Interatomic Machine Learning Potentials for Energy Materials

Heenen Group

In our group we develop machine-learned interatomic potentials for materials that are at the heart of energy conversion processes in battery technology and catalysis.

Battery systems and (electro)catalysis applications are key technologies for the efficient storage of renewable energy. Many of the major challenges still limiting these technologies arise at interfaces which are inherently complex and therefore often poorly understood. Atomistic simulations which could potentially rationalize these interfaces require a high accuracy and exhaustive sampling. Here, direct first-principles (e.g. Density Functional Theory, DFT) or empirical force field calculations are often severely limited due to prohibitive computational cost or insufficient accuracy, respectively. In contrast, machine learned interatomic potentials (MLIP) offer an alternative route forward as they show great promise of providing both sufficient accuracy and efficiency - thus filling an important application gap.

In our group, we develop strategies to train reliable MLIP which can capture complex chemical spaces and reactive chemistry. The target MLIP are intentionally designed to be as general as possible and go beyond single-purpose acceleration schemes where a MLIP is trained to replace e.g. DFT in a specific task. A major part of our methodological work includes active learning and uncertainty quantification as well as the incorporation of long-range interactions. The development of general-purpose workflows and concepts to "automatically" obtain potentials with limited user input is a long-term goal, which we persue by collaboratively co-developing the libatoms/workflow code. We develop and adapt our efforts alongside the investigation of technologically relevant systems that would be otherwise inaccessible (i.e. with DFT), such as Li-Sulfur battery chemistry, deactivation processes of electrocatalytic catalysts and graphene synthesis.

Selected Publications

Selected recent publications are listed below (a full list can be found under Publications):

Operando Characterization and Molecular Simulations Reveal the Growth Kinetics of Graphene on Liquid Copper During Chemical Vapor Deposition, V. Rein. et al., ACS Nano (2024)

wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows, E. Gelžinytė, et al., J. Chem. Phys. (2023)

Graphene at Liquid Copper Catalysts: Atomic-Scale Agreement of Experimental and First-Principles Adsorption Height, H. Gao, et al., Adv. Sci. (2022)

The mechanism for acetate formation in electrochemical CO(2) reduction on Cu: selectivity with potential, pH, and nanostructuring, H. H. Heenen, et al., Energy Environ. Sci. (2022)

On the Role of Long-Range Electrostatics in Machine-Learned Interatomic Potentials for Complex Battery Materials, C. G. Staacke, H. H. Heenen, C. Scheurer, G. Csányi, K. Reuter, and J. T. Margraf, ACS Appl. Energy Mater. (2021)

Catalytic polysulfide conversion and physiochemical confinement for lithium-sulfur batteries, Z. Sun*, S. Vijay*, H. H. Heenen*, et al., Adv. Energy Mater. (2020)

Solvation at metal/water interfaces: An ab initio molecular dynamics benchmark of common computational approaches, H. H. Heenen, J. A. Gauthier, H. H. Kristoffersen, T. Ludwig, and K. Chan, J. Chem. Phys. (2020)

Multi-ion Conduction in Li₃OCl Glass Electrolytes, H. H. Heenen, J. Voss, C. Scheurer, K. Reuter, and A. C. Luntz, J. Phys. Chem. Lett. (2019)

Implications of Occupational Disorder on Ion Mobility in Li₄Ti₅O₁₂ Battery Materials, H. H. Heenen, C. Scheurer, and K. Reuter, Nano Lett. (2017)