1
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Mroz AM, Toka PN, Del Río Chanona EA, Jelfs KE. Web-BO: towards increased accessibility of Bayesian optimisation (BO) for chemistry. Faraday Discuss 2024. [PMID: 39344946 DOI: 10.1039/d4fd00109e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Historically, the chemical discovery process has predominantly been a matter of trial-and-improvement, where small modifications are made to a chemical system, guided by chemical knowledge, with the aim of optimising towards a target property or combination of properties. While a trial-and-improvement approach is frequently successful, especially when assisted by the help of serendipity, the approach is incredibly time- and resource-intensive. Complicating this further, the available chemical space that could, in theory, be explored is remarkably vast. As we are faced with near infinite possibilities and limited resources, we require improved search methods to effectively move towards desired optima, e.g. chemical systems exhibiting a target property, or several desired properties. Bayesian optimisation (BO) has recently gained significant traction in chemistry, where within the BO framework, prior knowledge is used to inform and guide the search process to optimise towards desired chemical targets, e.g. optimal reaction conditions to maximise yield, or optimal catalyst exhibiting improved catalytic activity. While powerful, implementing BO algorithms in practice is largely limited to interfacing via various APIs - requiring advanced coding experience and bespoke scripts for each optimisation task. Further, it is challenging to seamlessly link these with electronic lab notebooks via a graphical user interface (GUI). Ultimately, this limits the accessibility of BO algorithms. Here, we present Web-BO, a GUI to support BO for chemical optimisation tasks. We demonstrate its performance using an open source dataset and associated emulator, and link the platform with an existing electronic lab notebook, datalab. By providing a GUI-based BO service, we hope to improve the accessibility of data-driven optimisation tools in chemistry; https://suprashare.rcs.ic.ac.uk/web-bo/.
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Affiliation(s)
- Austin M Mroz
- Department of Chemistry, Imperial College London, White City Campus, W12 0BZ, UK.
- I-X Centre for AI in Science, Imperial College London, White City Campus, W12 0BZ, UK
| | - Piotr N Toka
- Department of Chemistry, Imperial College London, White City Campus, W12 0BZ, UK.
| | | | - Kim E Jelfs
- Department of Chemistry, Imperial College London, White City Campus, W12 0BZ, UK.
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2
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Gongora AE, Friedman C, Newton DK, Yee TD, Doorenbos Z, Giera B, Duoss EB, Han TYJ, Sullivan K, Rodriguez JN. Accelerating the design of lattice structures using machine learning. Sci Rep 2024; 14:13703. [PMID: 38871775 DOI: 10.1038/s41598-024-63204-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/27/2024] [Indexed: 06/15/2024] Open
Abstract
Lattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities (R2 > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.
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Affiliation(s)
- Aldair E Gongora
- Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA.
| | - Caleb Friedman
- Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA
| | - Deirdre K Newton
- Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA
| | - Timothy D Yee
- Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA
| | - Zachary Doorenbos
- Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA
| | - Brian Giera
- Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA
| | - Eric B Duoss
- Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA
| | - Thomas Y-J Han
- Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA
| | - Kyle Sullivan
- Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA
| | - Jennifer N Rodriguez
- Lawrence Livermore National Laboratory, 7000 East Avenue, Livermore, CA, 94550, USA
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3
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Ebrahimian A, Mohammadi H, Maftoon N. Material characterization of human middle ear using machine-learning-based surrogate models. J Mech Behav Biomed Mater 2024; 153:106478. [PMID: 38493562 DOI: 10.1016/j.jmbbm.2024.106478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 02/09/2024] [Accepted: 02/24/2024] [Indexed: 03/19/2024]
Abstract
This study aims to introduce a novel non-invasive method for rapid material characterization of middle-ear structures, taking into consideration the invaluable insights provided by the mechanical properties of ear tissues. Valuable insights into various ear pathologies can be gleaned from the mechanical properties of ear tissues, yet conventional techniques for assessing these properties often entail invasive procedures that preclude their use on living patients. In this study, in the first step, we developed machine-learning models of the middle ear to predict its responses with a significantly lower computational cost in comparison to finite-element models. Leveraging findings from prior research, we focused on the most influential model parameters: the Young's modulus and thickness of the tympanic membrane and the Young's modulus of the stapedial annular ligament. The eXtreme Gradient Boosting (XGBoost) method was implemented for creating the machine-learning models. Subsequently, we combined the created machine-learning models with Bayesian optimization (BoTorch) for fast and efficient estimation of the Young's moduli of the tympanic membrane and the stapedial annular ligament. We demonstrate that the resultant surrogate models can fairly represent the vibrational responses of the umbo, stapes footplate, and vibration patterns of the tympanic membrane at most frequencies. Also, our proposed material characterization approach successfully estimated the Young's moduli of the tympanic membrane and stapedial annular ligament (separately and simultaneously) with values of mean absolute percentage error of less than 7%. The remarkable accuracy achieved through the proposed material characterization method underscores its potential for eventual clinical applications of estimating mechanical properties of the middle-ear structures for diagnostic purposes.
