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Day AL, Wahl CB, Gupta V, Dos Reis R, Liao WK, Mirkin CA, Dravid VP, Choudhary A, Agrawal A. Machine Learning-Enabled Image Classification for Automated Electron Microscopy. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2024; 30:456-465. [PMID: 38758983 DOI: 10.1093/mam/ozae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 02/19/2024] [Accepted: 04/29/2024] [Indexed: 05/19/2024]
Abstract
Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of "big data" and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.
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Affiliation(s)
- Alexandra L Day
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Technological Institute, 2145 Sheridan Road, Room L359, Evanston, IL 60208, USA
| | - Carolin B Wahl
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Cook Hall, 2220 Campus Drive, Room 2036, Evanston, IL 60208, USA
- International Institute for Nanotechnology, Northwestern University, Technological Institute, 2145 Sheridan Road, Room K111, Evanston, IL 60208, USA
| | - Vishu Gupta
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Technological Institute, 2145 Sheridan Road, Room L359, Evanston, IL 60208, USA
| | - Roberto Dos Reis
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Cook Hall, 2220 Campus Drive, Room 2036, Evanston, IL 60208, USA
- International Institute for Nanotechnology, Northwestern University, Technological Institute, 2145 Sheridan Road, Room K111, Evanston, IL 60208, USA
- The NUANCE Center, Northwestern University, Technological Institute, 2145 Sheridan Road, Room A173, Evanston, IL 60208, USA
| | - Wei-Keng Liao
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Technological Institute, 2145 Sheridan Road, Room L359, Evanston, IL 60208, USA
| | - Chad A Mirkin
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Cook Hall, 2220 Campus Drive, Room 2036, Evanston, IL 60208, USA
- International Institute for Nanotechnology, Northwestern University, Technological Institute, 2145 Sheridan Road, Room K111, Evanston, IL 60208, USA
- Department of Chemistry, Northwestern University, Technological Institute, 2145 Sheridan Road, Room K148, Evanston, IL 60208, USA
| | - Vinayak P Dravid
- Department of Materials Science and Engineering, McCormick School of Engineering, Northwestern University, Cook Hall, 2220 Campus Drive, Room 2036, Evanston, IL 60208, USA
- International Institute for Nanotechnology, Northwestern University, Technological Institute, 2145 Sheridan Road, Room K111, Evanston, IL 60208, USA
- The NUANCE Center, Northwestern University, Technological Institute, 2145 Sheridan Road, Room A173, Evanston, IL 60208, USA
| | - Alok Choudhary
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Technological Institute, 2145 Sheridan Road, Room L359, Evanston, IL 60208, USA
| | - Ankit Agrawal
- Department of Electrical and Computer Engineering, McCormick School of Engineering, Northwestern University, Technological Institute, 2145 Sheridan Road, Room L359, Evanston, IL 60208, USA
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Omee SS, Louis SY, Fu N, Wei L, Dey S, Dong R, Li Q, Hu J. Scalable deeper graph neural networks for high-performance materials property prediction. PATTERNS (NEW YORK, N.Y.) 2022; 3:100491. [PMID: 35607621 PMCID: PMC9122959 DOI: 10.1016/j.patter.2022.100491] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 02/22/2022] [Accepted: 03/18/2022] [Indexed: 01/02/2023]
Abstract
Machine-learning-based materials property prediction models have emerged as a promising approach for new materials discovery, among which the graph neural networks (GNNs) have shown the best performance due to their capability to learn high-level features from crystal structures. However, existing GNN models suffer from their lack of scalability, high hyperparameter tuning complexity, and constrained performance due to over-smoothing. We propose a scalable global graph attention neural network model DeeperGATGNN with differentiable group normalization (DGN) and skip connections for high-performance materials property prediction. Our systematic benchmark studies show that our model achieves the state-of-the-art prediction results on five out of six datasets, outperforming five existing GNN models by up to 10%. Our model is also the most scalable one in terms of graph convolution layers, which allows us to train very deep networks (e.g., >30 layers) without significant performance degradation. Our implementation is available at https://github.com/usccolumbia/deeperGATGNN.
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Affiliation(s)
- Sadman Sadeed Omee
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA
| | - Steph-Yves Louis
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA
| | - Nihang Fu
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA
| | - Lai Wei
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA
| | - Sourin Dey
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA
| | - Rongzhi Dong
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA
| | - Qinyang Li
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA
| | - Jianjun Hu
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29201, USA
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Lanzoni D, Rovaris F, Montalenti F. Machine learning potential for interacting dislocations in the presence of free surfaces. Sci Rep 2022; 12:3760. [PMID: 35260604 PMCID: PMC8904841 DOI: 10.1038/s41598-022-07585-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 02/16/2022] [Indexed: 11/24/2022] Open
Abstract
Computing the total energy of a system of N interacting dislocations in the presence of arbitrary free surfaces is a difficult task, requiring Finite Element (FE) numerical calculations. Worst, high accuracy requires very fine meshes in the proximity of each dislocation core. Here we show that FE calculations can be conveniently replaced by a Machine Learning (ML) approach. After formulating the elastic problem in terms of one and two-body terms only, we use Sobolev training to obtain consistent information on both energy and forces, fitted using a feed-forward neural network (NN) architecture. As an example, we apply the proposed methodology to corrugated, heteroepitaxial semiconductor films, searching for the minimum-energy dislocation distributions by using Monte Carlo. Importantly, the presence of an interaction cutoff allows for the application of the method to systems of different sizes without the need to repeat training. Millions of energy evaluations are performed, a task which would have been impossible by brute-force FE calculations. Finally, we show how forces can be exploited in running 2D ML-based dislocation dynamics simulations.
