(Source: wikipedia.com)

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Machine learning for science

Machine learning that shapes science.

Areas:

• Materials science: Exploring faster ways to compute materials properties and generate new kinds of materials.
• Computational chemistry: Exploring the molecular space and interactions.
  
• Quantum machine learning: Exploring the interplay between quantum computing and machine learning.
  

Talks/Tutorials

• Climate change: Challenges and AI-driven solutions, @Swinburne Vietnam, Hanoi,  Vietnam, Dec 2019.
  
• AI for matters, Phenikaa University, Hanoi, Vietnam, Jan 2019.

Publications

Materials science:

• Variational hyper-encoding networks, P Nguyen, T Tran, S Gupta, S Rana, HC Dam, S Venkatesh, ECML-PKDD'21, 2021
• Hybrid generative-discriminative models for inverse materials design, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Svetha Venkatesh. arXiv preprint arXiv:1811.06060. Extention of:
• Incomplete conditional density estimation for fast materials discovery, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Svetha Venkatesh. SDM'19.
• Incomplete conditional density estimation for fast materials discovery, Phuoc Nguyen, Truyen Tran, Sunil Gupta, Svetha Venkatesh. SDM'19.
• Committee machine that votes for similarity between materials; Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Tuan-Dung Ho, Truyen Tran, Keisuke Takahashi and Hieu-Chi Dam. IUCrJ, 2018 Nov 1; 5(Pt 6): 830–840.

Computational chemistry:

• Graph transformation policy network for chemical reaction prediction, Kien Do, Truyen Tran, Svetha Venkatesh, KDD'19.
• Relational dynamic memory networks, Trang Pham, Truyen Tran, Svetha Venkatesh, arXiv preprint arXiv:1808.04247. Extension of:
• Graph memory networks for molecular activity prediction, Trang Pham, Truyen Tran, Svetha Venkatesh, ICPR'18.
• Neural reasoning for chemical-chemical interaction. Trang Pham, Truyen Tran, Svetha Venkatesh, NIPS 2018 Workshop on Machine Learning for Molecules and Materials.
• Attentional multilabel learning over graphs: A message passing approach, K Do, T Tran, T Nguyen, S Venkatesh, Machine Learning, 2019.
  
• Graph memory networks for molecular activity prediction, Trang Pham, Truyen Tran, Svetha Venkatesh, ICPR'18. 
• Prelim version appears at NIPS Workshop on Deep learning for physical sciences, 2017
• Extending: Graph classification via deep learning with virtual nodes Trang Pham, Truyen Tran, Hoa Dam, Svetha Venkatesh, Third Representation Learning for Graphs Workshop (ReLiG 2017).