A machine learning-guided mechanochemical protocol enabling rapid, solvent-free cyclopropanation and epoxidation under mild, air-equilibrated conditions is reported, establishing a foundation for the integration of Machine Learning and mechanochemistry in designing industrially relevant transformations that prioritize safety and sustainability.
Abstract
The Johnson-Corey-Chaykovsky reaction stands as an elegant approach for the synthesis of cyclopropanes and epoxides. However, most procedures still rely on the original NaH/DMSO conditions, which pose notable safety and handling issues especially in view of industrial applications. Herein, we combine Bayesian Optimization and mechanochemistry to develop a rapid, solvent-free protocol for the Johnson-Corey-Chaykovsky reaction. By prioritizing efficiency and sustainability, Machine Learning quickly identified a new set of reaction conditions for this transformation, also demonstrating that these transformations can proceed efficiently under air-equilibrated, mild conditions using an inexpensive and safe base (KOH). The method is broadly applicable, scalable, and tolerant to diverse functional groups and enabled the preparation of a wide variety of three-membered homo- and heterocycles. Time-Resolved in situ X-ray Powder Diffraction experiments highlighted the crucial role of active milling in promoting this transformation. Overall, this work establishes a foundation for the integration of Machine Learning and mechanochemistry in designing industrially relevant transformations that prioritize safety and sustainability. The Johnson–Corey-Chaykovsky reaction traditionally relies on hazardous conditions that limit its sustainability and industrial practicality. Here, the authors report a machine learning-guided mechanochemical protocol enabling rapid, solvent-free cyclopropanation and epoxidation under mild, air-equilibrated conditions.
A small-sample, accelerated evolution strategy that integrates focused rational iterative site-specific mutagenesis (FRISM) with the EVOLVEpro model is reported, providing a robust, "lightweight" machine learning framework for the rapid development of new-to-nature photoenzymatic transformations.
A workflow for its feasibility is established by combining first-principles calculations and machine learning to quickly predict SHS reactions of MAX and MAB phases, confirming the practical utility of the calculated thermodynamic properties and T ad .
H. Yin, Wanjun Yu, Yongdong Yu et al.· npj Computational Materials· 0 citations
The discovery and development of high-performance catalysts, which is crucial across all catalysis areas, requires advanced technologies and innovative approaches. Recently, machine learning (ML) has shown promise in accelerating this process, but its capability and examples of discovery of truly novel catalysts have remained limited. In this study, we describe an ML approach that goes beyond the traditional element pool, incorporating elements that have not been previously studied, to develop highly efficient catalysts for ethanol synthesis via CO2 hydrogenation. Starting with an initial data set of 58 catalysts (274 data points obtained at reaction temperatures ranging from 240-400 °C), we conducted 24 iterations of a closed-loop discovery system (ML predictions + experimental validation), testing a total of 555 catalysts (2477 data points), and building a large experimental data set. More than 50 catalysts with superior activity were discovered through this data-driven approach. The multielemental Pd(0.8)-Au(0.3)/K(2.5)-Sr(1)-Fe(20)-Zn(4)-Cd(2)-Yb(1)-Re(1)/CeO2(25%)-ZrO2 catalyst, where the numbers in parentheses represent weight percent (wt %), was identified as the most effective catalyst for ethanol synthesis (ethanol space-time yield: 8.2 mmol gcat-1 h-1 with a CO2 conversion of 57.6% and an ethanol selectivity of 23.2% under reaction conditions of 360 °C, 4 MPa, 12 L gcat-1 h-1, H2/CO2 = 3/1). Comprehensive characterizations, including in situ/operando techniques such as X-ray absorption spectroscopy (XAS), ambient-pressure X-ray photoelectron spectroscopy (AP-XPS), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), enable us to highlight the critical roles of each constituting element in improving ethanol synthesis efficiency.
