Digital discovery最新文献

We investigate the model reported by Giagou and Meyer in 2010 for comparing deuterium kinetic isotope effects (KIEs) computed using density functional theory (DFT) for the intramolecular hydrogen transfer step in the mechanism of the Swern oxidation of alcohols to aldehydes, with those measured by experiment. Whereas the replication of the original computed values for the gas-phase reaction proved entirely successful, several issues were discovered when a continuum solvent model was used. These included uncertainty regarding the parameters and methods used for the calculations and also the coordinates for the original reactant and transition structures, via their provision as data in the ESI. The original conclusions, in which a numerical mis-match between the magnitude of the computed and experimentally measured KIE was attributed to significant deviations from transition structure theory, are here instead rationalised as a manifestation of basis-set effects in the computation. Transition state theory appears to be operating successfully. We now recommend the use of basis sets of triple- or quadruple-ζ quality, rather than the split-valence level previously employed, that dispersion energy corrections be included and that a continuum solvent model using smoothed reaction cavities is essential for effective geometry optimisation and hence accurate normal coordinate analysis. An outlying experimental KIE obtained for chloroform as solvent is attributed to a small level of an explicit hydrogen bonded interaction with the substrate. A temperature outlier for the measured KIE at 195 K is suggested for further experimental investigation, although it may also be an indication of an unusually abrupt incursion of hydrogen tunnelling which would need non-Born–Oppenheimer methods in which nuclear quantum effects are included to be more accurately modelled. We predict KIEs for new substituents, of which those for R = NMe₂ are significantly larger than those for R = H. This approach could be useful in designing variations of the Swern reagent that could lead to synthesis of aldehydes incorporating much higher levels of deuterium. The use of FAIR data rather than the traditional model of its inclusion in the ESI is discussed, and two data discovery tools exploiting these FAIR attributes are suggested.

Designing nanoparticles with desired properties is a challenging endeavor, due to the large combinatorial space and complex structure–function relationships. High throughput methodologies and machine learning approaches are attractive and emergent strategies to accelerate nanoparticle composition design. To date, how to combine nanoparticle formulation, screening, and computational decision-making into a single effective workflow is underexplored. In this study, we showcase the integration of three key technologies, namely microfluidic-based formulation, high content imaging, and active machine learning. As a case study, we apply our approach for designing PLGA-PEG nanoparticles with high uptake in human breast cancer cells. Starting from a small set of nanoparticles for model training, our approach led to an increase in uptake from ∼5-fold to ∼15-fold in only two machine learning guided iterations, taking one week each. To the best of our knowledge, this is the first time that these three technologies have been successfully integrated to optimize a biological response through nanoparticle composition. Our results underscore the potential of the proposed platform for rapid and unbiased nanoparticle optimization.

What are researchers' motivations and challenges related to automation and autonomy in materials science laboratories? Our survey on this topic received 102 responses from researchers across a variety of institutions and in a variety of roles. Accelerated discovery was a clear theme in the responses, and another theme was concern about the role of human researchers. Survey respondents shared a variety of use cases targeting accelerated materials discovery, including examples where partial automation is preferred over full self-driving laboratories. Building on the observed patterns of researcher priorities and needs, we propose a framework for levels of laboratory autonomy from non-automated (L0) to fully autonomous (L5).

Electrolyte additives in liquid electrolyte batteries can trigger the formation of a protective solid electrolyte interphase (SEI) at the electrodes e.g. to suppress side reactions at the electrodes. Studies of varying amounts of additives have been done over the last few years, providing a comprehensive understanding of the impact of the electrolyte formulation on the lifetime of the cells. However, these studies mostly focused on the variation of the mass fraction of additive in the electrolyte while disregarding the ratio (r_add) of the additive's amount of substance (n_add) to the electrode area (A_electrode). Herein we utilize our accurate automatic battery assembly system (AUTOBASS) to vary electrode area and amount of substance of the additive. Our data provides evidence that reporting the mass ratios of electrolyte components is insufficient and the amount of substance of additive relative to the electrodes' area should be reported. Herein, the two most utilized additives, namely fluoroethylene carbonate (FEC) and vinylene carbonate (VC) were studied. Each additive was varied from 0.1 wt-%–3.0 wt-% for VC, and 5 wt-%–15 wt-% for FEC for two electrode loadings of 1 mA h cm⁻² and 3 mA h cm⁻². To help the community to find better descriptors, such as the proposed r_add, we publish the dataset alongside this manuscript. The active electrode placement correction reduces the failure rate of our automatically assembled cells to 3%.

Applications of advanced artificial intelligence (AI) methods in the materials science domain has grown significantly in recent years resulting in numerous research efforts spanning diverse aspects of materials design, materials synthesis, and materials characterization. The AI for Accelerated Materials Design (AI4Mat) workshop at NeurIPS 2023 featured many of the ongoing major research themes by bringing together an international interdisciplinary community of researchers and enthusiasts across academia, industry, and national labs. The goal of these discussions was to highlight cutting-edge work from active researchers in these fields and uncover major impactful research problems that the community can jointly address. In this article, the AI4Mat-2023 organizing committee showcases the major developments in the field as well as ongoing research challenges where innovative solutions can bring transformative changes to the state-of-the-art in applying AI for accelerated materials design. The editors of Digital Discovery are pleased to feature this overview, and a selection of these manuscripts, in a new themed collection.

