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 …
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 …
Microcystins risk assessment in lakes from space: Implications for SDG 6.1 evaluation
Cyanobacterial blooms release a large number of algal toxins (eg, Microcystins, MCs) and
seriously threaten the safety of drinking water sources what the SDG 6.1 pursues (to provide …
seriously threaten the safety of drinking water sources what the SDG 6.1 pursues (to provide …
Integrating Molecular Simulations with Machine Learning Guides in the Design and Synthesis of [BMIM][BF4]/MOF Composites for CO2/N2 Separation
Considering the existence of a large number and variety of metal–organic frameworks
(MOFs) and ionic liquids (ILs), assessing the gas separation potential of all possible IL/MOF …
(MOFs) and ionic liquids (ILs), assessing the gas separation potential of all possible IL/MOF …
Generalized uncertainty in surrogate models for concrete strength prediction
MA Hariri-Ardebili, G Mahdavi - Engineering Applications of Artificial …, 2023 - Elsevier
Applied soft computing has been widely used to predict material properties, optimal mixture,
and failure modes. This is challenging, especially for the highly nonlinear behavior of brittle …
and failure modes. This is challenging, especially for the highly nonlinear behavior of brittle …
Above-ground biomass prediction for croplands at a sub-meter resolution using uav–lidar and machine learning methods
Current endeavors to enhance the accuracy of in situ above-ground biomass (AGB)
prediction for croplands rely on close-range monitoring surveys that use unstaffed aerial …
prediction for croplands rely on close-range monitoring surveys that use unstaffed aerial …
Machine Learning Approach to Simulate Soil CO2 Fluxes under Cropping Systems
With the growing number of datasets to describe greenhouse gas (GHG) emissions, there is
an opportunity to develop novel predictive models that require neither the expense nor time …
an opportunity to develop novel predictive models that require neither the expense nor time …
[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 …