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1. A topological isomer of the Au ₂₅ (SR) ₁₈ ⁻ nanocluster

   https://doi.org/10.1039/D0CC03334K  

   Matus, María Francisca; Malola, Sami; Kinder Bonilla, Emily; Barngrover, Brian M.; Aikens, Christine M.; Häkkinen, Hannu (July 2020, Chemical Communications)

   null (Ed.)

   Energetically low-lying structural isomers of the much-studied thiolate-protected gold cluster Au 25 (SR) 18 − are discovered from extensive (80 ns) molecular dynamics (MD) simulations using the reactive molecular force field ReaxFF and confirmed by density functional theory (DFT). A particularly interesting isomer is found, which is topologically connected to the known crystal structure by a low-barrier collective rotation of the icosahedral Au 13 core. The isomerization takes place without breaking of any Au–S bonds. The predicted isomer is essentially iso-energetic with the known Au 25 (SR) 18 − structure, but has a distinctly different optical spectrum. It has a significantly larger collision cross-section as compared to that of the known structure, which suggests it could be detectable in gas phase ion-mobility mass spectrometry. 

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2. End-to-End Differentiable Reactive Molecular Dynamics Simulations Using JAX

   https://doi.org/10.1007/978-3-031-32041-5_11  

   Kaymak, M.C.; Schoenholz, S.S.; Cubuk, E.D.; O’Hearn, K.A.; Merz Jr., K.M.; Aktulga, H.M. (May 2023, Lecture notes in computer science)

   Bhatele, A.; Hammond, J.; Baboulin, M.; Kruse, C. (Ed.)

   The reactive force field (ReaxFF) interatomic potential is a powerful tool for simulating the behavior of molecules in a wide range of chemical and physical systems at the atomic level. Unlike traditional classical force fields, ReaxFF employs dynamic bonding and polarizability to enable the study of reactive systems. Over the past couple decades, highly optimized parallel implementations have been developed for ReaxFF to efficiently utilize modern hardware such as multi-core processors and graphics processing units (GPUs). However, the complexity of the ReaxFF potential poses challenges in terms of portability to new architectures (AMD and Intel GPUs, RISC-V processors, etc.), and limits the ability of computational scientists to tailor its functional form to their target systems. In this regard, the convergence of cyber-infrastructure for high performance computing (HPC) and machine learning (ML) presents new opportunities for customization, programmer productivity and performance portability. In this paper, we explore the benefits and limitations of JAX, a modern ML library in Python representing a prime example of the convergence of HPC and ML software, for implementing ReaxFF. We demonstrate that by leveraging auto-differentiation, just-in-time compilation, and vectorization capabilities of JAX, one can attain a portable, performant, and easy to maintain ReaxFF software. Beyond enabling MD simulations, end-to-end differentiability of trajectories produced by ReaxFF implemented with JAX makes it possible to perform related tasks such as force field parameter optimization and meta-analysis without requiring any significant software developments. We also discuss scalability limitations using the current version of JAX for ReaxFF simulations. 

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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. Reactive force fields for aqueous and interfacial magnesium carbonate formation

   https://doi.org/10.1039/D1CP02627E  

   Zare, Siavash; Qomi, Mohammad Javad (October 2021, Physical Chemistry Chemical Physics)

   We develop Mg/C/O/H ReaxFF parameter sets for two environments: an aqueous force field for magnesium ions in solution and an interfacial force field for minerals and mineral–water interfaces. Since magnesium is highly ionic, we choose to fix the magnesium charge and model its interaction with C/O/H through Coulomb, Lennard-Jones, and Buckingham potentials. We parameterize the forcefields against several crystal structures, including brucite, magnesite, magnesia, magnesium hydride, and magnesium carbide, as well as Mg 2+ water binding energies for the aqueous forcefield. Then, we test the forcefield for other magnesium-containing crystals, solvent separated and contact ion-pairs and single-molecule/multilayer water adsorption energies on mineral surfaces. We also apply the forcefield to the forsterite–water and brucite–water interface that contains a bicarbonate ion. We observe that a long-range proton transfer mechanism deprotonates the bicarbonate ion to carbonate at the interface. Free energy calculations show that carbonate can attach to the magnesium surface with an energy barrier of about 0.22 eV, consistent with the free energy required for aqueous Mg–CO 3 ion pairing. Also, the diffusion constant of the hydroxide ions in the water layers formed on the forsterite surface are shown to be anisotropic and heterogeneous. These findings can help explain the experimentally observed fast nucleation and growth of magnesite at low temperature at the mineral–water–CO 2 interface in water-poor conditions. 

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5. Recent Advances for Improving the Accuracy, Transferability and Efficiency of Reactive Force Fields

   https://doi.org/doi.org/10.1021/acs.jctc.1c00118  

   Leven, Itai; Hao, Hongxia; Tan, Songchen; Penrod, Katheryn A.; Akbarian, Dooman; Hossain, Md. Jamil; Evangelisti, Benjamin; Islam, Mahbub; Koski, Jason; Moore, Stan; et al (March 2021, Journal of chemical theory and computation)

   null (Ed.)

   Reactive force elds provide an aordable model for simulating chemical reactions at a fraction of the cost of quantum mechanical approaches. However classically accounting for chemical reactivity often comes at the expense of accuracy and transferability, while computational cost is still large relative to non-reactive force elds. In this Perspective we summarize recent eorts for improving the performance of reactive force elds in these three areas with a focus on the ReaxFF theoretical model. To improve accuracy we describe recent reformulations of charge equilibration schemes to overcome unphysical long-range charge transfer, new ReaxFF models that account for explicit electrons, and corrections for energy conservation issues of the ReaxFF model. To enhance transferability we also highlight new advances to include explicit treatment of electrons in the ReaxFF and hybrid non-reactive/reactive simulations that make it possible to model charge transfer, redox chemistry, and large systems such as reverse micelles within the framework of a reactive force eld. To address the computational cost we review recent work in extended Lagrangian schemes and matrix preconditioners for accelerating the charge equilibration method component of ReaxFF and improvements in its software performance in LAMMPs. 

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