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Related Experiment Video

Updated: Jul 1, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials.

Kyle Noordhoek¹, Christopher J Bartel¹

¹Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN, 55455, USA. cbartel@umn.edu.

|March 12, 2024

Summary

Computational surface science uses first-principles and machine learning methods to predict solid-state material surface properties. These advanced techniques enhance understanding of nanoscale surface behavior and phase stability for various applications.

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Published on: March 19, 2010

In Situ SIMS and IR Spectroscopy of Well-defined Surfaces Prepared by Soft Landing of Mass-selected Ions

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Published on: June 16, 2014

Area of Science:

Computational Materials Science
Surface Science
Nanotechnology

Background:

Solid-state material functionality is critically dependent on surface properties, especially at the nanoscale.
Surface structure and morphology are governed by synthesis and operating conditions, influencing thermodynamic and kinetic factors.
Computational surface science traditionally links thermochemical conditions to surface phase stability, vital for catalysis and thin film growth.

Purpose of the Study:

To review computational approaches for computing surface phase diagrams.
To introduce emerging data-driven methods, including machine learning, for surface studies.
To highlight the growing role of machine learning in modeling complex inorganic surfaces.

Main Methods:

Review of first-principles computational methods for surface phase diagram calculations.
Introduction to data-driven approaches and machine learning algorithms.
Focus on learned interatomic potentials for studying complex surfaces.

Main Results:

Traditional computational methods establish connections between thermochemical conditions and surface phase stability.
Emerging data-driven and machine learning techniques offer new avenues for surface analysis.
Machine learning, particularly learned interatomic potentials, shows promise for complex surface modeling.

Conclusions:

Computational surface science is evolving with the integration of machine learning.
Machine learning algorithms and large datasets are increasing the predictive power of computational methods.
These advancements are crucial for understanding and engineering nanoscale inorganic surfaces.