Robert Pollice

#RobSelects paper of the week #J_A_C_S: Reversible carbon-hydrogen activation of benzene and unactivated arenes with a nickel(0)-silylene complex. #orgchem https://doi.org/10.1021/jacs.5c10922

CASCADE-2.0: Real Time Prediction of 13C-NMR Shifts with sub-ppm Accuracy Nuclear Magnetic Resonance (NMR) is one of the most powerful structural characterization techniques in molecular sciences. However, the complexity of NMR spectra can make structural assignments prone to er-rors. Here we introduce a deep learning model – CASCADE-2.0 (ChemicAl Shift CAlculation with DEep learn-ing), a practical tool designed to assist chemists in making fast, reliable, and transparent 13C-NMR chemical shift predictions. Building on our previous model, we make improvements to the model architecture and train-ing data, while striving to enhance the model transparency. Leveraging advances in neural network poten-tials, a fourfold expansion of training data in terms of molecular and elemental coverage is made, resulting in a dataset containing around 170,000 experimental shifts cross-validated by DFT. To address DFT limitations, we developed an intelligent data augmentation strategy combining statistical analysis and machine learning predictions to further expand the dataset to 211,000 experimental values. With the expanded dataset and changes in model architecture, a state-of-the-art accuracy of 0.73 ppm was achieved when compared against experimental 13C-NMR shifts. The model also incorporates prediction confidence metrics using a deep-kernel learning architecture, as well as nearest-neighbor analysis, facilitated by a user-friendly web-server. Finally, we demonstrate the versatility of the final model using several real-world applications.

#RobSelects preprint of the week #ChemRxiv: New experimental datasets and an improved machine learning model for predicting carbon-13 nuclear magnetic resonance shifts of organic molecules. #aichem https://doi.org/10.26434/chemrxiv-2025-r8m9m

#RobSelects paper of the week #J_A_C_S: Demonstration of the permanent ferromagnetism of MnBi2 at both 10 K and room temperature. #matchem https://doi.org/10.1021/jacs.5c06874

The 2D-drone swarm, a safe open-source sample transfer system for laboratory full automation Laboratory automation is an active field in biology, drug discovery, and more recently in synthetic chemistry and materials science. Local automation has existed in the field for quite some time, but long-range or total laboratory automation is much less developed. In this article, we present a complete, open and decentralized, global automation system called the 2D drone swarm system. It is based on a simple approach of small mobile robots moving autonomously in a dedicated track suspended above the scientific equipment for the long-distance sample and closely connected to localized robotic arms dedicated to short-distance transfers, interaction with scientific equipment and direct sample processing. This approach is inspired by the Kiva/Amazon model, where isolated autonomous mobile robots automatically deliver goods to external operators. It is also inspired by the modern automotive industry, such as Tesla's Gigafactories, to provide an evolutionary and flexible system that can adapt to numerous types of tasks with a minimum of resources and easily adapt to different types of workstations. This global automation system is controlled directly from the Laboratory Scheduler by a Robot Subscheduler, coded in an open-source environment, which takes care of all mobile and local robot operations. The result is an operator and scientific equipment safe, cost and energy-efficient, easily extensible and open-source global laboratory automation system that can be adapted to many different applications and laboratories.

#RobSelects preprint of the week #ChemRxiv: Transferring samples between automated stations using a swarm of drone carts driving on near ceiling tracks. #autochem https://doi.org/10.26434/chemrxiv-2025-8x3zv

mstdn.science http://mstdn.science is a place for scientists and science enthusiasts to discuss research and topics surrounding our work.

