Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach

Menon, Aditya Krishna; Gupta, Chetali; Perkins, Kedar M.; DeCost, Brian; Budwal, Nikita; Rios, Renee T.; Zhang, Kun; Póczos, Barnabás; Washburn, Newell R.

doi:10.1039/c7me00027h

Cited by 28 publications

(30 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4 Recently, machine learning has been shown to accelerate the discovery of new materials for dielectric polymers, 5 OLED displays, 6 and polymeric dispersants. 7 In the realm of molecules, ML has been applied successfully to the prediction of atomization energies, 8 bond energies, 9 dielectric breakdown strength in polymers, 10 critical point properties of molecular liquids, 11 and exciton dynamics in photosynthetic complexes. 12 In the materials science realm, ML has recently yielded predictions for dielectric polymers, 5,10 superconducting materials, 13 nickel-based superalloys, 14 elpasolite crystals, 15 perovskites, 16 nanostructures, 17 Heusler alloys, 18 and the thermodynamic stabilities of half-Heusler compounds.…”

Section: Introductionmentioning

confidence: 99%

Applying machine learning techniques to predict the properties of energetic materials

Elton

Boukouvalas

Butrico

et al. 2018

Sci Rep

207

169

View full text Add to dashboard Cite

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes. We present a comprehensive comparison of machine learning models and several molecular featurization methods - sum over bonds, custom descriptors, Coulomb matrices, Bag of Bonds, and fingerprints. The best featurization was sum over bonds (bond counting), and the best model was kernel ridge regression. Despite having a small data set, we obtain acceptable errors and Pearson correlations for the prediction of detonation pressure, detonation velocity, explosive energy, heat of formation, density, and other properties out of sample. By including another dataset with ≈300 additional molecules in our training we show how the error can be pushed lower, although the convergence with number of molecules is slow. Our work paves the way for future applications of machine learning in this domain, including automated lead generation and interpreting machine learning models to obtain novel chemical insights.

show abstract

Section: Introductionmentioning

confidence: 99%

Applying machine learning techniques to predict the properties of energetic materials

Elton

Boukouvalas

Butrico

et al. 2018

Sci Rep

207

169

View full text Add to dashboard Cite

show abstract

“…In these scenarios, by employing an SGR model and drawing analogies to similar colloidal systems, numerical tools can present a picture of the molecular interactions and their effects on bulk biofilm viscoelasticity. Machine learning tools applied to materials science (134–136) are set to accelerate discoveries in this field and open up the possibility of designing artificial biofilms in conjunction with environmental functionalities (56, 137). A confluence of ideas and techniques from all three different disciplines is crucial to answering fundamental questions about biofilm structure-function relationships, and for the development of biofilm-inspired synthetic biomaterials.…”

Section: Discussionmentioning

confidence: 99%

Regulating, Measuring, and Modeling the Viscoelasticity of Bacterial Biofilms

et al. 2019

View full text Add to dashboard Cite

Biofilms occur in a broad range of environments under heterogeneous physicochemical conditions, such as in bioremediation plants, on surfaces of biomedical implants, and in the lungs of cystic fibrosis patients. In these scenarios, biofilms are subjected to shear forces, but the mechanical integrity of these aggregates often prevents their disruption or dispersal. Biofilms' physical robustness is the result of the multiple biopolymers secreted by constituent microbial cells which are also responsible for numerous biological functions. A better understanding of the role of these biopolymers and their response to dynamic forces is therefore crucial for understanding the interplay between biofilm structure and function. In this paper, we review experimental techniques in rheology, which help quantify the viscoelasticity of biofilms, and modeling approaches from soft matter physics that can assist our understanding of the rheological properties. We describe how these methods could be combined with synthetic biology approaches to control and investigate the effects of secreted polymers on the physical properties of biofilms. We argue that without an integrated approach of the three disciplines, the links between genetics, composition, and interaction of matrix biopolymers and the viscoelastic properties of biofilms will be much harder to uncover.

show abstract

“…In the Bayesian optimization, the training dataset was fitted to a nonparametric Gaussian Process Regression (GPR) model as a function of input parameters [ 25 ]. The GPR model is a probabilistic model that cannot be expressed as any specific functions.…”

Section: Methodsmentioning

confidence: 99%

“…Microwaveable food geometry design can be considered as a parametric optimization problem, where the objective is to properly design the food parameters, such as shape and size, for an improved heating performance. In general parametric optimization, machine-learning has shown its capability and advantages as an efficient method to ‘self-train’ and ‘speculate’ based on limited (training) data [ 23 , 24 , 25 ]. Machine-learning models can be developed to reveal the relationship between data inputs and outputs of a system [ 26 ] and identify the proper inputs that could generate the desired outputs [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Yang

Wang

Chen

2021

Foods

View full text Add to dashboard Cite

Mechanistic-modeling has been a useful tool to help food scientists in understanding complicated microwave-food interactions, but it cannot be directly used by the food developers for food design due to its resource-intensive characteristic. This study developed and validated an integrated approach that coupled mechanistic-modeling and machine-learning to achieve efficient food product design (thickness optimization) with better heating uniformity. The mechanistic-modeling that incorporated electromagnetics and heat transfer was previously developed and validated extensively and was used directly in this study. A Bayesian optimization machine-learning algorithm was developed and integrated with the mechanistic-modeling. The integrated approach was validated by comparing the optimization performance with a parametric sweep approach, which is solely based on mechanistic-modeling. The results showed that the integrated approach had the capability and robustness to optimize the thickness of different-shape products using different initial training datasets with higher efficiency (45.9% to 62.1% improvement) than the parametric sweep approach. Three rectangular-shape trays with one optimized thickness (1.56 cm) and two non-optimized thicknesses (1.20 and 2.00 cm) were 3-D printed and used in microwave heating experiments, which confirmed the feasibility of the integrated approach in thickness optimization. The integrated approach can be further developed and extended as a platform to efficiently design complicated microwavable foods with multiple-parameter optimization.

show abstract

Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach

Abstract: A machine learning approach to understanding and optimizing complex physical systems is presented in the context of polymeric dispersants.

Cited by 28 publications

References 43 publications

Applying machine learning techniques to predict the properties of energetic materials

Applying machine learning techniques to predict the properties of energetic materials

Regulating, Measuring, and Modeling the Viscoelasticity of Bacterial Biofilms

An Integrated Approach of Mechanistic-Modeling and Machine-Learning for Thickness Optimization of Frozen Microwaveable Foods

Contact Info

Product

Resources

About