AENET-LAMMPS and AENET-TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet-TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet-LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed-memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials

Understanding the Onset of Surface Degradation in LiNiO2Cathodes

Nickel-based layered oxides offer an attractive platform for the development of energy-dense cobalt-free cathodes for lithium-ion batteries but suffer from degradation via oxygen gas release during electrochemical cycling. While such degradation has previously been characterized phenomenologically with experiments, an atomic-scale understanding of the reactions that take place at the cathode surface has been lacking. Here, we develop a first-principles methodology for the prediction of the surface reconstructions of intercalation electrode particles as a function of the temperature and state of charge. We report the surface phase diagrams of the LiNiO2 (001) and (104) surfaces and identify surface structures that are likely visited during the first charge and discharge. Our calculations indicate that both surfaces experience oxygen loss during the first charge, resulting in irreversible changes to the surface structures. At the end of charge, the surface Ni atoms migrate into tetrahedral sites, from which they further migrate into Li vacancies during discharge, leading to Li/Ni mixed discharged surface phases. Further, the impact of the temperature and voltage range during cycling on the charge/discharge mechanism is discussed. The present study thus provides insight into the initial stages of cathode surface degradation and lays the foundation for the computational design of cathode materials that are stable against oxygen release

Machine learning prediction and experimental verification of Pt-modified nitride catalysts for ethanol reforming with reduced precious metal loading

Ethanol is the smallest molecule containing C–O, C–C, C–H, and O–H bonds present in biomass-derived oxygenates. The development of inexpensive and selective catalysts for ethanol reforming is important towards the renewable generation of hydrogen from biomass. Transition metal nitrides (TMN) are interesting catalyst support materials that can effectively reduce precious metal loading for the catalysis of ethanol and other oxygenates. Herein theoretical and experimental methods were used to probe platinum-modified molybdenum nitride (Pt/Mo2N) surfaces for ethanol reforming. Computations using density-functional theory and machine learning predicted monolayer Pt/Mo2N to be highly active and selective for ethanol reforming. Temperature-programmed desorption (TPD) experiments verified that ethanol primarily underwent decomposition on Mo2N, and the reaction pathway shifted to reforming on Pt/Mo2N surfaces. High-resolution electron energy loss spectroscopy (HREELS) results further indicated that while Mo2N decomposed the ethoxy intermediate by cleaving C–C, C–O, and C–H bonds, Pt-modification preserved the C–O bond, resulting in ethanol reforming

Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures

The prediction of temperature effects from first principles is computationally demanding and typically too approximate for the engineering of high-temperature processes. Here, we introduce a hybrid approach combining zero-Kelvin first-principles calculations with a Gaussian process regression model trained on temperature-dependent reaction free energies. We apply this physics-based machine-learning model to the prediction of metal oxide reduction temperatures in high-temperature smelting processes that are commonly used for the extraction of metals from their ores and from electronics waste and have a significant impact on the global energy economy and greenhouse gas emissions. The hybrid model predicts accurate reduction temperatures of unseen oxides, is computationally efficient, and surpasses in accuracy computationally much more demanding first-principles simulations that explicitly include temperature effects. The approach provides a general paradigm for capturing the temperature dependence of reaction free energies and derived thermodynamic properties when limited experimental reference data is available

AENET-LAMMPS and AENET-TINKER: Interfaces for accurate and efficient molecular dynamics simulations with machine learning potentials

Machine-learning potentials (MLPs) trained on data from quantum-mechanics based first-principles methods can approach the accuracy of the reference method at a fraction of the computational cost. To facilitate efficient MLP-based molecular dynamics and Monte Carlo simulations, an integration of the MLPs with sampling software is needed. Here, we develop two interfaces that link the atomic energy network (ænet) MLP package with the popular sampling packages TINKER and LAMMPS. The three packages, ænet, TINKER, and LAMMPS, are free and open-source software that enable, in combination, accurate simulations of large and complex systems with low computational cost that scales linearly with the number of atoms. Scaling tests show that the parallel efficiency of the ænet-TINKER interface is nearly optimal but is limited to shared-memory systems. The ænet-LAMMPS interface achieves excellent parallel efficiency on highly parallel distributed-memory systems and benefits from the highly optimized neighbor list implemented in LAMMPS. We demonstrate the utility of the two MLP interfaces for two relevant example applications: the investigation of diffusion phenomena in liquid water and the equilibration of nanostructured amorphous battery materials

Understanding the Onset of Surface Degradation in LiNiO2Cathodes

Nickel-based layered oxides offer an attractive platform for the development of energy-dense cobalt-free cathodes for lithium-ion batteries but suffer from degradation via oxygen gas release during electrochemical cycling. While such degradation has previously been characterized phenomenologically with experiments, an atomic-scale understanding of the reactions that take place at the cathode surface has been lacking. Here, we develop a first-principles methodology for the prediction of the surface reconstructions of intercalation electrode particles as a function of the temperature and state of charge. We report the surface phase diagrams of the LiNiO2 (001) and (104) surfaces and identify surface structures that are likely visited during the first charge and discharge. Our calculations indicate that both surfaces experience oxygen loss during the first charge, resulting in irreversible changes to the surface structures. At the end of charge, the surface Ni atoms migrate into tetrahedral sites, from which they further migrate into Li vacancies during discharge, leading to Li/Ni mixed discharged surface phases. Further, the impact of the temperature and voltage range during cycling on the charge/discharge mechanism is discussed. The present study thus provides insight into the initial stages of cathode surface degradation and lays the foundation for the computational design of cathode materials that are stable against oxygen release

Machine learning prediction and experimental verification of Pt-modified nitride catalysts for ethanol reforming with reduced precious metal loading

Ethanol is the smallest molecule containing C–O, C–C, C–H, and O–H bonds present in biomass-derived oxygenates. The development of inexpensive and selective catalysts for ethanol reforming is important towards the renewable generation of hydrogen from biomass. Transition metal nitrides (TMN) are interesting catalyst support materials that can effectively reduce precious metal loading for the catalysis of ethanol and other oxygenates. Herein theoretical and experimental methods were used to probe platinum-modified molybdenum nitride (Pt/Mo2N) surfaces for ethanol reforming. Computations using density-functional theory and machine learning predicted monolayer Pt/Mo2N to be highly active and selective for ethanol reforming. Temperature-programmed desorption (TPD) experiments verified that ethanol primarily underwent decomposition on Mo2N, and the reaction pathway shifted to reforming on Pt/Mo2N surfaces. High-resolution electron energy loss spectroscopy (HREELS) results further indicated that while Mo2N decomposed the ethoxy intermediate by cleaving C–C, C–O, and C–H bonds, Pt-modification preserved the C–O bond, resulting in ethanol reforming

Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures

The prediction of temperature effects from first principles is computationally demanding and typically too approximate for the engineering of high-temperature processes. Here, we introduce a hybrid approach combining zero-Kelvin first-principles calculations with a Gaussian process regression model trained on temperature-dependent reaction free energies. We apply this physics-based machine-learning model to the prediction of metal oxide reduction temperatures in high-temperature smelting processes that are commonly used for the extraction of metals from their ores and from electronics waste and have a significant impact on the global energy economy and greenhouse gas emissions. The hybrid model predicts accurate reduction temperatures of unseen oxides, is computationally efficient, and surpasses in accuracy computationally much more demanding first-principles simulations that explicitly include temperature effects. The approach provides a general paradigm for capturing the temperature dependence of reaction free energies and derived thermodynamic properties when limited experimental reference data is available