#RobSelects paper of the week #J_A_C_S: Leveraging a 1,3]-hydride shift in alpha-fluoro-diborylalkanes to synthesize cyclopropyl boronic esters. #orgchem [https://doi.org/10.1021/jacs.5c08276

A Flexible and Affordable Self-Driving Laboratory for Automated Reaction Optimization Self-driving laboratories (SDLs) have the potential to revolutionize chemical discovery and optimization, yet their widespread adoption remains limited by high costs, complex infrastructure, and limited accessibility. Here, we introduce RoboChem-Flex, a low-cost, modular self-driving laboratory platform designed to democratize autonomous chemical experimentation. The system combines customizable, in-house-built hardware with a flexible Python-based software framework that integrates real-time device control and advanced Bayesian optimization strategies, including multi-objective and transfer learning workflows. RoboChem-Flex supports both fully autonomous closed-loop operation and human-in-the-loop configurations, enabling seamless integration with shared analytical equipment and minimizing entry barriers. We validate the versatility of the platform across six diverse case studies, including photocatalysis, biocatalysis, thermal cross-couplings, and enantioselective catalysis, spanning both single and multi-objective optimizations. Through these campaigns, we demonstrate RoboChem-Flex’s ability to navigate large, complex chemical spaces, autonomously identify scalable high-performance reaction conditions, and flexibly adapt to a variety of analytical setups. By providing an affordable, scalable, and open platform, RoboChem-Flex offers a tangible step toward making SDLs accessible to resource-limited laboratories, fostering broader participation in automated chemical research.

#RobSelects preprint of the week #ChemRxiv: A frugal flow-based self-driving laboratory platform for optimization of diverse organic reactions. #autochem https://doi.org/10.26434/chemrxiv-2025-73xqf

#RobSelects paper of the week #J_A_C_S: Dirhodium(II)-catalyzed diamination via insertion of rhodium-bound nitrenes into two tetrahedral carbon-hydrogen bonds. #catalysis https://doi.org/10.1021/jacs.5c05969

Click Heterogenization of Phosphines Furnishes Recyclable Hydroformylation Catalysts that Reproduce Homogeneous Performance Heterogeneous catalysts confer notable practical advantages for large-scale reactions, while homogenous catalysts permit targeted performance optimization. A rapid and general method for the heterogenization of molecular transition-metal catalysts without loss of performance would thus permit facile translation of optimized homogeneous catalysts into practical heterogeneous catalysts. Here we show how a wide variety of phosphines carrying anionic substituents can be charge-tethered to the walls of the spacious super-cages of a metal-organic-framework (MOF) to provide an adaptable heterogeneous ligand set in a single synthetic step. The addition of Co2(CO)8 to MOF-heterogenized phosphine ligands provides recyclable, heterogeneous hydroformylation catalysts that faithfully reproduce the performance of the molecular analogues in both activity and selectivity. Key to the solution-like reactivity of the click-heterogenized phosphines is the high degree of mobility of the heterogenized phosphines, which could be directly demonstrated by 31P NMR analysis, and which enables them to effectively accommodate cobalt complexes with three distinct oxidation states and coordination geometries. While the lack of directionality of the ionic interaction between the ligand and the host permits the phosphines to effectively reproduce homogenous catalytic cycles, the strength of the ionic interaction ensures that phosphine leaching remains below 0.05 ppm.

#RobSelects preprint of the week #ChemRxiv: Heterogenizing sulfonated phosphines in a metal organic framework to develop a heterogeneous hydroformylation catalyst matching molecular analogues. #catalysis https://doi.org/10.26434/chemrxiv-2025-wpt9h

#RobSelects paper of the week #angew_chem: Automated procedure for palladium-catalyzed iterative carbon-nitrogen and carbon-carbon cross coupling. #catalysis https://doi.org/10.1002/anie.202509974

