Two-step machine learning enables optimized nanoparticle synthesis

In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with a desired absorbance spectrum. Combining a Gaussian Process based Bayesian Optimization (BO) with a Deep Neural Network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis, and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

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Two-Step Machine Learning Enables Optimized Nanoparticle Synthesis

10.26434/chemrxiv.12673742 ◽

2020 ◽

Author(s):

Flore Mekki-Berrada ◽

Zekun Ren ◽

Tan Huang ◽

Wai Kuan Wong ◽

Fang Zheng ◽

...

Keyword(s):

Machine Learning ◽

Optical Properties ◽

Materials Science ◽

Nanoparticle Synthesis ◽

Bayesian Optimization ◽

Absorbance Spectrum ◽

Nanomaterial Synthesis ◽

Target Spectrum ◽

Colour Palette ◽

Algorithmic Framework

In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with a desired absorbance spectrum. Combining a Gaussian Process based Bayesian Optimization (BO) with a Deep Neural Network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis, and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

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Machine Learning of Optical Properties of Materials - Predicting Spectra from Images and Images from Spectra

10.26434/chemrxiv.6726317 ◽

2018 ◽

Cited By ~ 1

Author(s):

Helge S. Stein ◽

Dan Guevarra ◽

Paul F Newhouse ◽

Edwin Soedarmadji ◽

John Gregoire

Keyword(s):

Machine Learning ◽

Optical Properties ◽

Band Gap ◽

Materials Science ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Underlying Structure ◽

Complex Data ◽

Properties Of Materials ◽

Optical Properties Of Materials

As the materials science community seeks to capitalize on recent advancements in computer science, the sparsity of well-labelled experimental data and limited throughput by which it can be generated have inhibited deployment of machine learning algorithms to date. Several successful examples in computational chemistry have inspired further adoption of machine learning algorithms, and in the present work we present autoencoding algorithms for measured optical properties of metal oxides, which can serve as an exemplar for the breadth and depth of data required for modern algorithms to learn the underlying structure of experimental materials science data. Our set of 180,902 distinct materials samples spans 78 distinct composition spaces, includes 45 elements, and contains more than 80,000 unique quinary oxide and 67,000 unique quaternary oxide compositions, making it the largest and most diverse experimental materials set utilized in machine learning studies. The extensive dataset enabled training and validation of 3 distinct models for mapping between sample images and absorption spectra, including a conditional variational autoencoder that generates images of hypothetical materials with tailored absorption properties. The absorption patterns auto-generated from sample images capture the salient features of ground truth spectra, and direct band gap energies extracted from these auto-generated patterns are quite accurate with a mean absolute error of 240 meV, which is the approximate uncertainty from traditional extraction of the band gap energy from measurements of the full transmission and reflection spectra. Optical properties of materials are not only ubiquitous in materials applications but also emblematic of the confluence of underlying physical phenomena that yield the type of complex data relationships that merit and benefit from neural network-type modelling.

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Machine Learning of Optical Properties of Materials - Predicting Spectra from Images and Images from Spectra

10.26434/chemrxiv.6726317.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Helge S. Stein ◽

Dan Guevarra ◽

Paul F Newhouse ◽

Edwin Soedarmadji ◽

John Gregoire

Keyword(s):

Machine Learning ◽

Optical Properties ◽

Band Gap ◽

Materials Science ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Underlying Structure ◽

Complex Data ◽

Properties Of Materials ◽

Optical Properties Of Materials

As the materials science community seeks to capitalize on recent advancements in computer science, the sparsity of well-labelled experimental data and limited throughput by which it can be generated have inhibited deployment of machine learning algorithms to date. Several successful examples in computational chemistry have inspired further adoption of machine learning algorithms, and in the present work we present autoencoding algorithms for measured optical properties of metal oxides, which can serve as an exemplar for the breadth and depth of data required for modern algorithms to learn the underlying structure of experimental materials science data. Our set of 180,902 distinct materials samples spans 78 distinct composition spaces, includes 45 elements, and contains more than 80,000 unique quinary oxide and 67,000 unique quaternary oxide compositions, making it the largest and most diverse experimental materials set utilized in machine learning studies. The extensive dataset enabled training and validation of 3 distinct models for mapping between sample images and absorption spectra, including a conditional variational autoencoder that generates images of hypothetical materials with tailored absorption properties. The absorption patterns auto-generated from sample images capture the salient features of ground truth spectra, and direct band gap energies extracted from these auto-generated patterns are quite accurate with a mean absolute error of 240 meV, which is the approximate uncertainty from traditional extraction of the band gap energy from measurements of the full transmission and reflection spectra. Optical properties of materials are not only ubiquitous in materials applications but also emblematic of the confluence of underlying physical phenomena that yield the type of complex data relationships that merit and benefit from neural network-type modelling.

