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Strategies to Obtain Reliable Energy Landscapes from Embedded Multireference Correlated Wavefunction Methods for Surface Reactions
Embedded correlated wavefunction (ECW) theory is a powerful tool for studying ground- and excited-state reaction mechanisms and associated energetics in heterogeneous catalysis. Several factors are important to obtaining reliable ECW energies, critically the construction of consistent active spaces (ASs) along reaction pathways when using a multireference correlated wavefunction (CW) method that relies on a subset of orbital spaces in the configuration interaction expansion to account for static electron correlation, e.g., complete AS self-consistent field theory, in addition to the adequate partitioning of the system into a cluster and environment, as well as the choice of a suitable basis set and number of states included in excited-state simulations. Here, in this work, we conducted a series of systematic studies to develop best-practice guidelines for ground- and excited-state ECW theory simulations, utilizing the decomposition of NH 3 on Pd(111) as an example. We determine that ECW theory results are relatively insensitive to cluster size, the aug-cc-pVDZ basis set provides an adequate compromise between computational complexity and accuracy, and that a fixed-clean-surface approximation holds well for the derivation of the embedding potential. Additionally, we demonstrate that a merging approach, which involves generating ASs from the molecular fragments at each configuration, is preferable to a creeping approach, which utilizes ASs from adjacent structures as an initial guess, for the generation of consistent potential energy curves involving open-d-shell metal surfaces, and, finally, we show that it is essential to include bands of excited states in their entirety when simulating excited-state reaction pathways.
Spatial Mapping of Valence Excited-State Landscapes Using Time-Resolved Shake-Down Spectroscopy
Time-resolved X-ray photoelectron spectroscopy (XPS) is used to track the photodissociation dynamics of 2-iodothiophene following 262 nm excitation. The transient XPS features include both direct ionization of the initially populated excited states and pronounced satellite peaks arising from shake-down processes. While the direct ionization signals exhibit only minimal energy shifts during C−I bond cleavage, the shake-down transitions undergo a substantial, 5 eV, shift over the reaction coordinate. By correlating these shifts with simulated C−I bond lengths, a direct structural mapping is established that reveals the exceptional sensitivity of shake-down channels to molecular geometry. These results demonstrate that shake-down transitions provide a new and powerful probe of ultrafast structural dynamics.
Mechanism and Reaction Energy Landscape for Apiose Cross-Linking by Boric Acid in Rhamnogalacturonan II
Rhamnogalacturonan II (RG-II)—the most complex polysaccharide known in nature—exists as a borate cross-linked dimer in the plant primary cell wall. Boric acid facilitates the formation of this cross-link on the apiosyl residues of RG-II’s side chain A. Here, we detail the reaction mechanism for the cross-linking process with ab initio calculations coupled with transition state theory. We determine the formation of the first ester linkage to be the rate-limiting step of the mechanism. Our findings demonstrate that the regio- and stereospecific nature of subsequent steps in the reaction itinerary presents four distinct energetically plausible reaction pathways. This has significant implications for the overall structure of the cross-linked RG-II dimer assembly. Finally, our transition state and reaction path analyses reveal key geometric insights that corroborate previous experimental hypotheses on borate ester formation reactions.
Inferring Pathways of Oxidative Folding from Prefolding Free Energy Landscapes of Disulfide-Rich Toxins
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Adsorbate Free Energies from DFT-Derived Translational Energy Landscapes
Adsorption free energies are fundamental to surface chemistry and catalysis. Standard models combine some assumed analytical form of the translational potential energy surface, often parameterized against density functional theory (DFT) calculations, with an analytical expression for the resultant translational densities of states (DOS), free energy, and entropy. Here we compare the performance of such models against numerical evaluations of the DOS and thermodynamic functions derived from solutions to the translational Schrodinger equation. We compare results for a translational potential energy surface (PES) derived from nudged eleastic band calculations with those obtained from adsorbate rastering across a series of monatomic (O, S, C, N, H) and polyatomic (NH x ) adsorbates on (100) Pt and Au facets. We find that analytical models as commonly parameterized have mixed performance for describing the translational PES and that the consequences for computed free energies are modest but potentially significant in microkinetic models. Here, numerical solutions are possible for modest to no additional computational cost over analytical models and thus should be considered when reliable free energy estimates are needed or translational potential energy surfaces are available.
Neural Network Sampling of the Free Energy Landscape for Nitrogen Dissociation on Ruthenium
In heterogeneous catalysis, free energy profiles of reactions govern the mechanisms, rates, and equilibria. Energetics are conventionally computed using the harmonic approximation (HA), which requires determination of critical states a priori. Here, we use neural networks to efficiently sample and directly calculate the free energy surface (FES) of a prototypical heterogeneous catalysis reaction—the dissociation of molecular nitrogen on ruthenium—at density-functional-theory-level accuracy. We find that the vibrational entropy of surface atoms, often neglected in HA for transition metal catalysts, contributes significantly to the reaction barrier. The minimum free energy path for dissociation reveals an “on-top” adsorbed molecular state prior to the transition state. While a previously reported flat-lying molecular metastable state can be identified in the potential energy surface, it is absent in the FES at relevant reaction temperatures. Finally, these findings demonstrate the importance of identifying critical points self-consistently on the FES for reactions that involve considerable entropic effects.