Transition metal complexes have played crucial roles in various homogeneous catalytic processes due to their exceptional versatility. This adaptability stems not only from the central metal ions but also from the vast array of choices of the ligand spheres, which form an enormously large chemical space. For example, Rh complexes, with a well-designed ligand sphere, are known to be efficient in catalyzing the C–H activation process in alkanes. To investigate the structure–property relation of the Rh complex and identify the optimal ligand that minimizes the calculated reaction energy ΔE of an alkane C–H activation, we have applied a Δ-machine learning method trained on various features to study 1743 pairs of reactants (Rh(PLP)(Cl)(CO)) and intermediates (Rh(PLP)(Cl)(CO)(H)(propyl)). Our findings demonstrate that the models exhibit robust predictive performance when trained on features derived from electron density (R² = 0.816), and SOAPs (R² = 0.819), a set of position-based descriptors. Leveraging the model trained on xTB-SOAPs that only depend on the xTB-equilibrium structures, we propose an efficient and accurate screening procedure to explore the extensive chemical space of bisphosphine ligands. By applying this screening procedure, we identify ten newly selected reactant–intermediate pairs with an average ΔE of 33.2 kJ mol⁻¹, remarkably lower than the average ΔE of the original data set of 68.0 kJ mol⁻¹. This underscores the efficacy of our screening procedure in pinpointing structures with significantly lower energy levels.

Automation technologies and data science techniques have been successfully applied to optimisation and discovery activities in the chemical sciences for decades. As the sophistication of these techniques and technologies have evolved, so too has the ambition to expand their scope of application to problems of significant synthetic difficulty. Of these applications, some of the most challenging involve investigation of chemical mechanism in organometallic processes (with particular emphasis on air- and moisture-sensitive processes), particularly with the reagent and/or catalyst used. We discuss herein the development of enabling methodologies to allow the study of these challenging systems and highlight some important applications of these technologies in problems of considerable interest to applied synthetic chemists.

Accurate and comprehensive material databases extracted from research papers are crucial for materials science and engineering, but their development requires significant human effort. With large language models (LLMs) transforming the way humans interact with text, LLMs provide an opportunity to revolutionize data extraction. In this study, we demonstrate a simple and efficient method for extracting materials data from full-text research papers leveraging the capabilities of LLMs combined with human supervision. This approach is particularly suitable for mid-sized databases and requires minimal to no coding or prior knowledge about the extracted property. It offers high recall and nearly perfect precision in the resulting database. The method is easily adaptable to new and superior language models, ensuring continued utility. We show this by evaluating and comparing its performance on GPT-3 and GPT-3.5/4 (which underlie ChatGPT), as well as free alternatives such as BART and DeBERTaV3. We provide a detailed analysis of the method's performance in extracting sentences containing bulk modulus data, achieving up to 90% precision at 96% recall, depending on the amount of human effort involved. We further demonstrate the method's broader effectiveness by developing a database of critical cooling rates for metallic glasses over twice the size of previous human curated databases.

Machine learning is becoming an important tool for predicting peptide function that holds promise for accelerating their discovery. In this paper, we explore feature selection techniques to improve data mining of antimicrobial and catalytic peptides, boost predictive performance and model explainability. SMILES is a widely employed software-readable format for the chemical structures of peptides, and it allows for extraction of numerous molecular descriptors. To reduce the high number of features therein, we conduct a systematic data preprocessing procedure including the widespread wrapper techniques and a computationally better solution provided by the filter technique to build a classification model and make the search for relevant numerical descriptors more efficient without reducing its effectiveness. Comparison of the outcomes of four model implementations in terms of execution time and classification performance together with Shapley-based model explainability method provide valuable insight into the impact of feature selection and suitability of the models with SMILE-derived molecular descriptors. The best results were achieved using the filter method with a ROC-AUC score of 0.954 for catalytic and 0.977 for antimicrobial peptides, with the execution time of feature selection lower by 2 or 3 orders of magnitude. The proposed models were also validated by comparison with established models used for the prediction of antimicrobial and catalytic functions.

Machine learning models for chemical retrosynthesis have attracted substantial interest in recent years. Unaddressed challenges, particularly the absence of robust evaluation metrics for performance comparison, and the lack of black-box interpretability, obscure model limitations and impede progress in the field. We present an automated benchmarking pipeline designed for effective model performance comparisons. With an emphasis on user-friendly design, we aim to streamline accessibility and facilitate utilisation within the research community. Additionally, we suggest and perform a new interpretability study to uncover the degree of chemical understanding acquired by retrosynthesis models. Our results reveal that frameworks based on chemical reaction rules yield the most diverse, chemically valid, and feasible reactions, whereas purely data-driven frameworks suffer from unfeasible and invalid predictions. The interpretability study emphasises that incorporating reaction rules not only enhances model performance but also improves interpretability. For simple molecules, we show that Graph Neural Networks identify relevant functional groups in the product molecule, offering model interpretability. Sequence-to-sequence Transformers are not found to provide such an explanation. As the molecule and reaction mechanism grow more complex, both data-driven models propose unfeasible disconnections without offering a chemical rationale. We stress the importance of incorporating chemically meaningful descriptors within deep-learning models. Our study provides valuable guidance for the future development of retrosynthesis frameworks.