We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. Until recently, there was no visceral sense that the largest challenge we face is fixing the planet. Responding to environmental problems was for too long viewed by big companies as a marketing strategy to target consumers who were more environmentally conscious than others. Today, the tides are, literally, changing, and sustainability is now mission critical for businesses as new wisdom has emerged that illustrates how being'green' is a catalyst for innovation and market opportunity. Climate tech companies can now leverage advances in visual data collection, computer vision and AI to bolster their bottom line by focusing on enhancing sustainable practices.

With the global production of 150 million tons in 2016, ethylene is one of the most significant building blocks in today's chemical industry. Most ethylene is now produced in cracking furnaces by thermal cracking of fossil feedstocks with steam. This process consumes around 8% of the main energy used in the petrochemical industry, making it the single most energy-intensive process in the chemical industry. This paper studies a tubular thermal cracking reactor fed by propane and the molecular mechanism of the reaction within the reactor. After developing the reaction model, the existing issues, such as the reaction, flow, momentum, and energy, were resolved by applying heat to the outer tube wall.

Chemical engineers and materials scientists are constantly looking for the next revolutionary material, chemical, and drug. The rise of machine-learning approaches is expediting the discovery process, which could otherwise take years. "Ideally, the goal is to train a machine-learning model on a few existing chemical samples and then allow it to produce as many manufacturable molecules of the same class as possible, with predictable physical properties," says Wojciech Matusik, professor of electrical engineering and computer science at MIT. "If you have all these components, you can build new molecules with optimal properties, and you also know how to synthesize them. That's the overall vision that people in that space want to achieve" However, current techniques, mainly deep learning, require extensive datasets for training models, and many class-specific chemical datasets contain a handful of example compounds, limiting their ability to generalize and generate physical molecules that could be created in the real world. Now, a new paper from researchers at MIT and IBM tackles this problem using a generative graph model to build new synthesizable molecules within the same chemical class as their training data.

Predicting electronic energies, densities, and related chemical properties can facilitate the discovery of novel catalysts, medicines, and battery materials. By developing a physics-inspired equivariant neural network, we introduce a method to learn molecular representations based on the electronic interactions among atomic orbitals. Our method, OrbNet-Equi, leverages efficient tight-binding simulations and learned mappings to recover high fidelity quantum chemical properties. OrbNet-Equi models a wide spectrum of target properties with an accuracy consistently better than standard machine learning methods and a speed orders of magnitude greater than density functional theory. Despite only using training samples collected from readily available small-molecule libraries, OrbNet-Equi outperforms traditional methods on comprehensive downstream benchmarks that encompass diverse main-group chemical processes. Our method also describes interactions in challenging charge-transfer complexes and open-shell systems. We anticipate that the strategy presented here will help to expand opportunities for studies in chemistry and materials science, where the acquisition of experimental or reference training data is costly.

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, SMILES, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, SMILES has several shortcomings -- most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100\% robustness: SELFIES (SELF-referencIng Embedded Strings). SELFIES has since simplified and enabled numerous new applications in chemistry. In this manuscript, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete Future Projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.

The world's oceans are amazing carbon sponges, capturing a quarter of human-produced carbon dioxide when surface waters react with the greenhouse gas in the air or marine organisms gobble it up as they grow. Some research groups and start-ups want to help accelerate this natural process by adding certain minerals to the oceans that could help them lock up even more carbon and slow climate change. The idea has attracted a lot of excitement and investment. However, a number of recent studies suggest that some of these approaches may not be as effective as scientists had hoped. That's disappointing news, because the world may need to suck up an additional 10 billion tons of carbon annually by midcentury to limit warming to 2 C, according to a recent report.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. This week, Nvidia announced a slew of AI-focused hardware and software innovations during its March GTC 2022 conference. The company unveiled the Grace CPU Superchip, a data center processor designed to serve high-performance compute and AI applications. And it detailed the H100, the first in a new line of GPU hardware aimed at accelerating AI workloads including training large natural language models. But one announcement that slipped under the radar was the general availability of Nvidia's Riva 2.0 SDK, as well as the company's Riva Enterprise managed offering. Both can be deployed for building speech AI applications and point to the growing market for speech recognition in particular.

The use of Artificial Intelligence (AI) in financial services is all over the news, with some reports estimating it to be a US$450 billion opportunity. But what's the real story around what AI can do? Beyond just automating certain processes, AI has the potential to improve accuracy in credit or risk decisioning workflows, encouraging financial inclusion and allowing the underbanked and unbanked access to financial services in ways that were previously unreachable. Over 3 billion people in Asia have no access to formal credit and three of the top ten'most unbanked' countries in the world happen to be located in APAC (Vietnam, the Philippines and Indonesia). Finding innovative ways to enable more access to financial services is critical.

The field of extrasolar planet studies is undergoing a seismic shift. To date, 4,940 exoplanets have been confirmed in 3,711 planetary systems, with another 8,709 candidates awaiting confirmation. With so many planets available for study and improvements in telescope sensitivity and data analysis, the focus is transitioning from discovery to characterization. Instead of simply looking for more planets, astrobiologists will examine "potentially-habitable" worlds for potential "biosignatures." This refers to the chemical signatures associated with life and biological processes, one of the most important of which is water.

The use of Artificial Intelligence in financial services is all over the news, with reports estimating it to be a US$450 billion opportunity.