Machine learning and data-driven methods in computational surface and interface science
Lukas Hörmann, Wojciech G. Stark, Reinhard J. Maurer
Abstract
Machine learning and data-driven methods have started to transform the study of surfaces and interfaces. Here, we review how data-driven methods and machine learning approaches complement simulation workflows and contribute towards tackling grand challenges in computational surface science from 2D materials to interface engineering and electrocatalysis. Challenges remain, including the scarcity of large datasets and the need for more electronic structure methods for interfaces.
Topics & Concepts
WorkflowComputer scienceInterface (matter)Complement (music)Data scienceSurface (topology)Artificial intelligenceScience and engineeringMachine learningHuman–computer interactionEngineeringEngineering ethicsChemistryDatabaseGeneBiochemistryMathematicsGeometryParallel computingBubbleMaximum bubble pressure methodPhenotypeComplementationMachine Learning in Materials ScienceElectronic and Structural Properties of OxidesFerroelectric and Negative Capacitance Devices