Chemical Reaction Networks Explain Gas Evolution Mechanisms in Mg-Ion Batteries

Toward waterproof magnesium metal anodes by uncovering water-induced passivation and drawing water-tolerant interphases

Magnesium (Mg) metal is a promising anode candidate for high-energy and cost-effective multivalent metal batteries, but suffers from severe surface passivation in conventional electrolytes, especially aqueous solutions. Here, we uncover that MgH2, in addition to the well-known MgO and Mg(OH)2, can be formed during the passivation of Mg by water. The formation mechanism and spatial distribution of MgH2, and its detrimental effect on interfacial dynamics and stability of Mg anode are revealed by comprehensive experimental and theoretical investigations. Furthermore, a graphite-based hydrophobic and Mg2+-permeable water-tolerant interphase is drawn using a pencil on the surface of Mg anodes, allowing them to cycle stably in symmetric (> 900 h) and full cells (> 500 cycles) even after contact with water. The mechanistic understanding of MgH2-involved Mg passivation and the design of pencil-drawn waterproof Mg anodes may inspire the further development of Mg metal batteries with high water resistance.

Analytical ab initio hessian from a deep learning potential for transition state optimization

Identifying transition states-saddle points on the potential energy surface connecting reactant and product minima-is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully differentiable equivariant neural network potential, NewtonNet, on thousands of organic reactions and derive the analytical Hessians. By reducing the computational cost by several orders of magnitude relative to the density functional theory (DFT) ab initio source, we can afford to use the learned Hessians at every step for the saddle point optimizations. We show that the full machine learned (ML) Hessian robustly finds the transition states of 240 unseen organic reactions, even when the quality of the initial guess structures are degraded, while reducing the number of optimization steps to convergence by 2-3× compared to the quasi-Newton DFT and ML methods. All data generation, NewtonNet model, and ML transition state finding methods are available in an automated workflow.

Quantum chemical package Jaguar: A survey of recent developments and unique features

This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach. A number of multistep workflows dependent on Jaguar are covered: prediction of protonation equilibria in aqueous solutions (particularly calculations of tautomeric stability and pKa), reactivity predictions based on automated transition state search, assembly of Boltzmann-averaged spectra such as vibrational and electronic circular dichroism, as well as nuclear magnetic resonance. Discussed also are quantum chemical calculations that are oriented toward materials science applications, in particular, prediction of properties of optoelectronic materials and organic semiconductors, and molecular catalyst design. The topic of treatment of conformations inevitably comes up in real world research projects and is considered as part of all the workflows mentioned above. In addition, we examine the role of machine learning methods in quantum chemical calculations performed by Jaguar, from auxiliary functions that return the approximate calculation runtime in a user interface, to prediction of actual molecular properties. The current work is second in a series of reviews of Jaguar, the first having been published more than ten years ago. Thus, this paper serves as a rare milestone on the path that is being traversed by Jaguar's development in more than thirty years of its existence.

CoeffNet: predicting activation barriers through a chemically-interpretable, equivariant and physically constrained graph neural network

Activation barriers of elementary reactions are essential to predict molecular reaction mechanisms and kinetics. However, computing these energy barriers by identifying transition states with electronic structure methods (e.g., density functional theory) can be time-consuming and computationally expensive. In this work, we introduce CoeffNet, an equivariant graph neural network that predicts activation barriers using coefficients of any frontier molecular orbital (such as the highest occupied molecular orbital) of reactant and product complexes as graph node features. We show that using coefficients as features offer several advantages, such as chemical interpretability and physical constraints on the network's behaviour and numerical range. Model outputs are either activation barriers or coefficients of the chosen molecular orbital of the transition state; the latter quantity allows us to interpret the results of the neural network through chemical intuition. We test CoeffNet on a dataset of SN2 reactions as a proof-of-concept and show that the activation barriers are predicted with a mean absolute error of less than 0.025 eV. The highest occupied molecular orbital of the transition state is visualized and the distribution of the orbital densities of the transition states is described for a few prototype SN2 reactions.

Large Computational Survey of Intrinsic Reactivity of Aromatic Carbon Atoms with Respect to a Model Aldehyde Oxidase

Aldehyde oxidase (AOX) and other related molybdenum-containing enzymes are known to oxidize the C-H bonds of aromatic rings. This process contributes to the metabolism of pharmaceutical compounds and, therefore, is of vital importance to drug pharmacokinetics. The present work describes an automated computational workflow and its use for the prediction of intrinsic reactivity of small aromatic molecules toward a minimal model of the active site of AOX. The workflow is based on quantum chemical transition state searches for the underlying single-step oxidation reaction, where the automated protocol includes identification of unique aromatic C-H bonds, creation of three-dimensional reactant and product complex geometries via a templating approach, search for a transition state, and validation of reaction end points. Conformational search on the reactants, products, and the transition states is performed. The automated procedure has been validated on previously reported transition state barriers and was used to evaluate the intrinsic reactivity of nearly three hundred heterocycles commonly found in approved drug molecules. The intrinsic reactivity of more than 1000 individual aromatic carbon sites is reported. Stereochemical and conformational aspects of the oxidation reaction, which have not been discussed in previous studies, are shown to play important roles in accurate modeling of the oxidation reaction. Observations on structural trends that determine the reactivity are provided and rationalized.