/s 2) Test dataset 3) Electronic Structure 4) Analytical results 5) Other Experiments Error metrics *Benchmarks must be well-defined with an associated DOI
- 26. Hands-on session notebooks (later) Natural Language Processing [44,45] 1. ChemNLP example (Part I) 2. ChemNLP example (Part II) JARVIS-Leaderboard [5] Analyzing benchmarks in the JARVIS-Leaderboard
- 27. 27 Summary • NIST-JARVIS infrastructure with multiple components • ChemNLP for solids currently, expand to polymers… • Several events to engage (sign-up today & Demo!) • Continuously growing, contribute, collaborate… https://jarvis.nist.gov https://github.com/usnistgov/jarvis https://github.com/usnistgov/alignn https://github.com/usnistgov/atomvision https://github.com/usnistgov/chemnlp https://github.com/usnistgov/atomqc https://github.com/usnistgov/jarvis_leaderboard Email: kamal.choudhary@nist.gov, @dr_k_choudhary @knc6 Slides:https://www.slideshare.net/KAMALCHOUDHARY4