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1. NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces

   https://doi.org/10.1039/D2DD00008C  

   Haghighatlari, Mojtaba; Li, Jie; Guan, Xingyi; Zhang, Oufan; Das, Akshaya; Stein, Christopher J.; Heidar-Zadeh, Farnaz; Liu, Meili; Head-Gordon, Martin; Bertels, Luke; et al (June 2022, Digital Discovery)

   We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of directional information from trainable force vectors, and physics-infused operators that are inspired by Newtonian physics, the entire model remains rotationally equivariant, and many-body interactions are inferred by more interpretable physical features. We test NewtonNet on the prediction of several reactive and non-reactive high quality ab initio data sets including single small molecules, a large set of chemically diverse molecules, and methane and hydrogen combustion reactions, achieving state-of-the-art test performance on energies and forces with far greater data and computational efficiency than other deep learning models. 

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2. Ignition of Lean Hydrogen–Oxygen Mixtures in a Platinum Microtube

   Cicanese, J; Kumar, K (March 2026, 2026 Spring Technical Meeting of the Western States Section of the Combustion Institute)

   Hydrogen has gained significant interest as a versatile fuel for various applications from power generation to micropropulsion . To enable clean, safe, and efficient utilization, it is critical to understand its combustion characteristics. Previous works include a study of the stability limits of hydrogen-air combustion in catalytic microchannels by Qin et al. [6]. The nitrogen oxide produced by catalytic hydrogen combustion was investigated by Fumey [7]. This study investigates the ignition behavior of lean hydrogen-oxygen mixtures within a platinum microtube across a range of stoichiometries and flow rates. Only a small amount of experimental data is available on the conditions necessary to trigger combustion in such systems. The knowledge of the external energy input required to initiate the reaction is vital in designing realistic catalytic combustion-based systems for such fuels. Furthermore, this energy requirement will likely vary with the operating conditions and composition of the mixture. Therefore, the minimum ignition energies must be systematically mapped. Finally, the behavior of the ignition process itself is a crucial factor that also requires careful consideration 

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3. Efficient exploration of reaction pathways using reaction databases and active learning

   https://doi.org/10.1063/5.0235715  

   Kuryla, Domantas; Csányi, Gábor; van_Duin, Adri_C T; Michaelides, Angelos (March 2025, The Journal of Chemical Physics)

   Lian, T (Ed.)

   The fast and accurate simulation of chemical reactions is a major goal of computational chemistry. Recently, the pursuit of this goal has been aided by machine learning interatomic potentials (MLIPs), which provide energies and forces at quantum mechanical accuracy but at a fraction of the cost of the reference quantum mechanical calculations. Assembling the training set of relevant configurations is key to building the MLIP. Here, we demonstrate two approaches to training reactive MLIPs based on reaction pathway information. One approach exploits reaction datasets containing reactant, product, and transition state structures. Using an SN2 reaction dataset, we accurately locate reaction pathways and transition state geometries of up to 170 unseen reactions. In another approach, which does not depend on data availability, we present an efficient active learning procedure that yields an accurate MLIP and converged minimum energy path given only the reaction end point structures, avoiding quantum mechanics driven reaction pathway search at any stage of training set construction. We demonstrate this procedure on an SN2 reaction in the gas phase and with a small number of solvating water molecules, predicting reaction barriers within 20 meV of the reference quantum chemistry method. We then apply the active learning procedure on a more complex reaction involving a nucleophilic aromatic substitution and proton transfer, comparing the results against the reactive ReaxFF force field. Our active learning procedure, in addition to rapidly finding reaction paths for individual reactions, provides an approach to building large reaction path databases for training transferable reactive machine learning potentials. 

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4. Pressure-dependent kinetics of peroxy radicals formed in isobutanol combustion

   https://doi.org/10.1039/d0cp02872j  

   Goldman, Mark Jacob; Yee, Nathan W.; Kroll, Jesse H.; Green, William H. (September 2020, Physical Chemistry Chemical Physics)

   null (Ed.)

   Bio-derived isobutanol has been approved as a gasoline additive in the US, but our understanding of its combustion chemistry still has significant uncertainties. Detailed quantum calculations could improve model accuracy leading to better estimation of isobutanol's combustion properties and its environmental impacts. This work examines 47 molecules and 38 reactions involved in the first oxygen addition to isobutanol's three alkyl radicals located α, β, and γ to the hydroxide. Quantum calculations are mostly done at CCSD(T)-F12/cc-pVTZ-F12//B3LYP/CBSB7, with 1-D hindered rotor corrections obtained at B3LYP/6-31G(d). The resulting potential energy surfaces are the most comprehensive isobutanol peroxy networks published to date. Canonical transition state theory and a 1-D microcanonical master equation are used to derive high-pressure-limit and pressure-dependent rate coefficients, respectively. At all conditions studied, the recombination of γ-isobutanol radical with O 2 forms HO 2 + isobutanal. The recombination of β-isobutanol radical with O 2 forms a stabilized hydroperoxy alkyl radical below 400 K, water + an alkoxy radical at higher temperatures, and HO 2 + an alkene above 1200 K. The recombination of β-isobutanol radical with O 2 results in a mixture of products between 700–1100 K, forming acetone + formaldehyde + OH at lower temperatures and forming HO 2 + alkenes at higher temperatures. The barrier heights, high-pressure-limit rates, and pressure-dependent kinetics generally agree with the results from previous quantum chemistry calculations. Six reaction rates in this work deviate by over three orders of magnitude from kinetics in detailed models of isobutanol combustion, suggesting the rates calculated here can help improve modeling of isobutanol combustion and its environmental fate. 

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5. Machine learning accelerated photodynamics simulations

   https://doi.org/10.1063/5.0159247  

   Li, Jingbai; Lopez, Steven A (September 2023, Chemical Physics Reviews)

   Machine learning (ML) continues to revolutionize computational chemistry for accelerating predictions and simulations by training on experimental or accurate but expensive quantum mechanical (QM) calculations. Photodynamics simulations require hundreds of trajectories coupled with multiconfigurational QM calculations of excited-state potential energies surfaces that contribute to the prohibitive computational cost at long timescales and complex organic molecules. ML accelerates photodynamics simulations by combining nonadiabatic photodynamics simulations with an ML model trained with high-fidelity QM calculations of energies, forces, and non-adiabatic couplings. This approach has provided time-dependent molecular structural information for understanding photochemical reaction mechanisms of organic reactions in vacuum and complex environments (i.e., explicit solvation). This review focuses on the fundamentals of QM calculations and ML techniques. We, then, discuss the strategies to balance adequate training data and the computational cost of generating these training data. Finally, we demonstrate the power of applying these ML-photodynamics simulations to understand the origin of reactivities and selectivities of organic photochemical reactions, such as cis–trans isomerization, [2 + 2]-cycloaddition, 4π-electrostatic ring-closing, and hydrogen roaming mechanism. 

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