g-xTB: A General-Purpose Extended Tight-Binding Electronic Structure Method For the Elements H to Lr (Z=1–103) We present g-xTB, a next-generation semi-empirical electronic structure method derived from tight-binding (TB) approximations to Kohn–Sham density functional theory (KS-DFT). Designed to bridge the gap between semi-empirical quantum mechanical (SQM) approaches and DFT in terms of accuracy, robustness, and general applicability, g-xTB targets the performance of the ωB97M-V range-separated hybrid density functional with large basis sets while maintaining TB speed. Key innovations include an atom-in-molecule adaptive atomic orbital basis, a refined Hamiltonian incorporating range-separated approximate Fock exchange, up to fourth-order charge-fluctuation terms with a novel first-order electronic contribution, and atomic correction potentials (ACPs), as well as a charge-dependent semi-classical repulsion function. Parameterized on extended and extremely diverse molecular training sets – including “mindless molecules” – g-xTB achieves excellent accuracy across a broad chemical space, including the actinide elements. Benchmarking against around 32k relative energies across thermochemistry, conformational energetics, non-covalent interactions, and reaction barriers shows that g-xTB consistently outperforms GFN2-xTB, often reducing mean absolute errors by half. Notably, it achieves a WTMAD-2 of 9.3 kcal mol−1 on the full GMTKN55 benchmark, comparable to low-cost DFT methods. It also shows substantial improvements for transition-metal complexes, relative spin state energies, and orbital energy gaps – areas where many SQM and even DFT methods often struggle. In summary, g-xTB offers DFT-like accuracy with minimal computational overhead compared to its predecessor, GFN2-xTB, making it a robust, minimally empirical, transferable, and efficient alternative to machine learning interatomic potentials for a wide range of molecular simulations. It is proposed as a general replacement for the GFNn-xTB family and, in many practical cases, a viable substitute for low- and mid-level DFT methods.

#RobSelects preprint 1 of the week #ChemRxiv: Reaching density functional approxmation accuracy at the cost of extended tight-binding quantum chemistry. #compchem https://doi.org/10.26434/chemrxiv-2025-bjxvt

Boryl Radical beta-Scission Enables Divergent Deaminative Cross-Coupling of Amines Amines are among the most common functional groups in bioactive molecules and pharmaceuticals, yet they are almost universally treated as synthetic endpoints. Here we report a strategy that repositions native primary, secondary, and tertiary amines as versatile handles for divergent cross-coupling. The platform relies on in situ activation via borane coordination and exploits a copper catalytic redox system that generates amine-ligated boryl radicals, which undergo beta-scission across the C(sp³)–N bond to release alkyl radicals. These intermediates engage in copper-catalyzed cross-couplings with a broad array of C-, N-, O-, and S-based nucleophiles. The method tolerates diverse amine classes, enables modular functionalization, and supports late-stage editing of complex drug scaffolds. In addition, amides can be incorporated into the manifold via reductive funneling. This work establishes a general approach to deaminative C–N bond functionalization and introduces a new logic for retrosynthetic diversification and pharmacophore remodeling.

#RobSelects preprint 2 of the week #ChemRxiv: Copper-catalyzed deaminative cross coupling of alkyl amines with a diverse range of nucleophiles. #catalysis https://doi.org/10.26434/chemrxiv-2025-ch3wj

#RobSelects paper of the week #ChemicalScience: Catalytic carbon-hydrogen activation outcome prediction and ligand design with finetuned language models. #catalysis https://doi.org/10.1039/D5SC01098E

Chemical Space Exploration with Artificial ”Mindless” Molecules We introduce MindlessGen, a Python-based generator for creating chemically diverse, “mindless” molecules through random atomic placement and subsequent geometry optimization. Using this framework, we constructed the MB2061 benchmark set, containing 2061 molecules with high-level PNO-LCCSD(T)-F12 reference data for dissociation reactions. This set provides a challenging benchmark for testing, validation, and training of density functional approximations (DFAs), semiempirical methods, force fields, and machine learning potentials using molecular structures beyond the conventional chemical space. For DFAs, we initially hypothesized that highly parameterized functionals might perform poorly on this set. However, no consistent relationship between fitting strategy and accuracy was observed. A clear Jacob’s ladder trend emerges, with ωB97X-2 achieving the lowest mean absolute error (MAE) of 8.4 kcal·mol−1 and r²SCAN-3c offering a robust cost-efficient alternative (19.6 kcal·mol−1). Furthermore, we discuss the performance of selected semiempirical methods and contemporary machine learned interatomic potentials.