Quantifying Polypeptoid Conformational Landscapes through Integrated Experiment and Simulation
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Measuring and Tuning the Potential Landscape of Electrostatically Defined Quantum Dots in Graphene
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Free-Energy Landscapes and Surface Dynamics in Methane Activation on Ni(511) via Machine Learning and Enhanced Sampling
Methane activation on stepped Ni(511) surfaces involves the rearrangement of surface atoms as the chemical reaction proceeds. This process is particularly sensitive to temperature. Using machine-learned interatomic potentials (MLIPs) coupled with enhanced sampling techniques, we investigate the activation of methane under realistic operando conditions. Our analysis reveals that methane dissociation occurs predominantly at step-edge nickel atoms. As CH x (where x = 3 or 4) species bind to additional surface nickel atoms, their reduced mobility leads to entropic penalties that suppress certain configurations and transition states. This is reflected in the underlying free energy surfaces, where configurations such as methyl binding to hollow sites and activation routes involving two nickel atoms become unfavorable as temperature increases. At elevated temperatures, methane activation extends from step-edge sites to terrace regions because of reduced free-energy barriers and enhanced surface dynamics. By decomposing the free-energy into enthalpic and entropic contributions, we uncover temperature-dependent shifts in the preferences of methane for the relevant active sites and arrive at a detailed molecular picture of methane activation.
Expanding the Landscape of Dual Action Antifolate Antibacterials through 2,4-Diamino-1,6-dihydro-1,3,5-triazines
Antibiotics that operate via multiple mechanisms of action are a promising strategy to combat growing resistance. Previous studies have shown that dual action antifolates formed from a pyrroloquinazolinediamine core can inhibit the growth of bacterial pathogens without developing resistance. Here, in this work, we expand the scope of dual action antifolates by repurposing the 2,4-diamino-1,6-dihydro-1,3,5-triazine (DADHT) cycloguanil scaffold to a variety of derivatives designed to inhibit dihydrofolate reductase (DHFR) and disrupt bacterial membranes. Dual mechanism DADHTs have activity against a variety of target pathogens, including Mycobacterium tuberculosis, Mycobacterium abscessus, and Pseudomonas aeruginosa, among other ESKAPEE organisms. Through X-ray crystallography, we confirmed engagement of the Escherichia coli DHFR target and found that some DADHTs stabilize a previously unobserved conformation of the enzyme but, broadly, bind in the occluded conformation. Using in vitro inhibition of purified E. coli and Staphylococcus aureus DHFR and disruption of E. coli membranes, we determined that alkyl substitution of dihydrotriazine at the 6-position best optimizes the DADHT's two mechanisms of action. By employing both mechanisms, the DADHT spectrum of activity was extended beyond the scope of traditional antifolates. Finally, we are optimistic that the dual mechanism approach, particularly through the action of antifolates, offers a unique means of combating hard-to-treat bacterial infections.
Energy Landscapes of Supramolecular Peptide?Drug Conjugates Directed by Linker Selection and Drug To
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The Road Ahead for Metal–Organic Frameworks: Current Landscape, Challenges and Future Prospects
This perspective highlights the transformative potential of Metal–Organic Frameworks (MOFs) in environmental and healthcare sectors. It discusses work that has advanced beyond technology readiness levels of >4 including applications in capture, storage, and conversion of gases to value added products. This work showcases efforts in the most salient applications of MOFs which have been performed at a great cadence, enabled by the federal government, large companies, and startups to commercialize these technologies despite facing significant challenges. This article also forecasts the role of nanoscale MOFs in healthcare, including strides toward personalized medicine, advocating for their use in custom-tailored drug delivery systems. Lastly, we underscore the potential acceleration in MOF research and development through the integration of machine learning and AI, positioning MOFs as versatile tools poised to address global sustainability and health challenges.
Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows
Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a “user guide” for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure–property relationships in soft materials.
Modulation of the Electronic and Vibrational Landscape in Lead Organic Chalcogenides
Metal organic chalcogenides (MOCs) are an emerging class of two-dimensional (2D) materials featuring tunable band gaps and strong light-matter interactions with great potential for optical and polaritonic applications. Lead organic chalcogenides (LOCs) stand out among MOCs for sustaining long-lived coherent optical phonons despite their distorted lattices. However, the strong electron–phonon coupling also leads to rapid charge carrier self-trapping, hindering carrier transport. Herein, we demonstrate that charge carrier self-trapping is suppressed in Se- and Te-based LOCs. Specifically, the expansive Se and Te orbitals give rise to more dispersive conduction bands and reduced electron effective masses, thereby mitigating carrier self-trapping. As a result of reduced carrier self-trapping, the Se- and Te-based LOCs exhibit enhanced band-to-band photoluminescence and improved charge transport performance. Furthermore, our results provide a synthetic route to Se- and Te-based LOC single crystals and demonstrate the potential of orbital engineering to tune their electronic and phononic properties.