SignificanceTo harness sunlight for growth, plants and algae rapidly convey absorbed excitation energy from antennae pigments to a reaction center, where the excitations convert to chemical energy. In deep water environments, cryptophyte algae survive by using the molecular motion of phycobiliprotein antennae to funnel excitations to low-energy pigments. The mechanism of down-conversion in phycobiliproteins remains controversial: Could specific vibrations resonant with pigment energy gaps support coherence between excited states and thereby enhance the rate of transport by transient delocalization? Here, we demonstrate that down-conversion in a specific phycobiliprotein, PC645, is both incoherent and enhanced by a broad range of high-frequency vibrations. We suggest that a similar incoherent mechanism is appropriate across phycobiliproteins.

Modeling reactivity with chemical reaction networks could yield fundamental mechanistic understanding that would expedite the development of processes and technologies for energy storage, medicine, catalysis, and more. Thus far, reaction...

The formation of passivation films by interfacial reactions, though critical for applications ranging from advanced alloys to electrochemical energy storage, is often poorly understood. In this work, we explore the formation of an exemplar passivation film, the solid−electrolyte interphase (SEI), which is responsible for stabilizing lithium-ion batteries. Using stochastic simulations based on quantum chemical calculations and data-driven chemical reaction networks, we directly model competition between SEI products at a mechanistic level for the first time. Our results recover the Peled-like separation of the SEI into inorganic and organic domains resulting from rich reactive competition without fitting parameters to experimental inputs. By conducting accelerated simulations at elevated temperature, we track SEI evolution, confirming the postulated reduction of lithium ethylene monocarbonate to dilithium ethylene monocarbonate and H 2 . These findings furnish fundamental insights into the dynamics of SEI formation and illustrate a path forward toward a predictive understanding of electrochemical passivation.

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Prediction of bond dissociation energies for charged molecules with a graph neural network enabled by global molecular features and reaction difference features between products and reactants.

Interfacial reactions are notoriously difficult to characterize, and robust prediction of the chemical evolution and associated functionality of the resulting surface film is one of the grand challenges of materials chemistry. The solid−electrolyte interphase (SEI), critical to Li-ion batteries (LIBs), exemplifies such a surface film, and despite decades of work, considerable controversy remains regarding the major components of the SEI as well as their formation mechanisms. Here we use a reaction network to investigate whether lithium ethylene monocarbonate (LEMC) or lithium ethylene dicarbonate (LEDC) is the major organic component of the LIB SEI. Our data-driven, automated methodology is based on a systematic generation of relevant species using a general fragmentation/recombination procedure which provides the basis for a vast thermodynamic reaction landscape, calculated with density functional theory. The shortest pathfinding algorithms are employed to explore the reaction landscape and obtain previously proposed formation mechanisms of LEMC as well as several new reaction pathways and intermediates. For example, we identify two novel LEMC formation mechanisms: one which involves LiH generation and another that involves breaking the (CH 2 )O−C(O)OLi bond in LEDC. Most importantly, we find that all identified paths, which are also kinetically favorable under the explored conditions, require water as a reactant. This condition severely limits the amount of LEMC that can form, as compared with LEDC, a conclusion that has direct impact on the SEI formation in Li-ion energy storage systems. Finally, the data-driven framework presented here is generally applicable to any electrochemical system and expected to improve our understanding of surface passivation.

Machine learning (ML) methods have great potential to transform chemical discovery by accelerating the exploration of chemical space and drawing scientific insights from data. However, modern chemical reaction ML models,...

b S Supporting Information ' INTRODUCTIONTemplated metal oxides have attracted sustained interest for many years, owing to their remarkably high degrees of compositional and structural diversity 1 and for technologically desirable physical properties. 2 However, increasing demand for new materials with enhanced properties has highlighted the lack of design and predictability in the syntheses of such compounds. The greatest limitation lies in our poor understanding of the mechanisms by which these compounds form. 3 While mechanisms have been postulated, 4À8 true design remains elusive. Significant progress has been made in the identification of reaction parameters that most strongly influence the formation of these materials.The primary influence over the structure of the inorganic component is reagent composition. Differences in reactant concentrations are known to directly affect the identity and availability of the primary building units from which the inorganic components are constructed. 9À14 Reactant concentrations are clearly affected by a range of experimental parameters, ranging from the dependence of metal speciation on both pH and temperature to differences associated with source materials and reaction times. The manner in which the inorganic reactants oligomerize and polymerize in the systems described above is thought to be controlled by charge density matching 4,5 between the organic cations and the anionic inorganic synthons. This secondary influence allows for crystallization only after the charge densities of the cationic and anionic components match. The importance of charge density matching has been demonstrated in a range of systems, including silicates, 15À17 oxovanadium phosphates, 18 gallium phosphates, 4,5 molybdates, 19 and vanadium tellurites. 20 Despite the utility of the charge density matching approach, differences in reactant concentrations and amine pK a alone do not wholly dictate the connectivities and structures of the resulting inorganic architectures. Charge density matching cannot differentiate between markedly different inorganic structures that have nearly identical charge densities. For example, we have reported the formation of both [Mo 3 O 10 ] n 2nÀ and [Mo 8 O 26 ] n 4nÀ chains from reactions in which the respective amines had similar pK a 's and were used in nearly identical concentrations. 19 We have also observed both [V 2 Te 2 O 10 ] n 2nÀ chains and [V 2 TeO 8 ] n 2nÀ layers in reactions containing either 1,4-diaminobutane or 1,3-diaminopropane, respectively. 20 In each system, the differences in charge densities of the inorganic components are small. As such, a series of tertiary influences has been proposed, including amine symmetry and hydrogen-bonding preferences. 19À21 This report contains an elucidation and observation of tertiary influences in the formation of new organically templated vanadium tellurites. The NaVO 3 /Na 2 TeO 3 /2,5-dimethylpiperazine and NaVO 3 /Na 2 TeO 3 /2-methylpiperazine systems were explored using composition space analysis, result...