#RobSelects preprint of the week #ChemRxiv: Benchmarking density functional approximations with a systematic set of randomly generated molecules. #compchem https://doi.org/10.26434/chemrxiv-2025-rdsd0

#RobSelects paper of the week #J_A_C_S: Phenanthroline-ligated nickel(I)-alkyl complex reacts rapidly with nitrous oxide at ambient temperature to form alcohols. #catalysis https://doi.org/10.1021/jacs.5c03351

Closed-Loop: Vision-Guided Experimental Control in Self-Driving Labs In iterative optimization, actions are adjusted based on what we see—such as dosing until dissolution or stirring until mixing is complete. Self-driving laboratories (SDLs) offer an opportunity to guide experimental adjustments based on such visual feedback in an autonomous, iterative way. However, current SDLs do not monitor these visual cues. HeinSight 4.0 fills this gap by integrating computer vision into SDLs to enable real-time experimental adjustments based on visual feedback. The computer vision detects equipment (e.g., reactor, vial), classifies chemical phases (solid, liquid, air), and analyzes image features such as turbidity and color. HeinSight 4.0 tracks these physical characteristics frame by frame and interprets physical states (e.g., dissolution, separation). This data feeds into a rule-based system that integrates with the SDL to make real-time experimental adjustments. We demonstrate HeinSight 4.0 adaptability across two pharmaceutical case studies: purification (solubility screening) and drug formulation (melt spray congeal). We also developed a hardware-agnostic architecture and deployed it across two institutions with distinct robotic systems. The open-source HeinSight 4.0 enables SDLs to see, think, and act in real time.

#RobSelects preprint of the week #ChemRxiv: Integrating computer vision into self-driving laboratories. #autochem https://doi.org/10.26434/chemrxiv-2025-sxfvl

#RobSelects paper of the week #J_A_C_S: Accelerating the catalytic Mitsunobu reaction by faster water removal as revealed by rigorous kinetic analysis. #catalysis https://doi.org/10.1021/jacs.5c05404

N-N Bond Formation Through N-H Dehydrocoupling N-N bond formation from amines can be accomplished through proton/electron transfer using simple base and oxidant combinations, where kinetic limitations related to the BDFE(eff) of a particular base/oxidant pair can be overcome using Lewis acid catalysts such as FeCl3.

#RobSelects preprint of the week #ChemRxiv: Nitrogen-nitrogen dehydrocoupling of iminoanilines via iron(III) chloride-catalyzed multi-site proton-coupled electron transfer. #catalyis https://doi.org/10.26434/chemrxiv-2025-vtv4x

#RobSelects paper of the week #angew_chem: Base mediated nucleophilic germylation of carbon-carbon double and triple bonds to form carbon-germanium bonds. #inorgchem https://doi.org/10.1002/anie.202506106

Automated Iterative N-C and C-C Bond Formation Small molecule solutions to many contemporary societal challenges await discovery, but the artisanal and manual process via which this class of chemical matter is typically accessed limits the discovery of new functions. Automated iterative cross-coupling with (N-methyl iminodiacetic acid) MIDA or (tetramethyl N-methyl iminodiacetic acid) TIDA boronate building blocks alternatively enables generalized and automated preparation of many different types of small molecules in a modular fashion. But in its current form, this engine cannot leverage nitrogen atoms as iteration handles. Here, we disclose a new iteration-enabling group, CbzT, that reversibly attenuates the reactivity of nitrogen atoms and enables generalized catch-and-release purification. CbzT is leveraged to achieve the automated modular synthesis of Imatinib (Gleevec), an archetypical clinically approved kinase inhibitor, in which building blocks are iteratively linked by both N-C and C-C bonds. This work substantially expands the types of small molecules that can be made in an automated modular fashion. It also advances the concept of intentionally developing chemistry that machines can do.