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Affiliation(s)
- Arash Ebrahimian
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada; Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, ON, Canada
| | - Hossein Mohammadi
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada; Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, ON, Canada
| | - Nima Maftoon
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada; Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, ON, Canada.
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4
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Schwartz J, Di ZW, Jiang Y, Manassa J, Pietryga J, Qian Y, Cho MG, Rowell JL, Zheng H, Robinson RD, Gu J, Kirilin A, Rozeveld S, Ercius P, Fessler JA, Xu T, Scott M, Hovden R. Imaging 3D chemistry at 1 nm resolution with fused multi-modal electron tomography. Nat Commun 2024; 15:3555. [PMID: 38670945 PMCID: PMC11053043 DOI: 10.1038/s41467-024-47558-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 04/03/2024] [Indexed: 04/28/2024] Open
Abstract
Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment is completed. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one-nanometer resolution in an Au-Fe3O4 metamaterial within an organic ligand matrix, Co3O4-Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX/EELS) signals. We thus demonstrate that sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.
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Affiliation(s)
- Jonathan Schwartz
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Zichao Wendy Di
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
| | - Yi Jiang
- Advanced Photon Source Facility, Argonne National Laboratory, Lemont, IL, USA
| | - Jason Manassa
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Jacob Pietryga
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA
- Department of Material Science and Engineering, Northwestern University, Evanston, IL, USA
| | - Yiwen Qian
- Department of Materials Science and Engineering, University of California at Berkeley, Berkeley, CA, USA
| | - Min Gee Cho
- Department of Materials Science and Engineering, University of California at Berkeley, Berkeley, CA, USA
- National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jonathan L Rowell
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA
| | - Huihuo Zheng
- Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont, IL, USA
| | - Richard D Robinson
- Department of Material Science and Engineering, Cornell University, Ithaca, NY, USA
- Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, NY, USA
| | - Junsi Gu
- Dow Chemical Co., Collegeville, PA, USA
| | | | | | - Peter Ercius
- National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jeffrey A Fessler
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Ting Xu
- Department of Materials Science and Engineering, University of California at Berkeley, Berkeley, CA, USA
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Mary Scott
- Department of Materials Science and Engineering, University of California at Berkeley, Berkeley, CA, USA.
- National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - Robert Hovden
- Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA.
- Applied Physics Program, University of Michigan, Ann Arbor, MI, USA.
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5
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Zhu C, Bamidele EA, Shen X, Zhu G, Li B. Machine Learning Aided Design and Optimization of Thermal Metamaterials. Chem Rev 2024; 124:4258-4331. [PMID: 38546632 PMCID: PMC11009967 DOI: 10.1021/acs.chemrev.3c00708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/31/2024] [Accepted: 02/08/2024] [Indexed: 04/11/2024]
Abstract
Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.
