Papers by Geemi Wellawatte
The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynami... more The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice and there is a need for theory. The current state-of-the art method is mapping operators manually selected by experts. In this work, we demonstrate an automated approach by viewing this problem as supervised learning where we seek to reproduce the mapping operators produced by experts. We present a graph neural network based CG mapping predictor called DEEP SUPERVISED GRAPH PARTITIONING MODEL(DSGPM) that treats mapping operators as a graph segmentation problem. DSGPM is trained on a novel dataset, Human-annotated Mappings (HAM), consisting of 1,206 molecules with expert annotated mapping operators. HAM can be used to facilitate further research in this area. Our model uses a novel metric learning objective to produce high-quality atomic features that are used in spectral clustering. The results show th...
An outstanding challenge in deep learning in chemistry is its lack of interpretability. The inabi... more An outstanding challenge in deep learning in chemistry is its lack of interpretability. The inability of explaining why a neural network makes a prediction is a major barrier to deployment of AI models. This not only dissuades chemists from using deep learning predictions, but also has led to neural networks learning spurious correlations that are difficult to notice. Counterfactuals are a category of explanations that provide a rationale behind a model prediction with satisfying properties like providing chemical structure insights. Yet, counterfactuals are have been previously limited to specific model architectures or required reinforcement learning as a separate process. In this work, we show a universal model-agnostic approach that can explain any black-box model prediction. We demonstrate this method on random forest models, sequence models, and graph neural networks in both classification and regression.
Chemical Science
We propose a scalable graph neural network-based method for automating coarse-grained mapping pre... more We propose a scalable graph neural network-based method for automating coarse-grained mapping prediction for molecules.
Chemical Science
Correction for ‘Graph neural network based coarse-grained mapping prediction’ by Zhiheng Li et al... more Correction for ‘Graph neural network based coarse-grained mapping prediction’ by Zhiheng Li et al., Chem. Sci., 2020, 11, 9524–9531, DOI: 10.1039/D0SC02458A.
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Papers by Geemi Wellawatte