Engineering and Technologynpj Computational Materials
Performance of two complementary machine-learned potentials in modelling chemically complex systems
K. Gubaev, V. Zaverkin, et al.
This research investigates the performance of two advanced machine-learned potentials—the moment tensor potential and the Gaussian moment neural network—in modeling the Ta-V-Cr-W alloy family, showcasing their abilities to describe complex configurational and vibrational properties with high accuracy. The study was conducted by Konstantin Gubaev, Viktor Zaverkin, Prashanth Srinivasan, Andrew Ian Duff, Johannes Kästner, and Blazej Grabowski.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Economics
Modelling dataset bias in machine-learned theories of economic decision-making
T. Thomas, D. Straub, et al.
Computer Science
Machine learning dismantling and early-warning signals of disintegration in complex systems
M. Grassia, M. D. Domenico, et al.
Business
The mediating role of accounting information systems in small and medium enterprise strategies and organizational performance in Iraq
H. M. Kareem, A. H. Alsheikh, et al.
Engineering and Technology
Machine Learning Techniques for the Performance Enhancement of Multiple Classifiers in the Detection of Cardiovascular Disease from PPG Signals
S. W. Rabkin, A. Cataldo, et al.