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Affiliation(s)
- Changliang Zhu
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
| | - Emmanuel Anuoluwa Bamidele
- Materials
Science and Engineering Program, University
of Colorado, Boulder, Colorado 80309, United States
| | - Xiangying Shen
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
| | - Guimei Zhu
- School
of Microelectronics, Southern University
of Science and Technology, Shenzhen 518055, P.R. China
| | - Baowen Li
- Department
of Materials Science and Engineering, Southern
University of Science and Technology, Shenzhen 518055, P.R. China
- School
of Microelectronics, Southern University
of Science and Technology, Shenzhen 518055, P.R. China
- Department
of Physics, Southern University of Science
and Technology, Shenzhen 518055, P.R. China
- Shenzhen
International Quantum Academy, Shenzhen 518048, P.R. China
- Paul M. Rady
Department of Mechanical Engineering and Department of Physics, University of Colorado, Boulder 80309, United States
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6
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Bae S, Shin D, Kim H, Han JW, Lee JM. Accelerated Structural Optimization for the Supported Metal System Based on Hybrid Approach Combining Bayesian Optimization with Local Search. J Chem Theory Comput 2024; 20:2284-2296. [PMID: 38358319 DOI: 10.1021/acs.jctc.3c01265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
Numerous systematic methods have been developed to search for the global minimum of the potential energy surface, which corresponds to the optimal atomic structure. However, the majority of them still demand a substantial computing load due to the relaxation process that is embedded as an inner step inside the algorithm. Here, we propose a hybrid approach that combines Bayesian optimization (BO) and a local search that circumvents the relaxation step and efficiently finds the optimum structure, particularly in supported metal systems. The hybridization strategy combining the capabilities of BO's effective exploration and the local search's fast convergence expedites structural search. In addition, the formulation of physical constraints regarding the materials system and the feature of screening structure similarity enhance the computational efficiency of the proposed method. The proposed algorithm is demonstrated in two supported metal systems, showing the potential of the proposed method in the field of structural optimization.
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Affiliation(s)
- Shinyoung Bae
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Dongjae Shin
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Haechang Kim
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jeong Woo Han
- Department of Materials Science and Engineering, Research Institute of Advanced Materials, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Jong Min Lee
- School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
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7
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Elkhayat D, Abdelmalak NS, Amer R, Awad HH. Ezetimibe-Loaded Nanostructured Lipid Carrier for Oral Delivery: Response Surface Methodology; In Vitro Characterization and Assessing the Antihyperlipidemic Effect in Rats. ACS OMEGA 2024; 9:8103-8116. [PMID: 38405515 PMCID: PMC10882650 DOI: 10.1021/acsomega.3c08428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/28/2024] [Accepted: 02/02/2024] [Indexed: 02/27/2024]
Abstract
Among the independent risk factors for the occurrence of cardiovascular diseases like atherosclerosis is hyperlipidemia. To decrease cardiovascular events and patient mortality, antihyperlipidemia therapy is crucial. Our study aimed to enhance the solubility of the poorly soluble lipid-lowering agent ezetimibe (EZ), a member of class II as per the Biopharmaceutics Classification System (BCS). The drug was formulated as a nanostructured lipid carrier (NLC) employing the ultrasonication technique. A response surface D-optimal design was employed to study the effect of changing the liquid lipid type and the percentage of liquid lipid with respect to total lipid amount on the particle size, zeta potential, percentage entrapment efficiency, and percentage of drug released after 24 h. Nine NLC formulations were prepared and pharmaceutically evaluated, and the optimized NLC formulation was selected, further characterized, and evaluated as well. Optimized EZ-NLC was assessed in the high-fat diet model to induce hyperlipidemia in rats in comparison with the EZ suspension. The results of the optimized formulation showed that the prepared NLCs were spherical with no aggregation having a particle size of 204.3 ± 19.17 nm, zeta potential equal to -32 ± 7.59 mV, and entrapment efficiency of 81.5 ± 3.58% and 72.15 ± 4.58% drug released after 24 h. EZ-NLC significantly decreased the elevated serum lipid parameters, including total cholesterol, triglycerides, and LDL-C, but significantly normalized serum HDL-C levels of rats kept on a high-fat diet. The results demonstrated the improved efficacy of EZ-NLC in ameliorating the elevated serum lipid parameters compared to EZ.