#RobSelects preprint of the week #ChemRxiv: Enabling automated iterative carbon-nitrogen cross coupling via a boronate-substituted carboxy benzyl protecting group for amines. #autochem https://doi.org/10.26434/chemrxiv-2025-fvznl

#RobSelects paper of the week #angew_chem: Highly enantioselective Mukaiyama aldol reaction catalyzed by a chiral bidentate iodine(I)-based halogen bond donor catalyst. #catalysis https://doi.org/10.1002/anie.202506476

GoFlow: Efficient Transition State Geometry Prediction with Flow Matching and E(3)-Equivariant Neural Networks Transition state (TS) geometries of chemical reactions are key to understanding reaction mechanisms and estimating kinetic properties. Inferring these directly from 2D reaction graphs offers chemists a powerful tool for rapid and accessible reaction analysis. Quantum chemical methods for computing TSs are computationally intensive and often infeasible for larger molecular systems. Recently, deep learning–based diffusion models have shown promise in generating TSs from 2D reaction graphs for single-step reactions. However, framing TS generation as a diffusion process, by design, requires a prohibitively large number of sampling steps during inference. Here we show that modeling TS generation as an optimal transport flow problem, solved via E(3)-equivariant flow matching with geometric tensor networks, achieves over a hundredfold speedup in inference while improving geometric accuracy compared to the state-of-the-art. This breakthrough increase in sampling efficiency and predictive accuracy enables the practical use of deep learning-based TS generators in high-throughput settings for larger and more complex chemical systems. Our method, GoFlow, thus represents a significant methodological advancement in machine learning-based TS generation, bringing it closer to widespread use in computational chemistry workflows.

#RobSelects preprint of the week #ChemRxiv: Efficient prediction of transition state geometries from molecular strings of starting materials and products via E(3)-equivariant flow-matching. #aichem https://doi.org/10.26434/chemrxiv-2025-bk2rh

#RobSelects paper of the week #JCTC: Implementing computationally efficient and precise second-order derivatives for an equivariant graph neural network architecture for molecules via automatic differentiation. #aichem https://doi.org/10.1021/acs.jctc.4c01790

Data-Driven Prediction of Enantioselectivity for the Sharpless Asymmetric Dihydroxylation: Model Development and Experimental Validation The Sharpless asymmetric dihydroxylation remains a key transformation in chemical synthesis, yet its success hides unexpected cases of lower selectivity. A chemoinformatic workflow was developed to allow data-driven analysis of the reaction. A database of 1007 reactions employing AD-mix α and β was curated from the literature, and an alignment-dependent, fragment-based featurization of alkenes was implemented for modeling. This platform converged on machine learning models capable of predicting the magnitude of enantioselectivity for multiple alkene classes, achieving Q2F3 values ≥ 0.8, test r2 values ≥ 0.7 and mean absolute errors (MAE) ≤ 0.3 kcal/mol. The features of alkenes contributing to model performance were assessed with SHapley Additive exPlanations (SHAP) analysis to gather insight into factors underlying predictions. Experimental validation demonstrated that the models could achieve meaningful predictions on numerous out-of-sample alkenes.

#RobSelects preprint of the week #ChemRxiv: Developing an enantioselectivity prediction workflow for asymmetric Sharpless dihydroxylation. #catalysis https://doi.org/10.26434/chemrxiv-2025-zp7rn

#RobSelects paper of the week #J_A_C_S: Detailed investigation of the methyl lactate van der Waals dimers via jet-cooled rotational spectroscopy. #physchem https://doi.org/10.1021/jacs.5c01184

