Machine-Learned Coarse-Grained Models

Bejagam, Karteek K.; Singh, Samrendra; An, Yaxin; Deshmukh, Sanket A.

doi:10.1021/acs.jpclett.8b01416

Cited by 59 publications

(66 citation statements)

References 37 publications

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“…For the first time, machine learning (ML) technology was implemented for almond measurements in this study using stacked ensemble model (SEM). A predictive model system can be designed in the form of a known data set by implementing Machine-learning model a (Bejagam et al, 2018). Mirzabe et al (2013) measured the dimensions of almond kernels and then compared their physical properties, such as bulk density, true density, porosity and coefficient of friction using various statistical regression models, such as linear and quadratic regression analysis and distribution methods, including Normal distribution, Weibull distribution and Log-normal distribution which can be a tedious process.…”

Section: Machine Learning In Almond Researchmentioning

confidence: 99%

Size and mass prediction of almond kernels using machine learning image processing

Tiwari

Singh

2019

Preprint

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After harvesting almond crop, accurate measurement of almond kernel sizes is a significant specification to plan, develop and enhance almond processing operations. The size and mass of the individual almond kernels are vital parameters usually associated with almond quality, particularly head almond yield. In this study, we propose a novel methodology that combines image processing and machine-learning ensemble that accurately measures the size and mass of whole raw almond kernels (classification -Nonpareil) simultaneously. We have developed an image-processing algorithm using recursive method to identify the individual almond kernels from an image and estimate the size of the kernels based on the occupied pixels by a kernel. The number of pixels representing an almond kernel was used as its digital fingerprint to predict its size and mass. Various popular machine learning (ML) models were implemented to build a stacked ensemble model (SEM), predicting the mass of the individual almond kernels based on the features derived from the pixels of the individual kernels in the image. The prediction accuracy and robustness of image processing and SEM were analyzed using uncertainty quantification. The mean error in estimating the average length of 1000 almond kernel was 3.12%. Similarly, mean errors associated with predicting the 1000 kernel mass were 0.63%. The developed algorithm in almond imaging in this study can be used to facilitate a rapid almond yield and quality appraisals.

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Section: Machine Learning In Almond Researchmentioning

confidence: 99%

Size and mass prediction of almond kernels using machine learning image processing

Tiwari

Singh

2019

Preprint

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“…Recently, machine learning tools have facilitated the development CG force fields [9][10][11] and graph-based CG representations [12,13]. Here we propose to use machine learning to simultaneously optimize CG representations and potentials from atomistic simulations.…”

Section: Introductionmentioning

confidence: 99%

Coarse-graining auto-encoders for molecular dynamics

2019

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Molecular dynamics simulations provide theoretical insight into the microscopic behavior of materials in condensed phase and, as a predictive tool, enable computational design of new compounds. However, because of the large temporal and spatial scales involved in thermodynamic and kinetic phenomena in materials, atomistic simulations are often computationally unfeasible. Coarsegraining methods allow simulating larger systems, by reducing the dimensionality of the simulation, and propagating longer timesteps, by averaging out fast motions. Coarse-graining involves two coupled learning problems; defining the mapping from an all-atom to a reduced representation, and the parametrization of a Hamiltonian over coarse-grained coordinates. Multiple statistical mechanics approaches have addressed the latter, but the former is generally a hand-tuned process based on chemical intuition. Here we present Autograin, an optimization framework based on autoencoders to learn both tasks simultaneously. Autograin is trained to learn the optimal mapping between all-atom and reduced representation, using the reconstruction loss to facilitate the learning of coarse-grained variables. In addition, a force-matching method is applied to variationally determine the coarse-grained potential energy function. This procedure is tested on a number of model systems including single-molecule and bulk-phase periodic simulations.

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Section: Introductionmentioning

confidence: 99%

Stack ensembled model to measure size and mass of almond kernels

Vidyarthi

Tiwari

Singh

2020

J Food Process Engineering

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After harvesting almond crop, accurate measurement of almond kernel sizes is a significant specification to plan, develop and enhance almond processing operations. The size and mass of the individual almond kernels are vital parameters usually associated with almond quality and yield. In this study, we propose a novel methodology that combines image processing and machine‐learning ensemble that accurately measures the size and mass of whole raw almond kernels (classification—Nonpareil) simultaneously. We have developed an image‐processing algorithm using recursive method to identify the individual almond kernels from an image and estimate the size of the kernels based on the occupied pixels by a kernel. The number of pixels representing an almond kernel was used as its digital fingerprint to predict its size and mass. Various popular machine learning (ML) models were implemented to build a stacked ensemble model (SEM), predicting the mass of the individual almond kernels based on the features derived from the pixels of the individual kernels in the image. The prediction accuracy and robustness of image processing and SEM were analyzed using uncertainty quantification. The mean error in estimating the average length of 1,000 almond kernel was 3.12%. Similarly, mean errors associated with predicting the 1,000 kernel mass were 0.63%. The developed algorithm in almond imaging in this study can be used to facilitate a rapid almond yield and quality appraisals.

show abstract

Machine-Learned Coarse-Grained Models

Cited by 59 publications

References 37 publications

Size and mass prediction of almond kernels using machine learning image processing

Size and mass prediction of almond kernels using machine learning image processing

Coarse-graining auto-encoders for molecular dynamics

Stack ensembled model to measure size and mass of almond kernels

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