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Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of ourmore » fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.« less

Digital reticular chemistry is rapidly evolving into a pillar of modern chemistry. It is now at a critical junction in which an ecosystem of common data sets, tools, and good practices is needed to prevent this field from becoming an art rather than a science. We present the fundamentals of such an ecosystem and discuss common pitfalls that illustrate its importance.

Mg-Al mixed metal oxides (MMOs), derived from the decomposition of layered double hydroxides (LDHs), have been purposed as adsorbents for CO₂ capture of industrial plant emissions. To aid in the design and optimization of these materials for CO₂ capture at 200 °C, we have used a combination of solid-state nuclear magnetic resonance (ssNMR) and density functional theory (DFT) to characterize the CO₂ gas sorption products and determine the various sorption sites in Mg-Al MMOs. A comparison of the DFT cluster calculations with the observed ¹³C chemical shifts of the chemisorbed products indicates that mono- and bidentate carbonates are formed atmore » the Mg-O sites with adjacent Al substitution of an Mg atom, while the bicarbonates are formed at Mg-OH sites without adjacent Al substitution. Quantitative ¹³C NMR shows an increase in the relative amount of strongly basic sites, where the monodentate carbonate product is formed, with increasing Al/Mg molar ratios in the MMOs. This detailed understanding of the various basic Mg-O sites presented in MMOs and the formation of the carbonate, bidentate carbonate, and bicarbonate chemisorbed species yields new insights into the mechanism of CO₂ adsorption at 200 °C, which can further aid in the design and capture capacity optimization of the materials.« less

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of ~20,000 hypothetical MOFs, which are diverse in terms of their chemical design space - metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. Wemore » find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications - post-combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post-combustion carbon capture) and MOF-5 (for hydrogen storage). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications.« less

The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce themore » number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.« less

Finding the best material for a specific application is the ultimate goal of materials discovery. However, there is also the reverse problem: when experimental groups discover a new material, they would like to know all the possible applications this material would be promising for. Computational modeling can aim to fulfill this expectation, thanks to the sustained growth of computing power and the collective engagement of the scientific community in developing more efficient and accurate workflows for predicting materials' performances. We discuss the impact that reproducibility and automation of the modeling protocols have on the field of gas adsorption in nanoporousmore » crystals. We envision a platform that combines these tools and enables effective matching between promising materials and industrial applications.« less

Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we reviewmore » some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.« less

Colour is at the core of chemistry and has been fascinating humans since ancient times. It is also a key descriptor of optoelectronic properties of materials and is often used to assess the success of a synthesis. However, predicting the colour of a material based on its structure is challenging. In this work, we leverage subjective and categorical human assignments of colours to build a model that can predict the colour of compounds on a continuous scale. In the process of developing the model, we also uncover inadequacies in current reporting mechanisms. For example, we show that the majority ofmore » colour assignments are subject to perceptive spread that would not comply with common printing standards. To remedy this, we suggest and implement an alternative way of reporting colour—and chemical data in general. All data is captured in an objective, and standardised, form in an electronic lab notebook and subsequently automatically exported to a repository in open formats, from where it can be interactively explored by other researchers. We envision this to be key for a data-driven approach to chemical research.« less

We know significant advances have been made in the synthesis of chemically selective environments within metal-organic frameworks, yet materials development and industrial implementation have been hindered by the inability to predictively control crystallite size and shape. One common strategy to control crystal growth is the inclusion of coordination modulators, which are molecular species designed to compete with the linker for metal coordination during synthesis. However, these modulators can simultaneously alter the pH of the reaction solution, an effect that can also significantly influence crystal morphology. Herein, noncoordinating buffers are used to independently control reaction pH during metal-organic framework synthesis, enablingmore » direct interrogation of the role of the coordinating species on crystal growth. We demonstrate the efficacy of this strategy in the synthesis of low-dispersity single-crystals of the framework Co₂(dobdc) (dobdc^4-= 2,5-dioxido-1,4-benzenedicarboxylate) in a pH 7-buffered solution using cobalt(II) acetate as the metal source. Density functional theory calculations reveal that acetate competitively binds to Co during crystallization, and by using a series of cobalt(II) salts with carboxylate anions of varying coordination strength, it is possible to control crystal growth along the c-direction. Finally, we use zero length column chromatography to show that crystal morphology has a direct impact on guest diffusional path length for the industrially important hydrocarbon m-xylene. Together, these results provide molecular-level insight into the use of modulators in governing crystallite morphology and a powerful strategy for the control of molecular diffusion rates within metal-organic frameworks.« less

The prediction of material properties based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use them. We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification. We introduce design rules for reusable, code-agnostic, workflow interfaces to compute well-defined material properties, which wemore » implement for eleven quantum engines and use to compute various material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer’s expertise of the quantum engine directly available to non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.« less