Data-driven design of polymer-based biomaterials: high-throughput simulation, experimentation, and machine learning
Polymers, with the capacity to tunably alter properties and response based on manipulation
of their chemical characteristics, are attractive components in biomaterials. Nevertheless …
of their chemical characteristics, are attractive components in biomaterials. Nevertheless …
Hybrid magnetorheological elastomers enable versatile soft actuators
Recent advances in magnetorheological elastomers (MREs) have posed the question on
whether the combination of both soft-and hard-magnetic particles may open new routes to …
whether the combination of both soft-and hard-magnetic particles may open new routes to …
Bayesian optimization of catalysts with in-context learning
MC Ramos, SS Michtavy, MD Porosoff… - ar** pareto fronts for efficient multi-objective materials discovery
With advancements in automation and high-throughput techniques, complex materials
discovery with multiple conflicting objectives can now be tackled in experimental labs. Given …
discovery with multiple conflicting objectives can now be tackled in experimental labs. Given …
[HTML][HTML] A machine learning approach to automate ductile damage parameter selection using finite element simulations
Ductile damage models require constitutive model parameter values that are difficult to
derive experimentally or analytically. The calibration procedure for ductile damage model …
derive experimentally or analytically. The calibration procedure for ductile damage model …
Uncertainty-aware mixed-variable machine learning for materials design
Data-driven design shows the promise of accelerating materials discovery but is challenging
due to the prohibitive cost of searching the vast design space of chemistry, structure, and …
due to the prohibitive cost of searching the vast design space of chemistry, structure, and …