Pengfei Du, Abdellah Ait El Fakir, S. Mine et al.· Journal of the American Chem...· 0 citations
Metal phosphosulfides have emerged as unique multifunctional materials, but they present unique synthesis challenges compared to more established material classes such as oxides and nitrides. As a consequence, experimental development and theoretical understanding of phosphosulfides have focused on individual compounds rather than on accelerated broad-range exploration. In this work, we first evaluate the synthesizability and band gaps of 909 hypothetical ternary phosphosulfides by density functional theory. We find 19 previously unknown thermodynamically stable compounds, including the first Si- and Ge-based phosphosulfides. For rapid band gap prediction, we then develop a multi-fidelity machine learning model to translate semilocal density functional theory band gaps into experimentally calibrated band gaps. Importantly, we extend the accelerated material development workflow to the experimental domain by demonstrating a route to high-throughput synthesis and characterization of virtually any phosphosulfide material system. The method is based on thin-film combinatorial libraries and yields over 100 unique compositions in each experiment, enabling us to synthesize four distinct phosphosulfide compounds in only four combinatorial experiments without prior synthesis recipes and without compromising on material quality. Thus, we argue that accelerated materials development workflows combining theory, artificial intelligence, synthesis, and characterization can be viable even for experimentally challenging inorganic materials.
J. Sanz Rodrigo, Nicholas A. Kryger-Nelson, Lena A. Mittmann et al.· Small· 1 citation
Reaction optimization is a very time- and resource-intensive process, as optimal reaction conditions depend highly on specific electronic and steric properties of the substrate identity and require extensive fine-tuning of synthetic conditions to arrive at the highest-yielding conversions. Amide couplings, which comprise nearly 40% of synthetic transformations performed in a medicinal chemistry setting, present a particularly challenging context for predictive modeling given the diversity of coupling agents and reaction parameters that must be matched to substrate reactivity. Amide coupling reaction data is particularly well-suited for machine-learning approaches that predict optimal reaction conditions given a particular substrate feature set. Herein, we report a platform for standardizing and filtering open-source reaction data from the ORD (Open Reaction Database) and using this machine-readable data set of 3800 amide coupling reactions to evaluate 13 machine learning models. These include linear, tree-based, kernel method, instance-based, neural network, and ensemble architectures in the yield prediction and classification of coupling agents in amide coupling reactions. Yield prediction remained a difficult task because of the complexity of our reaction data, achieving R 2 scores of only 0.61. However, the models were largely successful in classifying reactions to their ideal coupling agent category (carbodiimide-based, uronium salts, or phosphonium salts) with ensemble- and kernel-based models achieving up to 87% accuracy. To further validate this approach, we deployed the classification models on literature-reported data not in the ORD database, achieving similarly high predictive performance. Our results demonstrate that kernel methods and ensemble-based architectures perform significantly better than other models such as linear or single-tree. Additionally, molecular environment features, captured by XYZ coordinates, three-dimensional features, and Morgan fingerprints around reactive functional groups, boosted model predictivity more than bulk material properties derived from SMILES such as molecular weight and log P.
Abhinav Sai Chalasani, Sourodeep Deb, Aarav Anand et al.· ACS Omega· 0 citations
The sluggish kinetics of the ammonia oxidation reaction constitute a critical bottleneck in the development of low‐temperature direct ammonia fuel cells. High‐entropy alloys (HEAs), owing to their diverse active sites, have emerged as promising catalysts. However, their vast compositional space makes traditional quantum chemical screening prohibitively expensive. In this study, we provide a computational proof of concept showing that the classical
d
‐band center theory fails to predict the ammonia oxidation activity of quinary HEAs and exhibits a negligible correlation with the energy barrier of the rate‐determining step. To address this limitation, we employed the Sure Independence Screening and Sparsifying Operator (SISSO) method to construct a transparent and interpretable symbolic descriptor, achieving excellent predictive accuracy (
R
2
= 0.981) within the range of the training data. This descriptor extends beyond simple single‐electron parameters by integrating the synergistic effects of electron‐donating ability, lattice stiffness, and local electronegativity perturbations. This data‐driven approach reduces computational costs by several orders of magnitude relative to exhaustive density functional theory (DFT) screening and identifies an “isolated‐surrounded” geometric configuration as a highly active site that significantly enhances intrinsic catalytic activity from a thermodynamic perspective. Crucially, the predicted motif should be interpreted within the hydrazine‐mediated thermodynamic framework used here, and its thermodynamic superiority may shift if alternative kinetic pathways dominate under operating conditions. This structural motif promotes efficient NN coupling while suppressing site poisoning. Overall, this study provides a coordination chemistry‐based blueprint for the rational design of next‐generation catalysts with reduced platinum‐group‐metal content and offers a theoretical framework for future experimental validation.
Shangfeng Jiang, Ting Tao, Kexiang Guo et al.· ENERGY & ENVIRONMENTAL M...· 0 citations