Computational Materials Science & AI for Accelerated Materials Discovery
Our research in Computational Materials Science and AI for Accelerated Materials Discovery leverages the power of data, simulation, and advanced characterisation to transform how materials are designed and understood. By integrating physics-based modelling with AI-driven approaches, we can predict material behaviours before they are synthesised — vastly accelerating the pace of innovation. Combined with state-of-the-art characterisation techniques at the atomic scale, our work enables faster, smarter discovery of materials for real-world impact.
Computational Materials Science:
Using simulations and modelling to predict material behaviours before they are made.
Materials Discovery with AI for Materials:
Accelerating innovation with machine learning to design materials faster and smarter.
State of the Art Characterisation:
Deploying cutting-edge techniques to uncover material properties at the atomic level.
Related Faculty
Related Publications
Authors: Emha Bayu Miftahullatif, Shreyas Dinesh Pethe, Andre K. Y. Low, Ayan A. Zhumekenov, Natalia Yantara, Priyanka Kajal, Qinjie Wu, Darrell Jun Jie Tay, Divyam Sharma, Saumya Sebastian, Jose Recatala-Gomez, Nambiar Abhishek, Nripan Mathews*, Kedar Hippalgaonkar*
Polyethylene-Glycol-Conjugated Peptide Coacervates with Tunable Size for Intracellular mRNA Delivery
Authors: Yue Sun, Xi Wu, Kimberle Shen, Ke Guo, Daryl Shern Lim, Wei Leong Chew, Jing Yu*, Ali Miserez*
Wyckoff Transformer: Generation of Symmetric Crystals
Authors: Nikita Kazeev, Wei Nong, Ignat Romanov, Ruiming Zhu, Andrey Ustyuzhanin, Shuya Yamazaki, and Kedar Hippalgaonkar*
Synthesis of Machine Learning‑Predicted Cs₂PbSnI₆ Double Perovskite Nanocrystals
Authors: Pritish Mishra, Mengyuan Zhang, Manaswita Kar, Maria Hellgren, Michele Casula, Benjamin Lenz*, Andy Paul Chen, Jose Recatala Gomez, Shakti Prasad Padhy, Marina Cagnon Trouche, Mohamed‑Raouf Amara, Ivan Cheong, Zengshan Xing, Carole Diederichs*, Tze Chien Sum, Martial Duchamp, Yeng Ming Lam, and Kedar Hippalgaonkar*
Robustness of Machine Learning Predictions for Fe-Co-Ni Alloys Prepared by Various Synthesis Methods
Authors: Shakti P. Padhy, Soumya R. Mishra, Li Ping Tan, Karl P. Davidson, Xuesong Xu, Varun Chaudhary*, and R. V. Ramanujan*
Machine Learning Driven Atom-Thin Materials for Fragrance Sensing
Authors: Yung-Hsuan Chou, Zixin Lin, Xin Hong, Botian Yang, Bingkun Zhu, Ivan K. Schuller, Nam-Joon Cho
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Research Achievements
News and announcements of MSE research achievements