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Affiliation(s)
- Daniele Lanzoni
- Materials Science Department, University of Milano-Bicocca, Via R. Cozzi 55, 20125, Milan, Italy
| | - Fabrizio Rovaris
- Materials Science Department, University of Milano-Bicocca, Via R. Cozzi 55, 20125, Milan, Italy. .,NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, Swierk, 05-400, Otwock, Poland.
| | - Francesco Montalenti
- Materials Science Department, University of Milano-Bicocca, Via R. Cozzi 55, 20125, Milan, Italy
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Deep Learning Approach to Mechanical Property Prediction of Single-Network Hydrogel. MATHEMATICS 2021. [DOI: 10.3390/math9212804] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Hydrogel has a complex network structure with inhomogeneous and random distribution of polymer chains. Much effort has been paid to fully understand the relationship between mesoscopic network structure and macroscopic mechanical properties of hydrogels. In this paper, we develop a deep learning approach to predict the mechanical properties of hydrogels from polymer network structures. First, network structural models of hydrogels are constructed from mesoscopic scale using self-avoiding walk method. The constructed model is similar to the real hydrogel network. Then, two deep learning models are proposed to capture the nonlinear mapping from mesoscopic hydrogel network structural model to its macroscale mechanical property. A deep neural network and a 3D convolutional neural network containing the physical information of the network structural model are implemented to predict the nominal stress–stretch curves of hydrogels under uniaxial tension. Our results show that the end-to-end deep learning framework can effectively predict the nominal stress–stretch curves of hydrogel within a wide range of mesoscopic network structures, which demonstrates that the deep learning models are able to capture the internal relationship between complex network structures and mechanical properties. We hope this approach can provide guidance to structural design and material property design of different soft materials.
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Frydrych K, Karimi K, Pecelerowicz M, Alvarez R, Dominguez-Gutiérrez FJ, Rovaris F, Papanikolaou S. Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges. MATERIALS (BASEL, SWITZERLAND) 2021; 14:5764. [PMID: 34640157 PMCID: PMC8510221 DOI: 10.3390/ma14195764] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 09/24/2021] [Accepted: 09/27/2021] [Indexed: 11/23/2022]
Abstract
In the design and development of novel materials that have excellent mechanical properties, classification and regression methods have been diversely used across mechanical deformation simulations or experiments. The use of materials informatics methods on large data that originate in experiments or/and multiscale modeling simulations may accelerate materials' discovery or develop new understanding of materials' behavior. In this fast-growing field, we focus on reviewing advances at the intersection of data science with mechanical deformation simulations and experiments, with a particular focus on studies of metals and alloys. We discuss examples of applications, as well as identify challenges and prospects.
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Affiliation(s)
- Karol Frydrych
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
| | - Kamran Karimi
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
| | - Michal Pecelerowicz
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
| | - Rene Alvarez
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
| | - Francesco Javier Dominguez-Gutiérrez
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
- Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY 11749, USA
| | - Fabrizio Rovaris
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
| | - Stefanos Papanikolaou
- NOMATEN Centre of Excellence, National Centre for Nuclear Research, ul. A. Sołtana 7, 05-400 Swierk-Otwock, Poland; (K.F.); (K.K.); (M.P.); (R.A.); (F.J.D.-G.); (F.R.)
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Guo K, Yang Z, Yu CH, Buehler MJ. Artificial intelligence and machine learning in design of mechanical materials. MATERIALS HORIZONS 2021; 8:1153-1172. [PMID: 34821909 DOI: 10.1039/d0mh01451f] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials and mechanical engineering, attributed to its power to predict materials properties, design de novo materials and discover new mechanisms beyond intuitions. As the structural complexity of novel materials soars, the material design problem to optimize mechanical behaviors can involve massive design spaces that are intractable for conventional methods. Addressing this challenge, ML models trained from large material datasets that relate structure, properties and function at multiple hierarchical levels have offered new avenues for fast exploration of the design spaces. The performance of a ML-based materials design approach relies on the collection or generation of a large dataset that is properly preprocessed using the domain knowledge of materials science underlying chemical and physical concepts, and a suitable selection of the applied ML model. Recent breakthroughs in ML techniques have created vast opportunities for not only overcoming long-standing mechanics problems but also for developing unprecedented materials design strategies. In this review, we first present a brief introduction of state-of-the-art ML models, algorithms and structures. Then, we discuss the importance of data collection, generation and preprocessing. The applications in mechanical property prediction, materials design and computational methods using ML-based approaches are summarized, followed by perspectives on opportunities and open challenges in this emerging and exciting field.
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Affiliation(s)
- Kai Guo
- Laboratory for Atomistic and Molecular Mechanics (LAMM), Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave. 1-290, Cambridge, Massachusetts 02139, USA.
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