OMNI-P2x: A Universal Neural Network Potential for Excited-State Simulations Photo-active molecular systems play an essential role in modern science and technology, finding applications in solar cells, organic light-emitting diodes (OLEDs), reaction catalysis, photodynamic therapy, and beyond. The rational design of photo-responsive molecules requires understanding of the photophysical and photochemical processes underlying their operation. This understanding can be gained via the first-principles quantum-mechanical (QM) calculations which, however, turn out prohibitively expensive for high-throughput investigations. To break through this limitation, here we introduce OMNI-P2x: the first universal neural network potential for molecular excited and ground electronic states. OMNI-P2x can be used, directly or after fine-tuning, in place of quantum-mechanical methods to perform a wide range of photophysical and photochemical simulations. OMNI-P2x is approaching the accuracy of time-dependent density functional theory (TD-DFT) methods at a fraction of the cost. Remarkably, this universal potential is more accurate and faster than established semiempirical QM methods, marking the watershed moment in theoretical method development for excited-state simulations. Here, we demonstrate its use in UV/Vis absorption spectroscopy, in real-time photodynamical simulations, and in the rational design of the visible-light-absorbing azobenzene systems.

#RobSelects preprint of the week #ChemRxiv: A universal neural network potential for excited state simulations of organic molecules. #aichem https://doi.org/10.26434/chemrxiv-2025-j207x

#RobSelects paper of the week #angew_chem: Probing electrostatic interactions in nucleophilic substitutions through rotation rates in molecular motors with analogous structures. #physorg https://doi.org/10.1002/anie.202505483

AIQM2: Organic Reaction Simulations Beyond DFT Density functional theory (DFT) is the workhorse of reaction simulations but it either suffers from prohibitive cost or insufficient accuracy. In this work, we report AIQM2, the universal AI-enhanced QM Method 2, the first method that enables fast and accurate large-scale organic reaction simulations for practically relevant system sizes and time scales beyond what is possible with DFT. This breakthrough is based on the outstanding speed of AIQM2, orders of magnitude faster than common DFT, while its accuracy in reaction energies, transition state optimizations, and barrier heights is at least at the level of DFT and often approaches the gold-standard coupled cluster accuracy. AIQM2 can be used out of the box without any further retraining. Compared to pure machine learning potentials, AIQM2 possesses high transferability and robustness in simulations without catastrophic breakdowns. We showcase the superiority of AIQM2 compared to traditional DFT by performing an extensive reaction dynamics study overnight and revising the mechanism and product distribution reported in the previous investigation of the bifurcating pericyclic reaction.

#RobSelects preprint of the week #ChemRxiv: Combining semiempirical quantum chemistry with transferable neural network potentials and an atom-pairwise dispersion correction in AIQM2 #aichem https://doi.org/10.26434/chemrxiv-2024-j8pxp-v2

#RobSelects paper of the week #J_A_C_S: Reductive carbon dioxide activation by a dinuclear iron-aluminum complex. #inorgchem https://doi.org/10.1021/jacs.5c00944

``Slim'' benchmark sets for faster method development The construction of large benchmark sets has accelerated advancement of quantum chemistry methods, especially in density functional theory and lower-cost methods. However, these large benchmark sets can be unsuitable for cutting-edge method development, because research codes developed for fundamentally new approaches are often inefficient and may consequently struggle to handle large molecules. Here, we introduce \texttt{Slim} benchmark sets that are designed to `summarize' the statistics of larger counterparts, but have the advantage that molecules are restricted in size (to five, 16 and 20 atoms) and may therefore be treated by inefficient implementations. They thereby allow data-driven methodologies to be exploited in the early stages of cutting-edge method development.

#RobSelects preprint of the week #ChemRxiv: Restricting computational chemistry benchmark sets in size while keeping their error distributions. #compchem https://doi.org/10.26434/chemrxiv-2025-949xn

#RobSelects paper of the week #J_A_C_S: Leveraging transfer learning to improve ligand performance prediction in an atroposelective nickel-catalyzed Suzuki-Miyaura cross-coupling. #catalysis https://doi.org/10.1021/jacs.5c00838