Catalyze Materials Science with Machine Learning

2021 ◽  
pp. 1151-1171
Author(s):  
Jaehyun Kim ◽  
Donghoon Kang ◽  
Sangbum Kim ◽  
Ho Won Jang
Keyword(s):  
Machine Learning ◽  
Materials Science

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

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.

scholarly journals Two-step machine learning enables optimized nanoparticle synthesis

2021 ◽  
Vol 7 (1) ◽  
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

AbstractIn 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 the 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.

scholarly journals Evolution of Metastable Structures at Bimetallic Surfaces from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Yu Xie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Materials Science ◽  
Automated Analysis ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Bimetallic Systems ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
First Time

Restructuring of interfaces 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 long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. By developing and performing accelerated machine-learning molecular dynamics followed by an automated analysis method, we discover and characterize previously unidentified surface restructuring mechanisms in an unbiased fashion, including Pd-Ag place exchange and Ag pop-out, as well as step ascent and descent. Remarkably, layer-by-layer dissolution of Pd into Ag is always preceded by an encapsulation of Pd islands by Ag, resulting in a significant migration of Ag out of the surface and a formation of extensive vacancy pits within a period of microseconds. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. Our approach is broadly applicable to complex multimetallic systems and enables the previously intractable mechanistic investigation of restructuring dynamics at atomic resolution.

scholarly journals A data ecosystem to support machine learning in materials science

2019 ◽  
Vol 9 (4) ◽  
pp. 1125-1133 ◽  
Author(s):  
Ben Blaiszik ◽  
Logan Ward ◽  
Marcus Schwarting ◽  
Jonathon Gaff ◽  
Ryan Chard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
Data Ecosystem ◽  
Image Position

Abstract

Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery

2020 ◽  
Vol 50 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Changwon Suh ◽  
Clyde Fare ◽  
James A. Warren ◽  
Edward O. Pyzer-Knapp
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
State Of The Art ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Computational Learning ◽  
Data Intensive ◽  
Research Activities ◽  
Materials Genome

Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice.

scholarly journals Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine

Matter ◽  
2020 ◽  
Vol 3 (6) ◽  
pp. 1893-1922
Author(s):  
Edgar A. Galan ◽  
Haoran Zhao ◽  
Xukang Wang ◽  
Qionghai Dai ◽  
Wilhelm T.S. Huck ◽  
...  
Keyword(s):  
Machine Learning ◽  
Materials Science

scholarly journals Machine learning in materials science

InfoMat ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 338-358 ◽  
Author(s):  
Jing Wei ◽  
Xuan Chu ◽  
Xiang‐Yu Sun ◽  
Kun Xu ◽  
Hui‐Xiong Deng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Materials Science

scholarly journals Application of machine learning techniques to electron microscopic/spectroscopic image data analysis

Microscopy ◽  
2019 ◽  
Vol 69 (2) ◽  
pp. 110-122 ◽  
Author(s):  
Shunsuke Muto ◽  
Motoki Shiga
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
Hyperspectral Image ◽  
Statistical Treatment ◽  
Image Data ◽  
Machine Learning Techniques ◽  
Multivariate Curve Resolution ◽  
Chemical Components ◽  
Electron Microscopic ◽  
Analytical Instruments

Abstract The combination of scanning transmission electron microscopy (STEM) with analytical instruments has become one of the most indispensable analytical tools in materials science. A set of microscopic image/spectral intensities collected from many sampling points in a region of interest, in which multiple physical/chemical components may be spatially and spectrally entangled, could be expected to be a rich source of information about a material. To unfold such an entangled image comprising information and spectral features into its individual pure components would necessitate the use of statistical treatment based on informatics and statistics. These computer-aided schemes or techniques are referred to as multivariate curve resolution, blind source separation or hyperspectral image analysis, depending on their application fields, and are classified as a subset of machine learning. In this review, we introduce non-negative matrix factorization, one of these unfolding techniques, to solve a wide variety of problems associated with the analysis of materials, particularly those related to STEM, electron energy-loss spectroscopy and energy-dispersive X-ray spectroscopy. This review, which commences with the description of the basic concept, the advantages and drawbacks of the technique, presents several additional strategies to overcome existing problems and their extensions to more general tensor decomposition schemes for further flexible applications are described.

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