Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling

Data-scientific approaches have permeated in chemistry and materials science. In general, these approaches are not easily applied to small data, such as experimental data in laboratories. Our group has focused...

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

Annual Review of Materials Research ◽

10.1146/annurev-matsci-082019-105100 ◽

2020 ◽

Vol 50 (1) ◽

pp. 1-25 ◽

Cited By ~ 2

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.

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Targeted sequence design within the coarse-grained polymer genome

Science Advances ◽

10.1126/sciadv.abc6216 ◽

2020 ◽

Vol 6 (43) ◽

pp. eabc6216

Author(s):

Michael A. Webb ◽

Nicholas E. Jackson ◽

Phwey S. Gil ◽

Juan J. de Pablo

Keyword(s):

Machine Learning ◽

Materials Science ◽

Chain Structure ◽

Data Availability ◽

Data Driven ◽

Coarse Grained ◽

Grand Challenge ◽

Sequence Information ◽

Single Chain ◽

Sequence Design

The chemical design of polymers with target structural and/or functional properties represents a grand challenge in materials science. While data-driven design approaches are promising, success with polymers has been limited, largely due to limitations in data availability. Here, we demonstrate the targeted sequence design of single-chain structure in polymers by combining coarse-grained modeling, machine learning, and model optimization. Nearly 2000 unique coarse-grained polymers are simulated to construct and analyze machine learning models. We find that deep neural networks inexpensively and reliably predict structural properties with limited sequence information as input. By coupling trained ML models with sequential model-based optimization, polymer sequences are proposed to exhibit globular, swollen, or rod-like behaviors, which are verified by explicit simulations. This work highlights the promising integration of coarse-grained modeling with data-driven design and represents a necessary and crucial step toward more complex polymer design efforts.

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Preface to the special issue on machine learning and data-driven design of materials issue in computational materials science

Computational Materials Science ◽

10.1016/j.commatsci.2021.110452 ◽

2021 ◽

Vol 195 ◽

pp. 110452

Author(s):

Taylor D. Sparks ◽

Anton O. Oliynyk ◽

Vitaliy Romaka

Keyword(s):

Machine Learning ◽

Materials Science ◽

Data Driven ◽

Special Issue ◽

Computational Materials Science ◽

Computational Materials

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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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