The mastery of details in the workflow of materials machine learning
Y Ma, P Xu, M Li, X Ji, W Zhao, W Lu - npj Computational Materials, 2024 - nature.com
As machine learning (ML) continues to advance in the field of materials science, the
variation in strategies for the same steps of the ML workflow becomes increasingly …
variation in strategies for the same steps of the ML workflow becomes increasingly …
Combining machine learning and molecular simulations to unlock gas separation potentials of MOF membranes and MOF/polymer MMMs
Due to the enormous increase in the number of metal-organic frameworks (MOFs),
combining molecular simulations with machine learning (ML) would be a very useful …
combining molecular simulations with machine learning (ML) would be a very useful …
A critical examination of robustness and generalizability of machine learning prediction of materials properties
Recent advances in machine learning (ML) have led to substantial performance
improvement in material database benchmarks, but an excellent benchmark score may not …
improvement in material database benchmarks, but an excellent benchmark score may not …
Bibliometric analysis of methods and tools for drought monitoring and prediction in Africa
The African continent has a long history of rainfall fluctuations of varying duration and
intensities. This has led to varying degrees of drought conditions, triggering research interest …
intensities. This has led to varying degrees of drought conditions, triggering research interest …
Microcystins risk assessment in lakes from space: Implications for SDG 6.1 evaluation
[HTML][HTML] Predicting spatial distribution of stable isotopes in precipitation by classical geostatistical-and machine learning methods
Stable isotopes of precipitation are important natural tracers in hydrology, ecology, and
forensics. The spatially explicit predictions of oxygen and hydrogen isotopes in precipitation …
forensics. The spatially explicit predictions of oxygen and hydrogen isotopes in precipitation …