Mohd Zaki [PhD Student, IIT Delhi]

Zaki works on developing machine learning models for predicting glass properties. He is also working on developing Natural Language Processing (NLP) and Natural Language Understanding (NLU) pipeline for information extraction from scientific literature for materials. He completed his B.Tech. in Civil Engineering from MNNIT Allahabad prior to joining M3RG, IIT Delhi as a research scholar. He has been awarded with Prime Minister’s Research Fellowship in December 2020 cycle.

Education

PhD Course Work

Brief CV: Click here to download.

Academic Referres:

• Prof. N. M. Anoop Krishnan, Department of Civil Engineering, IIT Delhi
• Prof. Jayadeva, Department of Electrical Engineering, IIT Delhi

Conferences and workshops attended:

International Conferences

• 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13465–13483, Toronto, Canada. Association for Computational Linguistics, 2023
  • DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles
• Glass and Optical Materials Division Meeting (GOMD2023), June 4 - 8, 2023, New Orleans, Louisiana, USA (Oral)
  • Automated extraction of glass compositions and properties from research literature
• Engineering Mechanics Institute Conference 2023 (EMI-2023), June 6 - 10, 2023, Atlanta, Georgia, USA (Oral)
  • Investigating large language models’ understanding of mechanics
• 2023 Materials Research Society Meeting & Exhibit, April 10 - 14, 2023, San Francisco, California, USA (Oral)
  • Extracting materials Compositions from research papers using natural language processing
• 26^th International Congress on Glass, 2022 (Oral presentations, Online)
  • Predicting Vickers Hardness of Glasses as a function of processing parameters.
  • Predicting Optical properties of Glasses as a function of testing parameters.
• Şişecam 37^th International Glass Conference, Istanbul, Turkey, 2022
  • Machine Learning Based Predictions and Explanations of Glasses Properties as a Function of Composition, Testing, and Processing Parameters (Poster)
• Pacific Rim Conference on Ceramic and Glass Technology including Glass and Optical Materials Division Meeting (GOMD2022) (Oral presentations, Online):
  • Predicting electrical properties of Glasses as a function of composition and wavelengths
• Pacific Rim Conference on Ceramic and Glass Technology including Glass and Optical Materials Division Meeting (GOMD2021) (Oral presentations, Online):
  • Predicting Normal and Anomalous Glasses Directly from the Indent Images using Machine Learning
  • Extracting the Synthesis and Processing Protocols from Materials Science Literature for Improved Property Prediction
  • Understanding the optical properties of glasses using interpretable machine learning
• Material Science & Technology 2021 (MS&T21) (Oral presentations, Online):
  • Elucidating Compositional Governance of Optical Properties Oxide Glasses Using Interpretable Machine Learning
  • Predicting Glass Behavior from Optical Microscopy Images Using Interpretable Machine Learning

Workshops and short-term courses

• NVIDIA DLI courses under National Supercomputing Mission during the period of January - March.
• Fundamental of Deep Learning by NVIDIA Deep Learning Institute, June 8th, 2021.
• Fundamental of Accelerated Data Science with RAPIDS by NVIDIA Deep Learning Institute, June 9th, 2021.
• Building Transformer-based Natural Language Processing Applications by NVIDIA Deep Learning Institute, June 10th, 2021.
• 6^th International Course on Seismic Analysis of Structures using OpenSees, July 19th – 22nd, 2021

Publications

1. Predicting oxide glass properties with low complexity neural network and physical and chemical descriptors.

   Bishnoi, Suresh and Badge, Skyler and Krishnan, NM Anoop and others.
   Journal of Non-Crystalline Solids, 2023

2. Glass hardness: Predicting composition and load effects via symbolic reasoning-informed machine learning.

   Mannan, Sajid and Zaki, Mohd and Bishnoi, Suresh and Cassar, Daniel R and Jiusti, Jeanini and Faria, Julio Cesar Ferreira and Christensen, Johan FS and Gosvami, Nitya Nand and Smedskjaer, Morten M and Zanotto, Edgar Dutra and others.
   Acta Materialia, 2023

3. Graph Neural Stochastic Differential Equations for Learning Brownian Dynamics.

   Bishnoi, Suresh and Ranu, Sayan and Krishnan, NM and others.
   arXiv preprint arXiv:2306.11435, 2023

4. Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks.