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Affiliation(s)
- Dalia Elkhayat
- Department
of Pharmaceutics, Faculty of Pharmacy, October
University for Modern Sciences and Arts (MSA), 26 July Mehwar Road intersection
with Wahat Road, Sixth October City, Giza 12451, Egypt
| | - Nevine S. Abdelmalak
- Department
of Pharmaceutics, Faculty of Pharmacy, Cairo University, Cairo, Egypt and School of pharmacy, New Giza University
NGU, Giza 3296121, Egypt
| | - Reham Amer
- Department
of Pharmaceutics and pharmaceutical technology, Faculty of Pharmacy, Al Azhar University Cairo, Cairo 4434003, Egypt
| | - Heba H. Awad
- Department
of Pharmacology and Toxicology, Faculty of Pharmacy, October University for Modern Sciences and Arts, Giza 12451, Egypt
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8
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Okada H, Maeda S. On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set. ACS OMEGA 2024; 9:7123-7131. [PMID: 38371820 PMCID: PMC10870292 DOI: 10.1021/acsomega.3c09066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/05/2024] [Accepted: 01/16/2024] [Indexed: 02/20/2024]
Abstract
Substrate optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools such as Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further by utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted substrate optimization, this study employs a computational Gibbs energy barrier data set in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as virtual experimental results and those with systematic and random noises as virtual computational results. The present numerical investigation shows that even the computational reactivity affected by noises of as much as 20 kJ/mol helps reduce the number of required experiments.
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Affiliation(s)
- Hiroaki Okada
- Graduate
School of Chemical Sciences and Engineering, Hokkaido University, Sapporo, Hokkaido 060-8628, Japan
| | - Satoshi Maeda
- Department
of Chemistry, Graduate School of Science, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Institute
for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University, Sapporo, Hokkaido 001-0021, Japan
- ERATO
Maeda Artificial Intelligence for Chemical Reaction Design and Discovery
Project, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
- Research
and Services Division of Materials Data and Integrated System (MaDIS), National Institute for Materials Science (NIMS), Tsukuba, Ibaraki 305-0044, Japan
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9
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Zhou T, Wan X, Huang DZ, Li Z, Peng Z, Anandkumar A, Brady JF, Sternberg PW, Daraio C. AI-aided geometric design of anti-infection catheters. SCIENCE ADVANCES 2024; 10:eadj1741. [PMID: 38170782 PMCID: PMC10776022 DOI: 10.1126/sciadv.adj1741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024]
Abstract
Bacteria can swim upstream in a narrow tube and pose a clinical threat of urinary tract infection to patients implanted with catheters. Coatings and structured surfaces have been proposed to repel bacteria, but no such approach thoroughly addresses the contamination problem in catheters. Here, on the basis of the physical mechanism of upstream swimming, we propose a novel geometric design, optimized by an artificial intelligence model. Using Escherichia coli, we demonstrate the anti-infection mechanism in microfluidic experiments and evaluate the effectiveness of the design in three-dimensionally printed prototype catheters under clinical flow rates. Our catheter design shows that one to two orders of magnitude improved suppression of bacterial contamination at the upstream end, potentially prolonging the in-dwelling time for catheter use and reducing the overall risk of catheter-associated urinary tract infection.
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Affiliation(s)
- Tingtao Zhou
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Xuan Wan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Daniel Zhengyu Huang
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA
- Beijing International Center for Mathematical Research, Peking University, Beijing 100871, China
| | - Zongyi Li
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA
| | - Zhiwei Peng
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Anima Anandkumar
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA
| | - John F. Brady
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Paul W. Sternberg
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Chiara Daraio
- Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125, USA
- Meta Platforms Inc., Reality Labs, 322 Airport Blvd., Burlingame, CA 94010, USA
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10
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Williamson E, Brutchey RL. Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis. Inorg Chem 2023; 62:16251-16262. [PMID: 37767941 PMCID: PMC10565808 DOI: 10.1021/acs.inorgchem.3c02697] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Indexed: 09/29/2023]
Abstract
The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.
