Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can search this space for suitable candidates at unprecedented speed and accuracy. This pipeline includes a polymer chemical fingerprinting capability called polyBERT (inspired by Natural Language Processing concepts), and a multitask learning approach that maps the polyBERT fingerprints to a host of properties. polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language. The present approach outstrips the best presently available concepts for polymer property prediction based on handcrafted fingerprint schemes in speed by two orders of magnitude while preserving accuracy, thus making it a strong candidate for deployment in scalable architectures including cloud infrastructures.

The majority of real-world processes are spatiotemporal, and the data generated by them exhibits both spatial and temporal evolution. Weather is one of the most important processes that fall under this domain, and forecasting it has become a crucial part of our daily routine. Weather data analysis is considered the most complex and challenging task. Although numerical weather prediction models are currently state-of-the-art, they are resource intensive and time-consuming. Numerous studies have proposed time-series-based models as a viable alternative to numerical forecasts. Recent research has primarily focused on forecasting weather at a specific location. Therefore, models can only capture temporal correlations. This self-contained paper explores various methods for regional data-driven weather forecasting, i.e., forecasting over multiple latitude-longitude points to capture spatiotemporal correlations. The results showed that spatiotemporal prediction models reduced computational cost while improving accuracy; in particular, the proposed tensor train dynamic mode decomposition-based forecasting model has comparable accuracy to ConvLSTM without the need for training. We use the NASA POWER meteorological dataset to evaluate the models and compare them with the current state of the art.

Spaceborne synthetic aperture radar (SAR) can provide accurate images of the ocean surface roughness day-or-night in nearly all weather conditions, being an unique asset for many geophysical applications. Considering the huge amount of data daily acquired by satellites, automated techniques for physical features extraction are needed. Even if supervised deep learning methods attain state-of-the-art results, they require great amount of labeled data, which are difficult and excessively expensive to acquire for ocean SAR imagery. To this end, we use the subaperture decomposition (SD) algorithm to enhance the unsupervised learning retrieval on the ocean surface, empowering ocean researchers to search into large ocean databases. We empirically prove that SD improve the retrieval precision with over 20% for an unsupervised transformer auto-encoder network. Moreover, we show that SD brings important performance boost when Doppler centroid images are used as input data, leading the way to new unsupervised physics guided retrieval algorithms.

The objective of this paper is to investigate the ability of physics-informed neural networks to learn the magnetic field response as a function of design parameters in the context of a two-dimensional (2-D) magnetostatic problem. Our approach is as follows. First, we present a functional whose minimization is equivalent to solving parametric magnetostatic problems. Subsequently, we use a deep neural network (DNN) to represent the magnetic field as a function of space and parameters that describe geometric features and operating points. We train the DNN by minimizing the physics-informed functional using stochastic gradient descent. Lastly, we demonstrate our approach on a \mbox{ten-dimensional} EI-core electromagnet problem with parameterized geometry. We evaluate the accuracy of the DNN by comparing its predictions to those of finite element analysis.

In this paper three customised Artificial Intelligence (AI) frameworks, considering Deep Learning (convolutional neural networks), Machine Learning algorithms and data reduction techniques are proposed, for a problem of long-term summer air temperature prediction. Specifically, the prediction of average air temperature in the first and second August fortnights, using input data from previous months, at two different locations, Paris (France) and C\'ordoba (Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, like the mega-heatwave of 2003, which affected France and the Iberian Peninsula. Thus, an accurate prediction of long-term air temperature may be valuable also for different problems related to climate change, such as attribution of extreme events, and in other problems related to renewable energy. The analysis carried out this work is based on Reanalysis data, which are first processed by a correlation analysis among different prediction variables and the target (average air temperature in August first and second fortnights). An area with the largest correlation is located, and the variables within, after a feature selection process, are the input of different deep learning and ML algorithms. The experiments carried out show a very good prediction skill in the three proposed AI frameworks, both in Paris and C\'ordoba regions.