   Bishnoi, Suresh and Bhattoo, Ravinder and Ranu, Sayan and Krishnan, NM and others.
   arXiv preprint arXiv:2307.05299, 2023

5. Predicting Young's modulus of oxide glasses with sparse datasets using machine learning.

   Bishnoi, Suresh and Singh, Sourabh and Ravinder, R and Bauchy, Mathieu and Gosvami, Nitya Nand and Kodamana, Hariprasad and Krishnan, NM Anoop.
   Journal of Non-Crystalline Solids, 2019

6. Deep learning aided rational design of oxide glasses.

   Ravinder, R and Sridhara, Karthikeya H and Bishnoi, Suresh and Grover, Hargun Singh and Bauchy, Mathieu and Kodamana, Hariprasad and Krishnan, NM Anoop and others.
   Materials horizons, 2020

7. An adaptive, interacting, cluster-based model for predicting the transmission dynamics of COVID-19.

   Ravinder, R and Singh, Sourabh and Bishnoi, Suresh and Jan, Amreen and Sharma, Amit and Kodamana, Hariprasad and Krishnan, NM Anoop.
   Heliyon, 2020

8. Scalable Gaussian processes for predicting the optical, physical, thermal, and mechanical properties of inorganic glasses with large datasets.

   Bishnoi, Suresh and Ravinder, R and Grover, Hargun Singh and Kodamana, Hariprasad and Krishnan, NM Anoop.
   Materials advances, 2021

9. Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century.

   Ravinder and Venugopal, Vineeth and Bishnoi, Suresh and Singh, Sourabh and Zaki, Mohd and Grover, Hargun Singh and Bauchy, Mathieu and Agarwal, Manish and Krishnan, NM Anoop.
   International journal of applied glass science, 2021

10. Interpreting the optical properties of oxide glasses with machine learning and Shapely additive explanations.

    Zaki, Mohd and Venugopal, Vineeth and Bhattoo, Ravinder and Bishnoi, Suresh and Singh, Sourabh Kumar and Allu, Amarnath R and Jayadeva and Krishnan, NM Anoop.
    Journal of the american ceramic society, 2022

11. Unravelling the performance of physics-informed graph neural networks for dynamical systems.

    Thangamuthu, Abishek and Kumar, Gunjan and Bishnoi, Suresh and Bhattoo, Ravinder and Krishnan, NM and Ranu, Sayan.
    Advances in Neural Information Processing Systems, 2022

12. Enhancing the inductive biases of graph neural ode for modeling dynamical systems.

    Bishnoi, Suresh and Bhattoo, Ravinder and Ranu, Sayan and Krishnan, NM.
    arXiv preprint arXiv:2209.10740, 2022

13. Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning.

    Bhattoo, Ravinder and Bishnoi, Suresh and Zaki, Mohd and Krishnan, NM Anoop.
    Acta materialia, 2023

14. MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models.

    Zaki, Mohd and Krishnan, NM and others.
    arXiv preprint arXiv:2308.09115, 2023

15. Glassomics: An omics approach toward understanding glasses through modeling, simulations, and artificial intelligence.

