Daniel P. Tabor

Mechanical forces can alter chemical reactivity by reshaping the potential energy surface landscape, but the relationships between force direction, molecular structure, and barrier structure remain poorly understood. Here, we develop a compliance-based framework for understanding primary mechanochemical activation by means of the compliance matrix and test it across a systematically designed chemical space of cyclopentadiene-based Diels-Alder reactions. Under the harmonic approximation, the force-induced change in a barrier height is determined by the difference in the compliances along the direction of force between the reactant complex and the transition state. We then evaluate this model across 233 Diels-Alder reactions, generating 1,271 reaction profiles and performing over 284,448 force-modified critical point calculations enabled by frontier machine-learned interatomic potentials. When deformation is restricted to a single coordinate, where the compliance matrix is effectively diagonal, the mechanochemical response depends strongly on the selected distortion, with dihedral motions providing a particularly effective mode of activation. However, upon optimizing in the presence of force, including the off-diagonal elements of the compliance matrix, the barrier-lowering behavior becomes widespread and is substantially less sensitive to the initial force direction. This behavior arises from the finding that reactant complexes are generally more compliant than their corresponding transition states. We show that a global reactant compliance descriptor captures this trend and identifies substituent-dependent differences in mechanochemical susceptibility, including candidate reactions for observing changes in stereoselectivity. These results show that mechanochemical activation for this reaction class does not require the force to be aligned with the traditional reaction coordinate. Instead, differential distortability across the reactant and transition states provides a general physical principle for understanding and potentially designing force-sensitive reactions. Finally, by extending this model to understand reactions under hydrostatic pressure conditions, this work provides insight into primary mechanochemistry across all distortion modes of molecular systems.

Non-conjugated redox-active polymers (NC-RAPs) in the dissolved state are important for future energy applications such as redox flow batteries. However, there remains a knowledge gap in understanding factors that strongly influence the redox kinetics and mechanisms of NC-RAPs. Here, we determine how concentration and backbone structure of phthalimide-based NC-RAPs influence the solution-state electrochemical activity, as described by the apparent diffusion coefficient (Dapp) and the homogeneous (kex,app) and heterogeneous (k0) rate constants. Three phthalimide-containing polymers with epichlorohydrin, methacrylate, and vinylbenzene backbones are compared experimentally and computationally. As polymer concentration increases from the dilute to the semidilute regimes, the diffusion and kinetics slow down. Interestingly, the polymer backbone does not strongly influence the redox kinetics of these phthalimide-based polymers. In total, these results show that, above the overlap concentration, single-file diffusion and interdependent flux of redox species lead to slower diffusion and kinetics. These findings provide mechanistic insights into the future molecular design of NC-RAPs in the dissolved state, enabling energy storage systems such as redox flow batteries with improved kinetics.

We report the IR spectroscopy and structural analysis of the halide ions F–, Cl–, and Br– bound inside a bicyclic amide-based cryptand cage (1) under cryo-cooled conditions in the gas phase. We record infrared spectra of the cryo-cooled gas-phase ions in the hydride stretch and fingerprint regions using IR-UV double resonance methods. The IR spectra in the NH and CH stretch regions are remarkably simple, reflecting highly symmetric structures for all three 1·X– complexes. We compare the experimental spectra with calculated candidate structures using first-principles predictions of the infrared spectra in the NH, CH, and fingerprint regions. We assign the 1·F– complex to a 9-coordinate, D3 symmetry structure that contains six NH···F– and three aromatic CH···F– H-bonds, much as it does in the crystalline solid, with a trigonal twist angle α = 35°. While the gas-phase and X-ray structures of 1·F– are similar, the crystal structure is compressed by its surroundings, with the distance between the two bridgehead tertiary amines that is 0.58 Å shorter than our assigned gas-phase structure. In contrast, the 1·Cl– and 1·Br– complexes are uniquely assigned based on their infrared spectra to C3h symmetry structures in which the six amide N’s form a 6-coordinate trigonal prism (α = 0°) binding pocket, while the two tertiary N’s form caps on the prism’s triangular faces. To accommodate these larger halide ions, the three aromatic rings rotate so that their closest CH groups are nearly tangential to the spherical halide ion at the cage’s center.

Theoretical vibrational spectroscopy methods are often necessary to interpret condensed-phase spectra. For some methods, such as those that use vibrational spectroscopic maps, the requisite atomistic molecular dynamics simulations can become prohibitively expensive for systems of interest. Here, we present a two-stage method for predicting amide-I region infrared spectra from coarse-grained protein simulations, which reduces the computational cost of spectral prediction. The first stage is a minimal, spectroscopically motivated backmapping diffusion model, and the second stage is a transformer-based, site-frequency perturbation model. The predictions replace the standard vibrational spectroscopic maps while preserving the core of the spectral calculation. We show that this framework predicts accurate infrared spectra for a wide variety of proteins. Finally, we show that the framework is modular, allowing for the integration of existing coarse-grained backmapping models.