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Affiliation(s)
- Emily
M. Williamson
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
| | - Richard L. Brutchey
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
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11
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Xu X, Zhao W, Wang L, Lin J, Du L. Efficient exploration of compositional space for high-performance copolymers via Bayesian optimization. Chem Sci 2023; 14:10203-10211. [PMID: 37772116 PMCID: PMC10530742 DOI: 10.1039/d3sc03174h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/04/2023] [Indexed: 09/30/2023] Open
Abstract
The traditional approach employed in copolymer compositional design, which relies on trial-and-error, faces low-efficiency and high-cost obstacles when attempting to simultaneously improve multiple conflicting properties. For example, designing co-cured polycyanurates that exhibit both moisture and thermal resistance, along with high modulus, is a long-term challenge because of the intrinsic trade-offs between these properties. In this work, to surmount these barriers, we developed a Bayesian optimization (BO)-guided method to expedite the discovery of co-cured polycyanurates exhibiting low water uptake, coupled with higher glass transition temperature and Young's modulus. By virtue of the knowledge of molecular simulations, benchmarking studies were carried out to develop an effective BO-guided method. Propelled by the developed method, several copolymers with improved comprehensive properties were obtained experimentally in a few iterations. This work provides guidance for efficiently designing other high-performance copolymers.
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Affiliation(s)
- Xinyao Xu
- Shanghai Key Laboratory of Advanced Polymeric Materials, Key Laboratory for Ultrafine Materials of Ministry of Education, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Materials Science and Engineering, East China University of Science and Technology Shanghai 200237 China
| | - Wenlin Zhao
- Shanghai Key Laboratory of Advanced Polymeric Materials, Key Laboratory for Ultrafine Materials of Ministry of Education, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Materials Science and Engineering, East China University of Science and Technology Shanghai 200237 China
| | - Liquan Wang
- Shanghai Key Laboratory of Advanced Polymeric Materials, Key Laboratory for Ultrafine Materials of Ministry of Education, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Materials Science and Engineering, East China University of Science and Technology Shanghai 200237 China
| | - Jiaping Lin
- Shanghai Key Laboratory of Advanced Polymeric Materials, Key Laboratory for Ultrafine Materials of Ministry of Education, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Materials Science and Engineering, East China University of Science and Technology Shanghai 200237 China
| | - Lei Du
- Shanghai Key Laboratory of Advanced Polymeric Materials, Key Laboratory for Ultrafine Materials of Ministry of Education, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Materials Science and Engineering, East China University of Science and Technology Shanghai 200237 China
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12
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Yoshikawa Y, Iwata T. Gaussian Process Regression With Interpretable Sample-Wise Feature Weights. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:5789-5803. [PMID: 34890339 DOI: 10.1109/tnnls.2021.3131234] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Gaussian process regression (GPR) is a fundamental model used in machine learning (ML). Due to its accurate prediction with uncertainty and versatility in handling various data structures via kernels, GPR has been successfully used in various applications. However, in GPR, how the features of an input contribute to its prediction cannot be interpreted. Here, we propose GPR with local explanation, which reveals the feature contributions to the prediction of each sample while maintaining the predictive performance of GPR. In the proposed model, both the prediction and explanation for each sample are performed using an easy-to-interpret locally linear model. The weight vector of the locally linear model is assumed to be generated from multivariate Gaussian process priors. The hyperparameters of the proposed models are estimated by maximizing the marginal likelihood. For a new test sample, the proposed model can predict the values of its target variable and weight vector, as well as their uncertainties, in a closed form. Experimental results on various benchmark datasets verify that the proposed model can achieve predictive performance comparable to those of GPR and superior to that of existing interpretable models and can achieve higher interpretability than them, both quantitatively and qualitatively.
