Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

Graph networks are a new machine learning (ML) paradigm that supports both relational reasoning and combinatorial generalization. Here, we develop universal MatErials Graph Network (MEGNet) models for accurate property prediction in both molecules and crystals. We demonstrate that the MEGNet models outperform prior ML models such as the SchNet in 11 out of 13 properties of the QM9 molecule data set. Similarly, we show that MEGNet models trained on $\sim 60,000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set. We present two new strategies to address data limitations common in materials science and chemistry. First, we demonstrate a physically-intuitive approach to unify four separate molecular MEGNet models for the internal energy at 0 K and room temperature, enthalpy and Gibbs free energy into a single free energy MEGNet model by incorporating the temperature, pressure and entropy as global state inputs. Second, we show that the learned element embeddings in MEGNet models encode periodic chemical trends and can be transfer-learned from a property model trained on a larger data set (formation energies) to improve property models with smaller amounts of data (band gaps and elastic moduli)

SOM-based aggregation for graph convolutional neural networks

Graph property prediction is becoming more and more popular due to the increasing availability of scientific and social data naturally represented in a graph form. Because of that, many researchers are focusing on the development of improved graph neural network models. One of the main components of a graph neural network is the aggregation operator, needed to generate a graph-level representation from a set of node-level embeddings. The aggregation operator is critical since it should, in principle, provide a representation of the graph that is isomorphism invariant, i.e. the graph representation should be a function of graph nodes treated as a set. DeepSets (in: Advances in neural information processing systems, pp 3391–3401, 2017) provides a framework to construct a set-aggregation operator with universal approximation properties. In this paper, we propose a DeepSets aggregation operator, based on Self-Organizing Maps (SOM), to transform a set of node-level representations into a single graph-level one. The adoption of SOMs allows to compute node representations that embed the information about their mutual similarity. Experimental results on several real-world datasets show that our proposed approach achieves improved predictive performance compared to the commonly adopted sum aggregation and many state-of-the-art graph neural network architectures in the literature

CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction

Pedestrian path prediction is an essential topic in computer vision and video understanding. Having insight into the movement of pedestrians is crucial for ensuring safe operation in a variety of applications including autonomous vehicles, social robots, and environmental monitoring. Current works in this area utilize complex generative or recurrent methods to capture many possible futures. However, despite the inherent real-time nature of predicting future paths, little work has been done to explore accurate and computationally efficient approaches for this task. To this end, we propose a convolutional approach for real-time pedestrian path prediction, CARPe. It utilizes a variation of Graph Isomorphism Networks in combination with an agile convolutional neural network design to form a fast and accurate path prediction approach. Notable results in both inference speed and prediction accuracy are achieved, improving FPS considerably in comparison to current state-of-the-art methods while delivering competitive accuracy on well-known path prediction datasets.Comment: AAAI-21 Camera Read

Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex

We discuss relations between Residual Networks (ResNet), Recurrent Neural Networks (RNNs) and the primate visual cortex. We begin with the observation that a shallow RNN is exactly equivalent to a very deep ResNet with weight sharing among the layers. A direct implementation of such a RNN, although having orders of magnitude fewer parameters, leads to a performance similar to the corresponding ResNet. We propose 1) a generalization of both RNN and ResNet architectures and 2) the conjecture that a class of moderately deep RNNs is a biologically-plausible model of the ventral stream in visual cortex. We demonstrate the effectiveness of the architectures by testing them on the CIFAR-10 dataset.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF – 1231216