The crux of molecular property prediction is to generate meaningful representations of the molecules. One promising route is to exploit the molecular graph structure through Graph Neural Networks (GNNs). It is well known that both atoms and bonds significantly affect the chemical properties of a molecule, so an expressive model shall be able to exploit both node (atom) and edge (bond) information simultaneously. Guided by this observation, we present Multi-View Graph Neural Network (MV-GNN), a multi-view message passing architecture to enable more accurate predictions of molecular properties. In MV-GNN, we introduce a shared self-attentive readout component and disagreement loss to stabilize the training process. This readout component also renders the whole architecture interpretable. We further boost the expressive power of MV-GNN by proposing a cross-dependent message passing scheme that enhances information communication of the two views, which results in the MV-GNN^cross variant. Lastly, we theoretically justify the expressiveness of the two proposed models in terms of distinguishing non-isomorphism graphs. Extensive experiments demonstrate that MV-GNN models achieve remarkably superior performance over the state-of-the-art models on a variety of challenging benchmarks. Meanwhile, visualization results of the node importance are consistent with prior knowledge, which confirms the interpretability power of MV-GNN models.

Logged forests cover four million square kilometres of the tropics and restoring these forests is essential if we are to avoid the worst impacts of climate change, yet monitoring recovery is challenging. Tracking the abundance of visually identifiable, early-successional species enables successional status and thereby restoration progress to be evaluated. Here we present a new pipeline, SLIC-UAV, for processing Unmanned Aerial Vehicle (UAV) imagery to map early-successional species in tropical forests. The pipeline is novel because it comprises: (a) a time-efficient approach for labelling crowns from UAV imagery; (b) machine learning of species based on spectral and textural features within individual tree crowns, and (c) automatic segmentation of orthomosaiced UAV imagery into 'superpixels', using Simple Linear Iterative Clustering (SLIC). Creating superpixels reduces the dataset's dimensionality and focuses prediction onto clusters of pixels, greatly improving accuracy. To demonstrate SLIC-UAV, support vector machines and random forests were used to predict the species of hand-labelled crowns in a restoration concession in Indonesia. Random forests were most accurate at discriminating species for whole crowns, with accuracy ranging from 79.3% when mapping five common species, to 90.5% when mapping the three most visually-distinctive species. In contrast, support vector machines proved better for labelling automatically segmented superpixels, with accuracy ranging from 74.3% to 91.7% for the same species. Models were extended to map species across 100 hectares of forest. The study demonstrates the power of SLIC-UAV for mapping characteristic early-successional tree species as an indicator of successional stage within tropical forest restoration areas. Continued effort is needed to develop easy-to-implement and low-cost technology to improve the affordability of project management.

Climate change is seen as the biggest challenge to the future of human life on Earth, and understanding the scientific language used to describe it can sometimes feel just as difficult. But help is at hand. Use our translator tool to find out what some of the words and phrases relating to climate change mean. Keeping the rise in global average temperature below 1.5 degrees Celsius will avoid the worst impacts of climate change, scientists say.

Companies have considered adoption of various high-level artificial intelligence (AI) principles for responsible AI, but there is less clarity on how to implement these principles as organizational practices. This paper reviews the principles-to-practices gap. We outline five explanations for this gap ranging from a disciplinary divide to an overabundance of tools. In turn, we argue that an impact assessment framework which is broad, operationalizable, flexible, iterative, guided, and participatory is a promising approach to close the principles-to-practices gap. Finally, to help practitioners with applying these recommendations, we review a case study of AI's use in forest ecosystem restoration, demonstrating how an impact assessment framework can translate into effective and responsible AI practices.

Ever since the 1930s when scientists, namely Hans Bethe, discovered that nuclear fusion was possible, researchers strived to initiate and control fusion reactions to produce useful energy on Earth. The best example of a fusion reaction is in the middle of stars like the Sun where hydrogen atoms are fused together to make helium releasing a lot of energy that powers the heat and light of the star. On Earth, scientists need to heat and control plasma, an ionised state of matter similar to gas, to cause particles to fuse and release their energy. Unfortunately, it is very difficult to start fusion reactions on Earth, as they require conditions similar to the Sun, very high temperature and pressure, and scientists have been trying to find a solution for decades. In May 2019, a workshop detailing how fusion could be advanced using machine learning was held that was jointly supported by the Department of Energy Offices of Fusion Energy Science (FES) and Advanced Scientific Computing Research (ASCR).

What do Ted Cruz, Neil deGrasse Tyson and Goldman Sachs all have in common? They predict that the world's first trillionaire will make their innumerable fortune in space. While Cruz is not precisely sure how this will come to be, Tyson and Goldman Sachs believe that the gateway to this immense wealth is through mining asteroids. The reason why space mining is so sought after is due to what is happening here on Earth. Based on known terrestrial reserves and estimates of the growing consumption in countries, essential elements needed for modern industry and food production (such as lead, phosphorus and gold) could be exhausted within the next 60 years.

The molecular dipole moment ($\boldsymbol{\mu}$) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra, as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). In this work, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression (SA-GPR) framework, which assigns a (vector) dipole moment to each atom, while movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting "MuML" models are fitted together to reproduce molecular $\boldsymbol{\mu}$ computed using high-level coupled-cluster theory (CCSD) and density functional theory (DFT) on the QM7b dataset. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to $\boldsymbol{\mu}$; further, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.

This week, host Isaac Butler talks to documentary theater makers Jessica Blank and Erik Jensen, whose plays include The Exonerated, about the criminal justice system, and Coal Country, about the Upper Big Branch mine disaster in West Virginia. Blank and Jensen explain how documentary theater works, from interviews with subjects to the final product, where actors perform interview excerpts verbatim. After the interview, Isaac and co-host June Thomas discuss why documentary theater is such a great way to communicate important information to an audience. Send your questions about creativity and any other feedback to working@slate.com.

When you think of the words "data" and "mine", no doubt the idea of data mining comes first. However, just as much as we find value in mining the rich resources of data, so too can we apply the advanced techniques for dealing with data to real-world mining -- that is, extracting natural resources from the earth. The world is just as dependent on natural resources as it is data resources, so it makes sense to see how the evolving areas of artificial intelligence and machine learning have an impact on the world of mining and natural resource extraction. Mining has always been a dangerous profession, since extracting minerals, natural gas, petroleum, and other resources requires working in conditions that can be dangerous for human life. Increasingly, we are needing to go to harsher climates such as deep under the ocean or deep inside the earth to extract the resources we still need.

Researchers from U of T Engineering and Carnegie Mellon University are using electrolyzers like this one to convert waste CO2 into commercially valuable chemicals. Their latest catalyst, designed in part through the use of AI, is the most efficient in its class. Credit: Daria Perevezentsev / University of Toronto Engineering Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class.