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13
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Prabhune P, Comlek Y, Shandilya A, Sundararaman R, Schadler LS, Brinson LC, Chen W. Design of Polymer Nanodielectrics for Capacitive Energy Storage. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2394. [PMID: 37686902 PMCID: PMC10490420 DOI: 10.3390/nano13172394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/05/2023] [Accepted: 08/10/2023] [Indexed: 09/10/2023]
Abstract
Polymer nanodielectrics present a particularly challenging materials design problem for capacitive energy storage applications like polymer film capacitors. High permittivity and breakdown strength are needed to achieve high energy density and loss must be low. Strategies that increase permittivity tend to decrease the breakdown strength and increase loss. We hypothesize that a parameter space exists for fillers of modest aspect ratio functionalized with charge-trapping molecules that results in an increase in permittivity and breakdown strength simultaneously, while limiting increases in loss. In this work, we explore this parameter space, using physics-based, multiscale 3D dielectric property simulations, mixed-variable machine learning and Bayesian optimization to identify the compositions and morphologies which lead to the optimization of these competing properties. We employ first principle-based calculations for interface trap densities which are further used in breakdown strength calculations. For permittivity and loss calculations, we use continuum scale modelling and finite difference solution of Poisson's equation for steady-state currents. We propose a design framework for optimizing multiple properties by tuning design variables including the microstructure and interface properties. Finally, we employ mixed-variable global sensitivity analysis to understand the complex interplay between four continuous microstructural and two categorical interface choices to extract further physical knowledge on the design of nanodielectrics.
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Affiliation(s)
- Prajakta Prabhune
- Thomas Lord Department of Mechanical Engineering and Material Science, Duke University, Durham, NC 27708, USA; (P.P.); (L.C.B.)
| | - Yigitcan Comlek
- Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA;
| | - Abhishek Shandilya
- Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (A.S.); (R.S.)
| | - Ravishankar Sundararaman
- Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (A.S.); (R.S.)
| | - Linda S. Schadler
- College of Engineering and Mathematical Sciences, University of Vermont, Burlington, VT 05405, USA;
| | - Lynda Catherine Brinson
- Thomas Lord Department of Mechanical Engineering and Material Science, Duke University, Durham, NC 27708, USA; (P.P.); (L.C.B.)
| | - Wei Chen
- Department of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USA;
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14
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Xu Y, Farris CW, Anderson SW, Zhang X, Brown KA. Bayesian reconstruction of magnetic resonance images using Gaussian processes. Sci Rep 2023; 13:12527. [PMID: 37532743 PMCID: PMC10397278 DOI: 10.1038/s41598-023-39533-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/26/2023] [Indexed: 08/04/2023] Open
Abstract
A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations such as parallel imaging, compressed sensing, and deep learning-based reconstruction. Here, we propose and demonstrate a Bayesian method to build statistical libraries of magnetic resonance (MR) images in k-space and use these libraries to identify optimal subsampling paths and reconstruction processes. Specifically, we compute a multivariate normal distribution based upon Gaussian processes using a publicly available library of T1-weighted images of healthy brains. We combine this library with physics-informed envelope functions to only retain meaningful correlations in k-space. This covariance function is then used to select a series of ring-shaped subsampling paths using Bayesian optimization such that they optimally explore space while remaining practically realizable in commercial MRI systems. Combining optimized subsampling paths found for a range of images, we compute a generalized sampling path that, when used for novel images, produces superlative structural similarity and error in comparison to previously reported reconstruction processes (i.e. 96.3% structural similarity and < 0.003 normalized mean squared error from sampling only 12.5% of the k-space data). Finally, we use this reconstruction process on pathological data without retraining to show that reconstructed images are clinically useful for stroke identification. Since the model trained on images of healthy brains could be directly used for predictions in pathological brains without retraining, it shows the inherent transferability of this approach and opens doors to its widespread use.
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Affiliation(s)
- Yihong Xu
- Department of Physics, Boston University, Boston, MA, 02215, USA
| | - Chad W Farris
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Stephan W Anderson
- Department of Radiology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Xin Zhang
- Department of Mechanical Engineering, Boston University, Boston, MA, 02215, USA
- Department of Electrical & Computer Engineering, Boston University, Boston, MA, 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Division of Materials Science & Engineering, Boston University, Boston, MA, 02215, USA
| | - Keith A Brown
- Department of Physics, Boston University, Boston, MA, 02215, USA.
