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

Efficacy of a low-dose candidate malaria vaccine, R21 in adjuvant Matrix-M, with seasonal administration to children in Burkina Faso: a randomised controlled trial.

BACKGROUND: Stalled progress in controlling Plasmodium falciparum malaria highlights the need for an effective and deployable vaccine. RTS,S/AS01, the most effective malaria vaccine candidate to date, demonstrated 56% efficacy over 12 months in African children. We therefore assessed a new candidate vaccine for safety and efficacy. METHODS: In this double-blind, randomised, controlled, phase 2b trial, the low-dose circumsporozoite protein-based vaccine R21, with two different doses of adjuvant Matrix-M (MM), was given to children aged 5-17 months in Nanoro, Burkina Faso-a highly seasonal malaria transmission setting. Three vaccinations were administered at 4-week intervals before the malaria season, with a fourth dose 1 year later. All vaccines were administered intramuscularly into the thigh. Group 1 received 5 μg R21 plus 25 μg MM, group 2 received 5 μg R21 plus 50 μg MM, and group 3, the control group, received rabies vaccinations. Children were randomly assigned (1:1:1) to groups 1-3. An independent statistician generated a random allocation list, using block randomisation with variable block sizes, which was used to assign participants. Participants, their families, and the local study team were all masked to group allocation. Only the pharmacists preparing the vaccine were unmasked to group allocation. Vaccine safety, immunogenicity, and efficacy were evaluated over 1 year. The primary objective assessed protective efficacy of R21 plus MM (R21/MM) from 14 days after the third vaccination to 6 months. Primary analyses of vaccine efficacy were based on a modified intention-to-treat population, which included all participants who received three vaccinations, allowing for inclusion of participants who received the wrong vaccine at any timepoint. This trial is registered with ClinicalTrials.gov, NCT03896724. FINDINGS: From May 7 to June 13, 2019, 498 children aged 5-17 months were screened, and 48 were excluded. 450 children were enrolled and received at least one vaccination. 150 children were allocated to group 1, 150 children were allocated to group 2, and 150 children were allocated to group 3. The final vaccination of the primary series was administered on Aug 7, 2019. R21/MM had a favourable safety profile and was well tolerated. The majority of adverse events were mild, with the most common event being fever. None of the seven serious adverse events were attributed to the vaccine. At the 6-month primary efficacy analysis, 43 (29%) of 146 participants in group 1, 38 (26%) of 146 participants in group 2, and 105 (71%) of 147 participants in group 3 developed clinical malaria. Vaccine efficacy was 74% (95% CI 63-82) in group 1 and 77% (67-84) in group 2 at 6 months. At 1 year, vaccine efficacy remained high, at 77% (67-84) in group 1. Participants vaccinated with R21/MM showed high titres of malaria-specific anti-Asn-Ala-Asn-Pro (NANP) antibodies 28 days after the third vaccination, which were almost doubled with the higher adjuvant dose. Titres waned but were boosted to levels similar to peak titres after the primary series of vaccinations after a fourth dose administered 1 year later. INTERPRETATION: R21/MM appears safe and very immunogenic in African children, and shows promising high-level efficacy. FUNDING: The European & Developing Countries Clinical Trials Partnership, Wellcome Trust, and National Institute for Health Research Oxford Biomedical Research Centre

A Molecular Dynamics Study of Moisture-Induced Effects on Asphalt Binders and Mineral Aggregates

The moisture effect on the work of adhesion of asphalt binder and mineral aggregates was investigated using molecular dynamic simulation. The implemented asphalt model, based on the OPLS-AA force field, was validated in terms of thermodynamic properties such as density and elastic properties (Young’s modulus and Poisson’s ratio). The density was about 2% below the experimental value of 1,005 kg/m^3 at ambient temperature. Two methods were used to obtain the Young’s modulus: the Theodorou and Suter method and the stress-strain method. The latter one was successful in predicting a Young’s modulus that is compatible with the values measured at the nanoscale (1,384 ± 595 MPa) using the Atomic Force Microscope and a Poisson’s ratio of 0.4. The Theodorou and Suter method did not yield the same success. Simulation successfully demonstrated the effect of temperature on the Young’s modulus, which decreased from 1,505 MPa to 1,024 MPa when temperature changed from 25 to 75°C. The Poisson’s ratio was found to be constant at all temperatures. Two mineral structures were constructed in this work, the {101̅4} calcite and the {001} α-quartz represented by an optimized force field and CLAYFF, respectively. Calculations were successful in predicting that at 25, 50 and 75C and at a dry basis, the developed asphalt binder has a higher affinity to bond with calcite than quartz. It was also found that the work of adhesion for both structures was not affected by temperature. In addition, the Lennard-Jones interactions mostly contributed to the work of adhesion (80%). Calcite’s work of adhesion decreased by around 41% after adding water compared to a decrease of 21% for the case of adding water to quartz, which makes the former more susceptible to moisture damage. However, the asphalt still favored the calcite surface over quartz, which is evident by comparing the absolute values of the work of adhesion. Therefore, it is expected that the damage will start at the quartz surface before it does in the calcite surface

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials we also increasingly rely on isotherms predicted from molecular simulations. In particular, for such screening studies, it is important that the procedures to generate the data are accurate, reliable, and robust. In this work, we develop an efficient and automated workflow for a meticulous sampling of pure component isotherms. The workflow was tested on a set of metal-organic frameworks (MOFs) and proved to be reliable given different guest molecules. We show that the coupling of our workflow with the Clausius-Clapeyron relation saves CPU time, yet enables us to accurately predict pure component isotherms at the temperatures of interest, starting from a reference isotherm at a given temperature. We also show that one can accurately predict the CO2 and N2 mixture isotherms using Ideal Adsorbed Solution Theory (IAST). In particular, we show that IAST is a more reliable numerical tool to predict binary adsorption uptakes for a range of pressures, temperatures, and compositions, as it does not rely on the fitting of experimental data, which typically needs to be done with analytical models such as dual-site Langmuir (DSL). This makes IAST a more suitable and general technique to bridge the gap between adsorption (raw) data and process modelling. To demonstrate this point, we show that the ranking of materials, for a standard 3-step Temperature Swing Adsorption (TSA) process, can be significantly different depending on the thermodynamic method used to predict binary adsorption data. We show that for the design of processes that capture CO2 from low concentration (0.4%) streams, the commonly used methodology to predict mixture isotherms incorrectly assigns up to 33% of the materials as top-performing

A data-science approach to predict the heat capacity of nanoporous materials

The heat capacity of a material is a fundamental property of great practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications, the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine learning approach, trained on density functional theory simulations, to accurately predict the heat capacity of these materials, that is, zeolites, metal-organic frameworks and covalent-organic frameworks. The accuracy of our prediction is confirmed with experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials, the heat requirement is reduced by as much as a factor of two using the correct heat capacity