Artificial intelligence (AI) has smoothly penetrated in a number of monitoring and control applications including the agriculture. However, research efforts towards low-power sensing devices with fully-functional AI on board are still fragmented. In this work, we present an embedded system enriched with the AI ensuring the continuous analysis and in-situ prediction of the growth dynamics of plant leaves. The embedded solution is grounded on a low-power embedded sensing system with a Graphics Processing Unit (GPU) and is able to run the neural networks-based AI on board. We use a Recurrent Neural Network (RNN) called the Long-Short Term Memory network (LSTM) as a core of AI in our system. The proposed approach guarantees the system autonomous operation for 180 days using a standard Li-ion battery. We rely on the state-of-the-art mobile graphical chips for ‘smart’ analysis and control of autonomous devices. This pilot study opens up wide vista for a variety of intelligent monitoring applications, especially in the agriculture domain. Also, we share with the research community the Tomato Growth dataset.

The rise of deep learning in various scientific and technology areas promotes the development of AI‐based tools for information retrieval. Optical recognition of organic structures is a key part of the automated extraction of chemical information. However, this is a challenging task because there is a large variety of representation styles. In this research, we present a Transformer‐based artificial neural network to convert images of organic structures to molecular structures. To train the model, we created a comprehensive data generator that stochastically simulates various drawing styles, functional groups, functional group placeholders (R‐groups), and visual contamination. We demonstrate that the Transformer‐based architecture can gather chemical insights from our generator with almost absolute confidence. That means that, with Transformer, one can fully concentrate on data simulation to build a good recognition model. A web demo of our optical recognition engine is available online at Syntelly platform, and the code for dataset generation is available on GitHub.

A parametric t-SNE approach based on deep feed-forward neural networks was applied to the chemical space visualization problem.

Lithium glymes adopt a distinct nanostructure at the negative electrode, unlike that observed with conventional ionic liquids.

The maximum density of monolayer packing on a graphene surface is calculated by means of molecular dynamics (MD) for ions of characteristic size and symmetry: 1-butyl-3-methylimidazolium [BMIM]+,...

In this work, we present a new method for predicting complex physical-chemical properties of organic molecules. The approach utilizes 3D convolutional neural network (ActivNet4) that uses solvent spatial distributions around solutes as input. These spatial distributions are obtained by a molecular theory called three-dimensional reference interaction site model. We have shown that the method allows one to achieve a good accuracy of prediction of bioconcentration factor which is difficult to predict by direct application of methods of molecular theory or simulations. Our research demonstrates that combination of molecular theories with modern machine learning approaches can be effectively used for predicting properties that are otherwise inaccessible to purely theory-based models.