8 research outputs found
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Chemistry and materials science are complex. Recently, there have been great
successes in addressing this complexity using data-driven or computational
techniques. Yet, the necessity of input structured in very specific forms and
the fact that there is an ever-growing number of tools creates usability and
accessibility challenges. Coupled with the reality that much data in these
disciplines is unstructured, the effectiveness of these tools is limited.
Motivated by recent works that indicated that large language models (LLMs)
might help address some of these issues, we organized a hackathon event on the
applications of LLMs in chemistry, materials science, and beyond. 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 our
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
Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications
Adsorptive Separation of CO<sub>2</sub> from Multicomponent Mixtures of Flue Gas in Carbon Nanotube Arrays: A Grand Canonical Monte Carlo Study
Grand
canonical Monte Carlo (GCMC) simulations have been performed to investigate
the adsorption and separation behavior of ternary and quaternary gaseous
mixtures of CO<sub>2</sub>, along with H<sub>2</sub>S, SO<sub>2</sub>, and N<sub>2</sub>, in bundles of aligned double-walled carbon nanotubes
with a diameter of 3 nm and an intertube distance of 0.5 nm. All of
the simulations are performed at 303 K and at pressures varying between
0 and 3 bar. The GCMC results are then compared to the ideal adsorbed
solution theory (IAST) predictions. For the ternary mixture H<sub>2</sub>S–CO<sub>2</sub>–N<sub>2</sub>, the results
show that CO<sub>2</sub> has the highest adsorption among the three
components. The IAST predictions agree reasonably well with the GCMC
data for the ternary mixture, except for H<sub>2</sub>S. For the quaternary
mixture H<sub>2</sub>S–SO<sub>2</sub>–CO<sub>2</sub>–N<sub>2</sub>, it is observed that initially CO<sub>2</sub> has the highest adsorption up until around 2 bar, whereafter there
is a crossover by SO<sub>2</sub> to have the highest adsorption. IAST
fails to predict the adsorption behavior of the quaternary mixture
involving SO<sub>2</sub>
Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening.
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. We 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
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14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon â€
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 our 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