Area of research
Materials Chemistry · Molecular Biology
Research interest
Research interests include Machine Learning in Materials Science, X-ray Diffraction in Crystallography, Genomics and Phylogenetic Studies, and Complex Network Analysis Techniques.
JARVIS-Leaderboard: a large scale benchmark of materials design methods
Recent advances and applications of deep learning methods in materials science
Enabling deeper learning on big data for materials informatics applications
ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition
Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds
Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection
A general-purpose machine learning framework for predicting properties of inorganic materials
Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
Combinatorial screening for new materials in unconstrained composition space with machine learning