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.

Non-degradable plastic waste stays for decades on land and in water, jeopardizing our environment; yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as the polymer family of polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world's plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. In this work, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world's yearly plastic production. We discuss possible synthesis routes for these identified promising materials. The developed multitask polymer property predictors are made available as a part of the Polymer Genome project at https://PolymerGenome.org.

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. As the only known solvent that life (as we know it) cannot exist, water is considered the divining rod for finding life.

Artificial intelligence (AI) is on the cusp of driving an agricultural revolution, and helping confront the challenge of feeding our growing global population in a sustainable way. But researchers warn that using new artificial intelligence technologies at scale holds huge risks that are not being considered. Imagine a field of wheat that extends to the horizon, being grown for flour that will be made into bread to feed cities' worth of people. Imagine that all authority for tilling, planting, fertilizing, monitoring, and harvesting this field has been delegated to artificial intelligence: algorithms that control drip-irrigation systems, self-driving tractors, and combine harvesters, clever enough to respond to the weather and the exact needs of the crop. Then imagine a hacker messes things up. A new risk analysis, published recently in the journal Nature Machine Intelligence, warns that the future use of artificial intelligence in agriculture comes with substantial potential risks for farms, farmers, and food security that are poorly understood and under-appreciated.

Data-driven methods are becoming an essential part of computational mechanics due to their unique advantages over traditional material modeling. Deep neural networks are able to learn complex material response without the constraints of closed-form approximations. However, imposing the physics-based mathematical requirements that any material model must comply with is not straightforward for data-driven approaches. In this study, we use a novel class of neural networks, known as neural ordinary differential equations (N-ODEs), to develop data-driven material models that automatically satisfy polyconvexity of the strain energy function with respect to the deformation gradient, a condition needed for the existence of minimizers for boundary value problems in elasticity. We take advantage of the properties of ordinary differential equations to create monotonic functions that approximate the derivatives of the strain energy function with respect to the invariants of the right Cauchy-Green deformation tensor. The monotonicity of the derivatives guarantees the convexity of the energy. The N-ODE material model is able to capture synthetic data generated from closed-form material models, and it outperforms conventional models when tested against experimental data on skin, a highly nonlinear and anisotropic material. We also showcase the use of the N-ODE material model in finite element simulations. The framework is general and can be used to model a large class of materials. Here we focus on hyperelasticity, but polyconvex strain energies are a core building block for other problems in elasticity such as viscous and plastic deformations. We therefore expect our methodology to further enable data-driven methods in computational mechanics