    Zaki, Mohd and Jan, Amreen and Krishnan, NM Anoop and Mauro, John C.
    MRS Bulletin, 2023

16. Cementron: Machine learning the alite and belite phases in cement clinker from optical images.

    Zaki, Mohd and Sharma, Siddhant and Gurjar, Sunil Kumar and Goyal, Raju and Krishnan, NM Anoop and others.
    Construction and Building Materials, 2023

17. Interpretable Machine Learning Approach for Identifying the Tip Sharpness in Atomic Force Microscopy.

    Zaki, Mohd and Kasimuthumaniyan, S and Sahoo, Sourav and Gosvami, Nitya Nand and Krishnan, NM Anoop and others.
    Scripta Materialia, 2022

18. Natural language processing-guided meta-analysis and structure factor database extraction from glass literature.

    Zaki, Mohd and Namireddy, Sahith Reddy and Pittie, Tanu and Bihani, Vaibhav and Keshri, Shweta Rani and Venugopal, Vineeth and Gosvami, Nitya Nand and Krishnan, NM Anoop and others.
    Journal of Non-Crystalline Solids: X, 2022

19. Extracting processing and testing parameters from materials science literature for improved property prediction of glasses.

    Zaki, Mohd and Krishnan, NM Anoop and others.
    Chemical Engineering and Processing-Process Intensification, 2022

20. Interpreting the optical properties of oxide glasses with machine learning and shapely additive explanations.

    Zaki, Mohd and Venugopal, Vineeth and Bhattoo, Ravinder and Bishnoi, Suresh and Singh, Sourabh Kumar and Allu, Amarnath R and Krishnan, NM Anoop.
    Journal of the American Ceramic Society, 2022

21. Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction.

    Hira, Kausik and Zaki, Mohd and Sheth, Dhruvil and Krishnan, NM and others.
    arXiv preprint arXiv:2310.08383, 2023

22. Glass hardness: Predicting composition and load effects via symbolic reasoning-informed machine learning.

    Mannan, Sajid and Zaki, Mohd and Bishnoi, Suresh and Cassar, Daniel R and Jiusti, Jeanini and Faria, Julio Cesar Ferreira and Christensen, Johan FS and Gosvami, Nitya Nand and Smedskjaer, Morten M and Zanotto, Edgar Dutra and others.
    Acta Materialia, 2023

23. DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles.

    Gupta, Tanishq and Zaki, Mohd and Khatsuriya, Devanshi and Hira, Kausik and Krishnan, NM and Mausam.
    To be updated, 2022

24. Accelerated design of chalcogenide glasses through interpretable machine learning for composition--property relationships.

    Singla, Sayam and Mannan, Sajid and Zaki, Mohd and Krishnan, NM Anoop.
    Journal of Physics: Materials, 2023

25. Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning.

    Bhattoo, Ravinder and Bishnoi, Suresh and Zaki, Mohd and Krishnan, NM Anoop.
    Acta Materialia, 2023

26. Matscibert: A materials domain language model for text mining and information extraction.

    Gupta, Tanishq and Zaki, Mohd and Krishnan, NM Anoop and Mausam.
    npj Computational Materials, 2022

27. Elucidating the constitutive relationship of calcium--silicate--hydrate gel using high throughput reactive molecular simulations and machine learning.

    Lyngdoh, Gideon A and Li, Hewenxuan and Zaki, Mohd and Krishnan, NM Anoop and Das, Sumanta.
    Scientific reports, 2020

28. Artificial Intelligence and Machine Learning in Glass Science and Technology: 21 Challenges for the 21st Century.

    Ravinder, R and Venugopal, Vineeth and Bishnoi, Suresh and Singh, Sourabh and Zaki, Mohd and Grover, Hargun Singh and Bauchy, Mathieu and Agarwal, Manish and Krishnan, NM Anoop.
    International Journal of Applied Glass Science, To be updated

29. Looking through glass: Knowledge discovery from materials science literature using natural language processing.

    Venugopal, Vineeth and Sahoo, Sourav and Zaki, Mohd and Agarwal, Manish and Gosvami, Nitya Nand and Krishnan, NM Anoop.
    Patterns, 2021

30. Machine learning-aided cost prediction and optimization in construction operations.

    Sharma, Virok and Zaki, Mohd and Jha, Kumar Neeraj and Krishnan, NM Anoop.
    Engineering, Construction and Architectural Management, 2022

31. Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning.

    Lyngdoh, Gideon A and Zaki, Mohd and Krishnan, NM Anoop and Das, Sumanta.
    Cement and Concrete Composites, 2022