Charge and mass transfer are key functionalities of redox-active polymers, impacting applications in energy storage. Tacticity affects polymer chain conformation and mobility, free volume, and redox unit spacing, but the consequences on their ability to store charge remain unclear. Here, we control tacticity in poly(2,2,6,6-tetramethylpiperidinyloxy methacrylate) (PTMA) and demonstrate its decisive influence on electrochemical properties. Isotactic, atactic, and syndiotactic PTMA with mm ∼80%, mr or rm ∼44%, and rr ∼77% configurations, respectively, are synthesized by anionic polymerization. Overall, isotactic PTMA possessed the highest specific capacity and energy -37% higher than syndiotactic PTMA at 5C. Electrochemical analysis of the reaction diffusion and kinetics, as well as electrochemical quartz crystal microbalance with dissipation monitoring (EQCM-D) analysis of the mass transfer process, reveal that specific capacity and energy are influenced by both processes. Of the three polymers studied, isotactic PTMA exhibited moderate charge transfer kinetics but the least mass transferred in the redox reaction. Meanwhile, atactic PTMA exhibited the fastest kinetics but the most mass transferred. All-atom molecular dynamics (MD) simulations explain this effect, showing that atactic PTMA bears the lowest density and the greatest conformational heterogeneity, leaving greater free volume for solvated ion (de)insertion. This work highlights that tacticity is an important consideration in developing next-generation nonconjugated redox-active polymers for electrochemical energy storage.

The functional role of biomolecular condensates is shaped by the composition of constituent proteins, nucleic acids, ions, and small molecules. Selective partitioning of small molecules into condensates has therefore emerged as a potential route to condensate-specific chemical probes and therapeutics. Although partitioning is influenced by differences in solvation environments between coexisting dense and dilute phases, a molecular framework connecting small-molecule structure to condensate-specific enrichment remains lacking. Here, we use existing experimental partitioning data for a library of FDA-approved drugs and metabolites across four biomolecular condensates to develop an interpretable graph-based model of small-molecule partitioning. By combining multitask pretraining, condensate-specific fine-tuning, evidential uncertainty quantification, and atom-level attribution analysis, our model predicts continuous partition coefficients with improved accuracy over descriptor-based approaches. Atom-level attributions reveal that condensate partitioning is not governed by a universal chemical rule: the same molecular scaffold can be read differently by distinct condensate environments, with local atomic context and connectivity determining whether specific atoms promote or suppress enrichment. We further apply the trained model to ~1.7 million drug-like molecules from ChEMBL, identifying a chemically diverse space of predicted condensate-selective partitioners and mapping regions where predictions are confident versus where new measurements would be most informative. Together, this work establishes condensate partitioning as a chemically learnable property shaped by the interplay between small-molecule structure and condensate-specific microenvironments, providing an interpretable and uncertainty-aware framework for defining molecular determinants of partitioning and guiding the discovery of condensate-selective small molecules.

Internal coordinates and their derivatives underpin the efficient treatment of geometry optimizations, high-resolution spectroscopic simulation, and the fitting of potential surfaces in quantum chemistry. Existing descriptions of the construction of internal coordinate derivatives generally either lack simplicity or generality. In this paper, we provide a simple framework for evaluating any internal coordinate derivative to any order and an automatic approach to obtain the corresponding inverse transformation. Through further extension to transformations between internal coordinate systems, this approach provides a complete, generic method for handling a wide variety of molecular problems. The utility of this construction is demonstrated by investigations into the behavior of internal coordinate interpolations for studying isomerizations, quantifying the coupling between carbonyl stretches and a complex stretch coordinate in an organometallic system, and analysis of the performance of a machine learned interatomic potential in computing anharmonic frequencies as a function of low-frequency coordinate distortions. This approach is shown to be numerically efficient as well as general, and a reference implementation is provided.

Transitions between rotationally ordered and disordered states in globular small molecules are associated with large entropy changes and thus hold promise as solid-state barocaloric refrigerants. However, the relationships between elements of the molecular structure and the corresponding thermodynamic properties of the phase transformation between ordered and disordered states remain unresolved. We hypothesize that more spherical molecules, as measured by their rotational moments of inertia, exhibit larger relative increases in their rotational degrees of freedom as they transition to rotationally disordered states. We probe this by isolating the impact of rotational moments of inertia from more dominant factors, including intermolecular bonding, through the selective deuteration of different functional groups of the model plastic crystal molecule neopentyl glycol. We demonstrate a decrease in the phase transition temperature of up to approximately 3 K associated with the existence of deuterated methyl groups and explain this change in terms of relative changes in the rotational moments of inertia of the compounds. This observation places bounds on the role of rotational moments of inertia in thermodynamic aspects of the phase transformation and introduces a vector for subtle modulation of the transition point for cooling applications.

The 2D IR spectra of a set of stable redox states in a diiron trinitrosyl complex are analyzed with reduced dimensional perturbation theory.

Internal coordinates and their derivatives underpin the efficient treatment of geometry optimizations, high-resolution spectroscopic simulation, and the fitting of potential surfaces in quantum chemistry. Existing descriptions of the construction of internal coordinate derivatives generally either lack simplicity or generality. In this paper, we provide a simple framework for evaluating any internal coordinate derivative to any order and an automatic approach to obtain the corresponding inverse transformation. Through further extension to transformations between internal coordinate systems, this approach provides a complete, generic method for handling a wide variety of molecular problems. The utility of this construction is demonstrated by investigations into the behavior of internal coordinate interpolations for studying isomerizations, quantifying the coupling between carbonyl stretches and a complex stretch coordinate in an organometallic system, and analysis of the performance of a machine learned interatomic potential in computing anharmonic frequencies as a function of low-frequency coordinate distortions. This approach is shown to be numerically efficient as well as general, and a reference implementation is provided.