Transferable Water Potentials Using Equivariant Neural Networks

Machine learning interatomic potentials (MLIPs) have emerged as a technique that promises quantum theory accuracy for reduced cost. It has been proposed [J. Chem. Phys. 2023, 158, 084111] that MLIPs trained on solely liquid water data cannot accurately transfer to the vapor–liquid equilibrium while recovering the many-body decomposition (MBD) analysis of gas-phase water clusters. This suggests that MLIPs do not directly learn the physically correct interactions of water molecules, limiting transferability. In this work, we show that MLIPs using equivariant architecture and trained on 3200 liquid water structures reproduces liquid-phase water properties (e.g., density within 0.003 g/cm3 between 230 and 365 K), vapor–liquid equilibrium properties up to 550 K, the MBD analysis of gas-phase water cluster up to six-body interactions, and the relative energy and the vibrational density of states of ice phases. We show that potentials developed using equivariant MLIPs allow transferability for arbitrary phases of water that remain stable in nanosecond long simulations

Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices

Recent developments in machine learning interatomic potentials (MLIPs) have empowered even nonexperts in machine learning to train MLIPs for accelerating materials simulations. However, reproducibility and independent evaluation of presented MLIP results is hindered by a lack of clear standards in current literature. In this Perspective, we aim to provide guidance on best practices for documenting MLIP use while walking the reader through the development and deployment of MLIPs including hardware and software requirements, generating training data, training models, validating predictions, and MLIP inference. We also suggest useful plotting practices and analyses to validate and boost confidence in the deployed models. Finally, we provide a step-by-step checklist for practitioners to use directly before publication to standardize the information to be reported. Overall, we hope that our work will encourage the reliable and reproducible use of these MLIPs, which will accelerate their ability to make a positive impact in various disciplines including materials science, chemistry, and biology, among others

Probe the Dynamic Adsorption and Phase Transition of Underpotential Deposition Processes at Electrode–Electrolyte Interfaces

Electrochemical scanning tunneling microscopy (EC-STM) and electrochemical quartz crystal microbalance (E-QCM) techniques in combination with DFT calculations have been applied to reveal the static phase and the phase transition of copper underpotential deposition (UPD) on a gold electrode surface. EC-STM demonstrated, for the first time, the direct visualization of the disintegration of (√3 × √3)R30° copper UPD adlayer with coadsorbed SO42– while changing sample potential (ES) toward the redox Pa2/Pc2 peaks, which are associated with the phase transition between the Cu UPD (√3 × √3)R30° phase II and disordered randomly adsorbed phase III. DFT calculations show that SO42– binds via three oxygens to the bridge sites of the copper with sulfate being located directly above the copper vacancy in the (√3 × √3)R30° adlayer, whereas the remaining oxygen of the sulfate points away from the surface. E-QCM measurement of the change of the electric charge due to Cu UPD Faradaic processes, the change of the interfacial mass due to the adsorption and desorption of Cu(II) and SO42–, and the formation and stripping of UPD copper on the gold surface provide complementary information that validates the EC-STM and DFT results. This work demonstrated the advantage of using complementary in situ experimental techniques (E-QCM and EC-STM) combined with simulations to obtain an accurate and complete picture of the dynamic interfacial adsorption and UPD processes at the electrode/electrolyte interface