BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules

A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (−1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model''s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.

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.     

A chemically consistent graph architecture for massive reaction networks applied to solid-electrolyte interphase formation

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 networks have been limited in size by chemically inconsistent graph representations of multi-reactant reactions (e.g. A + B → C) that cannot enforce stoichiometric constraints, precluding the use of optimized shortest-path algorithms. Here, we report a chemically consistent graph architecture that overcomes these limitations using a novel multi-reactant representation and iterative cost-solving procedure. Our approach enables the identification of all low-cost pathways to desired products in massive reaction networks containing reactions of any stoichiometry, allowing for the investigation of vastly more complex systems than previously possible. Leveraging our architecture, we construct the first ever electrochemical reaction network from first-principles thermodynamic calculations to describe the formation of the Li-ion solid electrolyte interphase (SEI), which is critical for passivation of the negative electrode. Using this network comprised of nearly 6000 species and 4.5 million reactions, we interrogate the formation of a key SEI component, lithium ethylene dicarbonate. We automatically identify previously proposed mechanisms as well as multiple novel pathways containing counter-intuitive reactions that have not, to our knowledge, been reported in the literature. We envision that our framework and data-driven methodology will facilitate efforts to engineer the composition-related properties of the SEI – or of any complex chemical process – through selective control of reactivity.

A chemically consistent graph architecture enables autonomous identification of novel solid-electrolyte interphase formation pathways from a massive reaction network.     

Stereochemistry of [4 + 2] Cycloadditions of Perfluorinated Selenocarbonyls to 1, 3‐Dienes

In completely stereospecific [4+2] cycloadditions, the perfluorinated selenocarbonyls 1 and 2 react both with trans‐trans‐2, 4‐hexadiene and cis‐trans‐2, 4‐hexadiene to yield 3, 6‐dihydro‐cis‐3, 6‐dimethyl‐2H‐selenapyrans 3 , 4a and 4b . The observed stereoselectivity leads to the conclusion, that the [4+2] cycloaddition of perfluorinated selenocarbonyls follows a concerted pathway. An identical mixture of isomers was isolated when using the precursor for 2 , trimethylstannyl (pentafluoroethyl)selane, which reacts with both 1, 3‐dienes over several weeks to form a mixture of syn‐2‐fluoro‐3, 6‐dihydro‐cis‐3, 6‐dimethyl‐2‐trifluoromethyl‐2H‐selenapyran ( 4a ) and anti‐2‐fluoro‐3, 6‐dihydro‐cis‐3, 6‐dimethyl‐2‐trifluoromethyl‐2H‐selenapyran ( 4b ) in the same ratio as found for 2 , thus proving the intermediate formation of Se=C(F)CF₃ ( 2 ). Complex 2D NMR experiments were used to distinguish the isomers 4a and 4b and to assign the ¹H, ¹³C and ¹⁹F NMR data of the selenaheterocycles.     

PHOTOLYSIS OF PHOSPHODIESTER BONDS IN PLASMID DNA BY HIGH INTENSITY UV LASER IRRADIATION

Abstract— The cleavage of phosphodiester bonds in DNA exposed to high intensity UV laser pulses in aerated aqueous solution has been investigated using a krypton fluoride excimer laser (248 nm) and bacterial plasmid DNA. The dependence of strand breakage on fluence and intensity has been studied in detail and shows that the process is non-linear with respect to intensity. The relationship between the quantum yield for strand breakage and intensity shows that the strand breakage reaction involves two-photon excitation of DNA bases. The quantum yield rises with intensity from a lower value of 7 times 10^-5 until a maximum value of 4.5 times 10^-4 is attained at intensities of 10¹¹ W m^-2 and above. This value is approximately fifty-fold higher than the quantum yield for strand breakage induced by exposure to low density UV irradiation (254 nm, 12 W m^-2). DNA sequencing experiments have shown that strand breakage occurs by the specific cleavage of the phosphodiester bond which lies immediately 3' to guanine residues in the DNA, leaving some alkali-labile remnant attached to the terminal phosphate. A mechanism for DNA strand breakage which involves the generation of guanine radical cations is proposed.     

Cell adhesion promotion strategies for signal transduction enhancement in microelectrode array in vitro electrophysiology: An introductory overview and critical discussion

Microelectrode arrays (MEAs) find application both in vitro and in vivo to record and stimulate electrical activity in electrogenic cells such as neurons, cardiomyocytes, pancreatic beta cells or immortalized cell lines derived therefrom (e.g., PC12, HL-1). In MEA electrophysiology, the quality of the predominantly extracellularly recorded or elicited electrical signals strongly depends on the distance, strength and stability of the interfacial contact between the electrogenic cells and an electrode. Decorating the substrate or electrode with biochemical adhesion factors and physical guidance cues does not only determine the tightness of that junction, but it also modulates substrate biocompatibility, its biostability, cell differentiation as well as cell fate. If an interface is furthermore topologically, chemically or physically patterned or constrained, neural interconnectivity may be steered towards directional organization. In this introductory and selective overview, we briefly discuss adhesion events at the chemical and biological level, review the general role and mechanisms of cell adhesion in (neuro)biology, then explore how cells adhere to artificial substrates. This will lead to the discussion of popular strategies for enhancing and steering interfacial interactions at the bio-hardware boundary with particular focus on MEA substrates. It will include a critical treatment of open issues with respect to the origin and shape of extracellularly recorded signals and their modulation by cell-culture-inherent events.     

Aqueous Diels–Alder reactions for thermochemical storage and heat transfer fluids identified using density functional theory

Thermal storage and transfer fluids have important applications in industrial, transportation, and domestic settings. Current thermal fluids have relatively low specific heats, often significantly below that of water. However, by introducing a thermochemical reaction to a base fluid, it is possible to enhance the fluid's thermal properties. In this work, density functional theory (DFT) is used to screen Diels–Alder reactions for use in aqueous thermal fluids. From an initial set of 52 reactions, four are identified with moderate aqueous solubility and predicted turning temperature near the liquid region of water. These reactions are selectively modified through 60 total functional group substitutions to produce novel reactions with improved solubility and thermal properties. Among the reactions generated by functional group substitution, seven have promising predicted thermal properties, significantly improving specific heat (by as much as 30.5%) and energy storage density (by as much as 4.9%) compared to pure water.     

Lie Isomorphisms of Prime Rings Satisfying St 4

Let : R R be a Lie isomorphism of prime rings. Suppose that if R satisfies the standard polynomial identity St₄ of degree 4. We prove that has a decomposition into the sum of a homomorphism (or the negative of an anithomomorphism) plus an additive map into the center of R.