Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions

TL;DR: An efficient deep learning approach is developed that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems, and unifies concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate chemical space predictions.

TL;DR: The recent advances in the state‐of‐the‐art of generative molecular design are reviewed and the considerations for integrating these models into real molecular discovery campaigns are discussed.

TL;DR: Process Systems Engineering (PSE) is the scientific discipline of integrating scales and components describing the behavior of a physicochemical system, via mathematical modelling, data analytics, design, optimization and control as discussed by the authors.

TL;DR: In this paper, the authors discuss scientific questions about the electrochemical systems to which ML can contribute, and discuss the necessary characteristics of such ML implementations, as well as discuss the scientific questions that ML can answer.

TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.

TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.

TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.