Self-Driving Laboratories for Chemistry and Materials Science.

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science.

Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspective, we first outline current applications across a diversity of problems in chemistry. Then, we discuss how machine learning researchers view and approach problems in the field. Finally, we provide our considerations for maximizing impact when researching machine learning for chemistry.

Nuclearity and Host Effects of Carbon-Supported Platinum Catalysts for Dibromomethane Hydrodebromination.

The identification of the active sites and the derivation of structure-performance relationships are central for the development of high-performance heterogeneous catalysts. Here, a platform of platinum nanostructures, ranging from single atoms to nanoparticles of ≈4 nm supported on activated- and N-doped carbon (AC and NC), is employed to systematically assess nuclearity and host effects on the activity, selectivity, and stability in dibromomethane hydrodebromination, a key step in bromine-mediated methane functionalization processes. For this purpose, catalytic evaluation is coupled to in-depth characterization, kinetic analysis, and mechanistic studies based on density functional theory. Remarkably, the single atom catalysts achieve exceptional selectivity toward CH3 Br (up to 98%) when compared to nanoparticles and any previously reported system. Furthermore, the results reveal unparalleled specific activity over 1.3-2.3 nm-sized platinum nanoparticles, which also exhibit the highest stability. Additionally, host effects are found to markedly affect the catalytic performance. Specifically, on NC, the activity and CH3 Br selectivity are enhanced, but significant fouling occurs. On the other hand, AC-supported platinum nanostructures deactivate due to sintering and bromination. Simulations and kinetic fingerprints demonstrate that the observed reactivity patterns are governed by the H2 dissociation abilities of the catalysts and the availability of surface H-atoms.

Electrochemical Reduction of Carbon Dioxide to 1-Butanol on Oxide-Derived Copper.

The electroreduction of carbon dioxide using renewable electricity is an appealing strategy for the sustainable synthesis of chemicals and fuels. Extensive research has focused on the production of ethylene, ethanol and n-propanol, but more complex C4 molecules have been scarcely reported. Herein, we report the first direct electroreduction of CO2 to 1-butanol in alkaline electrolyte on Cu gas diffusion electrodes (Faradaic efficiency=0.056 %, j1-Butanol =-0.080 mA cm-2 at -0.48 V vs. RHE) and elucidate its formation mechanism. Electrolysis of possible molecular intermediates, coupled with density functional theory, led us to propose that CO2 first electroreduces to acetaldehyde-a key C2 intermediate to 1-butanol. Acetaldehyde then undergoes a base-catalyzed aldol condensation to give crotonaldehyde via electrochemical promotion by the catalyst surface. Crotonaldehyde is subsequently electroreduced to butanal, and then to 1-butanol. In a broad context, our results point to the relevance of coupling chemical and electrochemical processes for the synthesis of higher molecular weight products from CO2 .

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs).

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research.

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back.

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

Performance Prediction of High-Entropy Perovskites La0.8Sr0.2MnxCoyFezO3 with Automated High-Throughput Characterization of Combinatorial Libraries and Machine Learning.

Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition-structure-performance relationships of high-entropy La0.8Sr0.2MnxCoyFezO3±ðž­ perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. Following the deposition of a continuous compositional map using thin-film combinatorial pulsed laser deposition, compositional, structural, and performance properties are characterized using six different techniques with mapping capabilities. Random forests effectively model electrochemical performance, consistently identifying Fe-rich oxides as optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700 °C. Additionally, the models identify a statistical correlation between oxygen sublattice distortion-derived from spectral analysis of Raman-active modes-and enhanced performance.

Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks.

Modeling in heterogeneous catalysis requires the extensive evaluation of the energy of molecules adsorbed on surfaces. This is done via density functional theory but for large organic molecules it requires enormous computational time, compromising the viability of the approach. Here we present GAME-Net, a graph neural network to quickly evaluate the adsorption energy. GAME-Net is trained on a well-balanced chemically diverse dataset with C1-4 molecules with functional groups including N, O, S and C6-10 aromatic rings. The model yields a mean absolute error of 0.18 eV on the test set and is 6 orders of magnitude faster than density functional theory. Applied to biomass and plastics (up to 30 heteroatoms), adsorption energies are predicted with a mean absolute error of 0.016 eV per atom. The framework represents a tool for the fast screening of catalytic materials, particularly for systems that cannot be simulated by traditional methods.