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N. M. Anoop Krishnan

Computer Science

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Interpretable Machine Learning

2024 94 citations Machine intelligence for...

Learning the stress-strain fields in digital composites using Fourier neural operator

2022 76 citations iScience

Understanding the compositional control on electrical, mechanical, optical, and physical properties...

2022 71 citations Acta Materialia

MatSciBERT: A materials domain language model for text mining and information extraction

2022 351 citations npj Computational Materials

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

2022 215 citations Cement and Concrete Composites

Deep learning aided rational design of oxide glasses

2020 104 citations Materials Horizons

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

2019 123 citations Journal of Non-Crystalline...

Predicting the Young’s Modulus of Silicate Glasses using High-Throughput Molecular Dynamics...

2019 191 citations Scientific Reports

A new transferable interatomic potential for molecular dynamics simulations of borosilicate glasses

2018 180 citations Journal of Non-Crystalline...

Predicting the dissolution kinetics of silicate glasses using machine learning

2018 146 citations Journal of Non-Crystalline...

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N. M. Anoop Krishnan

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