Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for target electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

AI in Oil & Gas market is projected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66% from 2017 to 2022. Northbrook, IL -- (SBWIRE) -- 06/20/2019 -- AI in Oil & Gas market is expected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66%, during the forecast period. The growth of AI in Oil & Gas market will be mainly driven by the rise in adoption of the big data technology in the Oil & Gas industry to augment E&P capabilities, a significant increase in venture capital investments, and growing need for automation in the Oil & Gas industry, and tremendous pressure to reduce production costs. Software in AI in Oil & Gas market is applicable in upstream Oil & Gas exploration and production activities. The hardware segment in AI in Oil & Gas market is expected to grow swiftly during the forecast period (2017 to 2022), mainly due to the increasing requirement for sophisticated hardware system configurations and components capable of handling massive data, including, but not limited to Tensor Processor Unit (TPU), Graphic Processing Unit (GPU), Resistive Processing Unit (RPU), Field Programmable Gate Array (FPGA), and Visual Processing Unit (VPU) to install software-based AI capabilities.

AI in Oil & Gas market is projected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66% from 2017 to 2022. Northbrook, IL -- (SBWIRE) -- 06/20/2019 -- AI in Oil & Gas market is expected to grow from an estimated USD 1.57 Billion in 2017 to USD 2.85 Billion by 2022, at a CAGR of 12.66%, during the forecast period. The growth of AI in Oil & Gas market will be mainly driven by the rise in adoption of the big data technology in the Oil & Gas industry to augment E&P capabilities, a significant increase in venture capital investments, and growing need for automation in the Oil & Gas industry, and tremendous pressure to reduce production costs. Software in AI in Oil & Gas market is applicable in upstream Oil & Gas exploration and production activities. The hardware segment in AI in Oil & Gas market is expected to grow swiftly during the forecast period (2017 to 2022), mainly due to the increasing requirement for sophisticated hardware system configurations and components capable of handling massive data, including, but not limited to Tensor Processor Unit (TPU), Graphic Processing Unit (GPU), Resistive Processing Unit (RPU), Field Programmable Gate Array (FPGA), and Visual Processing Unit (VPU) to install software-based AI capabilities.

We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science. As of June 20th 2019, the dataset comprises of 12 quantum mechanical properties of 119,487 organic molecules with up to 14 heavy atoms, sampled from the GDB MedChem database. The Alchemy dataset expands the volume and diversity of existing molecular datasets. Our extensive benchmarks of the state-of-the-art graph neural network models on Alchemy clearly manifest the usefulness of new data in validating and developing machine learning models for chemistry and material science. We further launch a contest to attract attentions from researchers in the related fields. More details can be found on the contest website \footnote{https://alchemy.tencent.com}. At the time of benchamrking experiment, we have generated 119,487 molecules in our Alchemy dataset. More molecular samples are generated since then. Hence, we provide a list of molecules used in the reported benchmarks.

Ford is recycling over one billion plastic bottles every year to develop elements of he car's interior, reducing the amount of plastic ending up in a landfill. The American car maker has revealed that their Romanian-built EcoSport SUVs' carpets are made using 470 single-use bottles from recycled plastic bottles. The combined weight is said to weigh an estimated 8,262 metric tons and, if they were laid end to end, would stretch more than twice around the world, they said. Plastic fantastic: Ford has revealed that its EcoSport SUV features carpets that are made from recycled plastic bottles. According to the United Nations Environmental Agency, the world produces around 300 million tons of plastic each year, half of which is single-use items.

Much of modern-day agriculture is dominated by monoculture, the practice of producing a single crop on a large swath of land. This approach makes it easier for farmers to manage their fields with tractors and other basic automated tools, but it also strips the soil of nutrients and reduces its productivity. As a result, many farmers rely heavily on nitrogen-based fertilizers, which can convert into nitrous oxide, a greenhouse gas 300 times more potent than carbon dioxide. Robots run on machine-learning software could help farmers manage a mix of crops more effectively at scale, while algorithms could help farmers predict what crops to plant when, regenerating the health of their land and reducing the need for fertilizers.

Once billionaire Jeff Bezos' Blue Origin lander makes it to the moon, the Amazon CEO says it won't have to go very far to re-fuel. In a space summit in Boston, Bezos told an audience that his somewhat mysterious moon lander will use ice harvested from the lunar surface to create fuel. 'We know things about the moon now we didn't know about during the Apollo days,' Bezos said at the conference as reported by CNBC. 'We can harvest that ice and use to make hydrogen and oxygen, which are rocket propellants.' Jeff Bezos says a recently discovered trove of water and ice in the moon's surface could fuel a lunar lander owned by his company Blue Origin.

AI is getting smarter, requiring less training data and moving from cloud to Edge. Finnish AI startups gathered last week in Business Finland's Customer Club to share relevant information for intelligent industry and to check the latest state of the art of AI solutions for industrial use. There are plenty of small, young Finnish companies that have created money saving and innovative AI solutions especially for pulp and paper industry, mining companies and oil refineries that are strong businesses in Finland. Possibilities for different profitable applications are numerous with solutions that combine the use of cloud and edge in storing and analyzing data. All data from industrial machines cannot be moved to the cloud because there is typically just too much data or the latency requirements don't allow it.

Traditionally, supercomputer performance is measured using the High-Performance Linpack (HPL) benchmark, which is the basis for the Top500 list that biannually ranks world's fastest supercomputers. The Linpack benchmark tests a supercomputer's ability to conduct high-performance tasks (like simulations) that use double-precision math. On June's Top500 list, announced Monday, Summit's 148 Linpack petaflops land it first place by a comfortable margin. Using that same machine configuration, Oak Ridge National Laboratory (ORNL) and Nvidia have tested Summit on HPL-AI and gotten a result of 445 petaflops. While the HPL benchmark tests supercomputers' performance in double-precision math, AI is a rapidly growing use case for supercomputers -- and most AI models use mixed-precision math.

"I see Amazon as a technology company that just happened to do retail," begins Werner Vogels, Amazon's chief technology officer. "When Jeff [Bezos] started Amazon, he wasn't thinking about starting a bookshop. He was really fascinated by the internet." Only "mortal humans", he tells me in an interview, ever saw Amazon as merely a retailer. So the question now is: what will Amazon become next?