The potential use of robotics for laboratory experiments offers an attractive route to alleviate scientists from tedious tasks while accelerating the process of obtaining new materials, where topical issues such as climate change and disease risks worldwide would greatly benefit. While some experimental workflows can already benefit from automation, it is common that sample preparation is still carried out manually due to the high level of motor function required when dealing with heterogeneous systems, e.g., different tools, chemicals, and glassware. A fundamental workflow in chemical fields is crystallisation, where one application is polymorph screening, i.e., obtaining a three dimensional molecular structure from a crystal. For this process, it is of utmost importance to recover as much of the sample as possible since synthesising molecules is both costly in time and money. To this aim, chemists have to scrape vials to retrieve sample contents prior to imaging plate transfer. Automating this process is challenging as it goes beyond robotic insertion tasks due to a fundamental requirement of having to execute fine-granular movements within a constrained environment that is the sample vial. Motivated by how human chemists carry out this process of scraping powder from vials, our work proposes a model-free reinforcement learning method for learning a scraping policy, leading to a fully autonomous sample scraping procedure. To realise that, we first create a simulation environment with a Panda Franka Emika robot using a laboratory scraper which is inserted into a simulated vial, to demonstrate how a scraping policy can be learned successfully. We then evaluate our method on a real robotic manipulator in laboratory settings, and show that our method can autonomously scrape powder across various setups.

Newly developed graph neural networks (GNNs) [1-3], in particular convolutional graph neural networks, have been shown to be effective in a variety of classification and regression tasks. Recently they have been applied to physical problems [4, 5] where they can accommodate unstructured and hierarchical data naturally. Analogous to pixel-based convolutional neural networks (CNNs), "message passing" [6] graph convolutional neural networks (GCNNs) [7, 8] employ convolutional operations to achieve a compact parameter space by exploiting correlations in the data through connectivity defined by adjacency on the source discretization. Frankel et al. [9] and others [10-13] derive the information transmission graph directly from the connectivity of the discretization, computational grid or mesh based on the assumption the physical interactions are local. Some obvious advantages of applying convolutions to the discretization graph are that: general mesh data can be handled without interpolation to a structured grid, the discretization can be conformal to the microstructure, periodic boundary conditions can be handled without padding, and topological irregularities can be accommodated without approximations. In this approach the kernels and number of parameters are similar for a pre-selected reduction of the representation, e.g. based on the grains in a polycrystal [5], but the size of the adjacency can be prohibitive.

We are in the middle of a business model revolution. And we are active participants in that revolution. We have been transitioning from a society where possession and application of physical commodities defined wealth and power, to a society where possession and application of knowledge define wealth and power. Throughout the 20th century, oil had been the most valuable commodity in defining wealth and power. Possession and application of oil defined the fortunes of individuals (John D. Rockefeller, H.L. Hunt, Paul Getty), companies (Standard Oil, ExxonMobil, Shell, BP), and countries (United States, Russia, Saudi Arab, Iraq, Canada, Venezuela).

If you're on Twitter and follow news about China, you likely have heard a pretty wild rumor recently: that President Xi Jinping was under house arrest and that there was about to be a major power grab in the country. First of all, let's be very clear: this report is false and should not be taken seriously. No credible sources on China have bought it. But it's interesting to dissect how a ridiculous rumor could be elevated and spread so widely that it made it to Twitter's deeply flawed trending list over the weekend, thanks to influencer translation and amplification from accounts based in India. This story is from China Report, MIT Technology Review's new newsletter giving you the inside scoop on what's happening in China.

A few years ago, Samsung had major battery issues when several faulty Galaxy Note 7 phones had exploding batteries. The devices were recalled, and the company spent a lot of time over the following years outlining all the rigorous battery tests it did to ensure it didn't happen again. Now, YouTuber Mrwhosetheboss, as well as others, have noticed batteries in Samsung phones are swelling up at a disproportionately high rate. This usually affects older devices, but some are only a couple of years old – the 2020-era Galaxy Z Fold 2, for instance. Samsung hasn't formally responded yet, but battery swelling isn't a new problem, nor one unique to Galaxy phones.