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Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

10.26434/chemrxiv.11811660.v1 ◽

2020 ◽

Author(s):

Jin Soo Lim ◽

Jonathan Vandermause ◽

Matthijs A. van Spronsen ◽

Albert Musaelian ◽

Christopher R. O’Connor ◽

...

Keyword(s):

Machine Learning ◽

Molecular Dynamics ◽

Large Scale ◽

Materials Science ◽

Complete Characterization ◽

Layer By Layer ◽

Surface Restructuring ◽

Metastable Structures ◽

Mechanistic Investigation ◽

Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

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

Foods ◽

10.3390/foods10040763 ◽

2021 ◽

Vol 10 (4) ◽

pp. 763

Author(s):

Ran Yang ◽

Zhenbo Wang ◽

Jiajia Chen

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Food Product ◽

Integrated Approach ◽

Mechanistic Modeling ◽

Bayesian Optimization ◽

Initial Training ◽

Thickness Optimization ◽

Heating Uniformity ◽

Food Design

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.

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Catalyze Materials Science with Machine Learning

ACS Materials Letters ◽

10.1021/acsmaterialslett.1c00204 ◽

2021 ◽

pp. 1151-1171

Author(s):

Jaehyun Kim ◽

Donghoon Kang ◽

Sangbum Kim ◽

Ho Won Jang

Keyword(s):

Machine Learning ◽

Materials Science

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New Hybrid Approach for Developing Automated Machine Learning Workflows: A Real Case Application in Evaluation of Marcellus Shale Gas Production

Fuels ◽

10.3390/fuels2030017 ◽

2021 ◽

Vol 2 (3) ◽

pp. 286-303

Author(s):

Vuong Van Pham ◽

Ebrahim Fathi ◽

Fatemeh Belyadi

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Shale Gas ◽

Field Data ◽

Marcellus Shale ◽

Hybrid Approach ◽

Gas Production ◽

Bayesian Optimization ◽

Real Field ◽

Engineering Problems

The success of machine learning (ML) techniques implemented in different industries heavily rely on operator expertise and domain knowledge, which is used in manually choosing an algorithm and setting up the specific algorithm parameters for a problem. Due to the manual nature of model selection and parameter tuning, it is impossible to quantify or evaluate the quality of this manual process, which in turn limits the ability to perform comparison studies between different algorithms. In this study, we propose a new hybrid approach for developing machine learning workflows to help automated algorithm selection and hyperparameter optimization. The proposed approach provides a robust, reproducible, and unbiased workflow that can be quantified and validated using different scoring metrics. We have used the most common workflows implemented in the application of artificial intelligence (AI) and ML in engineering problems including grid/random search, Bayesian search and optimization, genetic programming, and compared that with our new hybrid approach that includes the integration of Tree-based Pipeline Optimization Tool (TPOT) and Bayesian optimization. The performance of each workflow is quantified using different scoring metrics such as Pearson correlation (i.e., R2 correlation) and Mean Square Error (i.e., MSE). For this purpose, actual field data obtained from 1567 gas wells in Marcellus Shale, with 121 features from reservoir, drilling, completion, stimulation, and operation is tested using different proposed workflows. A proposed new hybrid workflow is then used to evaluate the type well used for evaluation of Marcellus shale gas production. In conclusion, our automated hybrid approach showed significant improvement in comparison to other proposed workflows using both scoring matrices. The new hybrid approach provides a practical tool that supports the automated model and hyperparameter selection, which is tested using real field data that can be implemented in solving different engineering problems using artificial intelligence and machine learning. The new hybrid model is tested in a real field and compared with conventional type wells developed by field engineers. It is found that the type well of the field is very close to P50 predictions of the field, which shows great success in the completion design of the field performed by field engineers. It also shows that the field average production could have been improved by 8% if shorter cluster spacing and higher proppant loading per cluster were used during the frac jobs.

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