| Matscibert: A materials domain language model for text mining and information extraction T Gupta, M Zaki, NMA Krishnan, Mausam npj Computational Materials 8 (1), 102, 2022 | 133 | 2022 |
| Prediction of concrete strengths enabled by missing data imputation and interpretable machine learning GA Lyngdoh, M Zaki, NMA Krishnan, S Das Cement and Concrete Composites 128, 104414, 2022 | 98 | 2022 |
| Looking through glass: Knowledge discovery from materials science literature using natural language processing V Venugopal, S Sahoo, M Zaki, M Agarwal, NN Gosvami, NMA Krishnan Patterns 2 (7), 2021 | 50 | 2021 |
| Artificial Intelligence and Machine Learning in Glass Science and Technology: 21 Challenges for the 21st Century R Ravinder, V Venugopal, S Bishnoi, S Singh, M Zaki, HS Grover, ... International Journal of Applied Glass Science, 0 | 32 | |
| Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning R Bhattoo, S Bishnoi, M Zaki, NMA Krishnan Acta Materialia 242, 118439, 2023 | 31* | 2023 |
| Interpreting the optical properties of oxide glasses with machine learning and shapely additive explanations M Zaki, V Venugopal, R Bhattoo, S Bishnoi, SK Singh, AR Allu, ... Journal of the American Ceramic Society 105 (6), 4046-4057, 2022 | 25 | 2022 |
| Extracting processing and testing parameters from materials science literature for improved property prediction of glasses M Zaki, NMA Krishnan Chemical Engineering and Processing-Process Intensification 180, 108607, 2022 | 20 | 2022 |
| MaScQA: Investigating Materials Science Knowledge of Large Language Models M Zaki, J Jayadeva, M Mausam, NMA Krishnan Digital Discovery, 2024 | 16* | 2024 |
| Machine learning-aided cost prediction and optimization in construction operations V Sharma, M Zaki, KN Jha, NMA Krishnan Engineering, Construction and Architectural Management 29 (3), 1241-1257, 2022 | 16 | 2022 |
| DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles T Gupta, M Zaki, D Khatsuriya, K Hira, NM Krishnan, Mausam arXiv preprint arXiv:2207.01079, 2022 | 14 | 2022 |
| Elucidating the constitutive relationship of calcium–silicate–hydrate gel using high throughput reactive molecular simulations and machine learning GA Lyngdoh, H Li, M Zaki, NMA Krishnan, S Das Scientific reports 10 (1), 1-15, 2020 | 14 | 2020 |
| Glass hardness: Predicting composition and load effects via symbolic reasoning-informed machine learning S Mannan, M Zaki, S Bishnoi, DR Cassar, J Jiusti, JCF Faria, ... Acta Materialia 255, 119046, 2023 | 11 | 2023 |
| Natural language processing-guided meta-analysis and structure factor database extraction from glass literature M Zaki, SR Namireddy, T Pittie, V Bihani, SR Keshri, V Venugopal, ... Journal of Non-Crystalline Solids: X 15, 100103, 2022 | 7 | 2022 |
| Reconstructing the materials tetrahedron: challenges in materials information extraction K Hira, M Zaki, D Sheth, NMA Krishnan Digital Discovery 3 (5), 1021-1037, 2024 | 6 | 2024 |
| Accelerated design of chalcogenide glasses through interpretable machine learning for composition–property relationships S Singla, S Mannan, M Zaki, NMA Krishnan Journal of Physics: Materials 6 (2), 024003, 2023 | 6 | 2023 |
| Cementron: Machine learning the alite and belite phases in cement clinker from optical images M Zaki, S Sharma, SK Gurjar, R Goyal, NMA Krishnan Construction and Building Materials 397, 132425, 2023 | 4* | 2023 |
| Interpretable Machine Learning Approach for Identifying the Tip Sharpness in Atomic Force Microscopy M Zaki, S Kasimuthumaniyan, S Sahoo, NN Gosvami, NMA Krishnan Scripta Materialia 221, 114965, 2022 | 4 | 2022 |
| Glassomics: An omics approach toward understanding glasses through modeling, simulations, and artificial intelligence M Zaki, A Jan, NMA Krishnan, JC Mauro MRS Bulletin 48 (10), 1026-1039, 2023 | 3 | 2023 |
| LLaMat: Large Language Models for Materials Science V Mishra, S Singh, M Zaki, HS Grover, S Miret, NMA Krishnan AI for Accelerated Materials Design-Vienna 2024, 0 | | |
| Interpretable machine learning for understanding compositional and testing condition effects on refractive index, density, dielectric constant, and loss tangent inorganic melts … M Zaki, J Jayadeva, NM Krishnan Frontiers in Materials 11, 1412701, 0 | | |
N. M. Anoop KrishnanAssociate Professor, Indian Institute of Technology Delhi在 iitd.ac.in 的电子邮件经过验证
