Analogous to the way the Human Genome Project advanced an array of biological sciences by mapping the human genome, the Materials Genome Initiative aims to enhance our understanding of the fundamentals of materials science by providing the information we need to accelerate the development of new materials.This approach is particularly applicable to recently developed classes of nanoporous materials, such as metal-organic frameworks (MOFs), which are synthesized from a limited set of molecular building blocks that can be combined to generate a very large number of different structures. In this Perspective, we illustrate how a materials genome approach can be used to search for high-performance adsorbent materials to store natural gas in a vehicular fuel tank. Drawing upon recent reports of large databases of existing and predicted nanoporous materials generated in silico, we have collected and compared on a consistent basis the methane uptake in over 650 000 materials based on the results of molecular simulation. The data that we have collected provide candidate structures for synthesis, reveal relationships between structural characteristics and performance, and suggest that it may be difficult to reach the current Advanced Research Project Agency-Energy (ARPA-E) target for natural gas storage. Broader contextNatural gas, mostly methane, is an attractive replacement for petroleum fuels for automotive vehicles because of its economic and environmental advantages. However, it suffers from a low volumetric energy density, necessitating densication to yield reasonable driving ranges from a full fuel tank. Densication strategies in the market today, liquefaction and compression, require expensive and cumbersome vehicular fuel tanks and rell station infrastructure. If we are able to develop a nanoporous adsorbent material to store natural gas at ambient temperature and moderate pressure, one could envision a simple fuel tank that can be relled at home. Modern, advanced nanoporous materials are highly tunable, inundating researchers with practically innite possibilities of materials to synthesize and test for methane storage. The current research is focused on nding among these millions of possible materials one that can be used to store natural gas without using liquefaction or compression processes. In this Perspective, we adopt a computational approach to screen databases of over 650 000 nanoporous material structures. Using this nanoporous materials genome approach, we reveal relationships between structural characteristics and performance, and suggest that it may be difficult, if not impossible, to reach the current Advanced Research Project Agency-Energy (ARPA-E) target for natural gas storage using nanoporous materials.

Accelerating progress in the discovery and deployment of advanced nanoporous materials relies on chemical insight and structure property relationships for rational design. Because of the complexity of this problem, trial-and-error is heavily involved in the laboratory today. A cost-effective route to aid experimental materials discovery is to construct structure models of nanoporous materials in silico and use molecular simulations to rapidly test them and elucidate data-driven guidelines for rational design. For example, highly-tunable nanoporous materials have shown promise as adsorbents for separating an industrially relevant gaseous mixture of xenon and krypton. In this work, we characterize, screen, and analyze the Nanoporous Materials Genome, a database of ca. 670,000 porous material structures, for candidate adsorbents for xenon/krypton separations. For over half a million structures, the computational resources required for a brute-force screening using grand-canonical Monte Carlo simulations of Xe/Kr adsorption are prohibitive.To overcome the computational cost, we used a hybrid approach combining machine learning algorithms (random forests) with molecular simulations. We compared the results from our large-scale screening with simple pore models to rationalize the strong link between pore size and selectivity. With this insight, we then analyzed the anatomy of the binding sites of the most selective materials. These binding sites can be constructed from tubes, pockets, rings, or cages and are often composed of non-discrete chemical fragments. The complexity of these binding sites emphasizes the importance of high-throughput computational screenings to discover new materials. Interestingly, our screening study predicts that the two most selective materials in the database are an aluminophosphate zeolite analogue and a calcium based coordination network, both of which have already been synthesized but not yet tested for Xe/Kr separations.

The technological advances of the past century, marked by the computer revolution and the advent of high-throughput screening technologies in drug discovery, opened the path to the computational analysis and visualization of bioactive molecules. For this purpose, it became necessary to represent molecules in a syntax that would be readable by computers and understandable by scientists of various fields. A large number of chemical representations have been developed over the years, their numerosity being due to the fast development of computers and the complexity of producing a representation that encompasses all structural and chemical characteristics. We present here some of the most popular electronic molecular and macromolecular representations used in drug discovery, many of which are based on graph representations. Furthermore, we describe applications of these representations in AI-driven drug discovery. Our aim is to provide a brief guide on structural representations that are essential to the practice of AI in drug discovery. This review serves as a guide for researchers who have little experience with the handling of chemical representations and plan to work on applications at the interface of these fields.