- Department of Mechanical Engineering, Boston University, Boston, MA, 02215, USA.
- Division of Materials Science & Engineering, Boston University, Boston, MA, 02215, USA.
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15
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Nasrin T, Pourali M, Pourkamali-Anaraki F, Peterson AM. Active learning for prediction of tensile properties for material extrusion additive manufacturing. Sci Rep 2023; 13:11460. [PMID: 37454171 DOI: 10.1038/s41598-023-38527-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 07/10/2023] [Indexed: 07/18/2023] Open
Abstract
Machine learning techniques were used to predict tensile properties of material extrusion-based additively manufactured parts made with Technomelt PA 6910, a hot melt adhesive. An adaptive data generation technique, specifically an active learning process based on the Gaussian process regression algorithm, was employed to enable prediction with limited training data. After three rounds of data collection, machine learning models based on linear regression, ridge regression, Gaussian process regression, and K-nearest neighbors were tasked with predicting properties for the test dataset, which consisted of parts fabricated with five processing parameters chosen using a random number generator. Overall, linear regression and ridge regression successfully predicted output parameters, with < 10% error for 56% of predictions. K-nearest neighbors performed worse than linear regression and ridge regression, with < 10% error for 32% of predictions and 10-20% error for 60% of predictions. While Gaussian process regression performed with the lowest accuracy (< 10% error for 32% of prediction cases and 10-20% error for 40% of predictions), it benefited most from the adaptive data generation technique. This work demonstrates that machine learning models using adaptive data generation techniques can efficiently predict properties of additively manufactured structures with limited training data.
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Affiliation(s)
- Tahamina Nasrin
- Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | - Masoumeh Pourali
- Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA, USA
| | | | - Amy M Peterson
- Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA, USA.
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16
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Wani P, Javidi B. 3D integral imaging depth estimation of partially occluded objects using mutual information and Bayesian optimization. OPTICS EXPRESS 2023; 31:22863-22884. [PMID: 37475387 DOI: 10.1364/oe.492160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/12/2023] [Indexed: 07/22/2023]
Abstract
Integral imaging (InIm) is useful for passive ranging and 3D visualization of partially-occluded objects. We consider 3D object localization within a scene and in occlusions. 2D localization can be achieved using machine learning and non-machine learning-based techniques. These techniques aim to provide a 2D bounding box around each one of the objects of interest. A recent study uses InIm for the 3D reconstruction of the scene with occlusions and utilizes mutual information (MI) between the bounding box in this 3D reconstructed scene and the corresponding bounding box in the central elemental image to achieve passive depth estimation of partially occluded objects. Here, we improve upon this InIm method by using Bayesian optimization to minimize the number of required 3D scene reconstructions. We evaluate the performance of the proposed approach by analyzing different kernel functions, acquisition functions, and parameter estimation algorithms for Bayesian optimization-based inference for simultaneous depth estimation of objects and occlusion. In our optical experiments, mutual-information-based depth estimation with Bayesian optimization achieves depth estimation with a handful of 3D reconstructions. To the best of our knowledge, this is the first report to use Bayesian optimization for mutual information-based InIm depth estimation.
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17
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Tassi S, Kigka V, Siogkas P, Rocchiccioli S, Pelosi G, Fotiadis DI, Sakellarios AI. Graph-guided Gaussian Process-based Diagnosis of CVD Severity with Uncertainty Measures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082986 DOI: 10.1109/embc40787.2023.10340916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The severity of coronary artery disease can be assessed invasively using the Fractional Flow Reserve (FFR) index which is a useful diagnostic tool for the clinicians to select the treatment approach. The present work capitalizes a Gaussian process (GP) framework over graphs for the prediction of FFR index using only non-invasive imaging and clinical features. More specifically, taking the per-node one-hop connectivity vector as input, we employed a regression-based task by applying an ensemble of graph-adapted Gaussian process experts, with a data-adaptive fashion via online training. The main novelty of the work lies in the fact that for the first time in a medical field the inference model considers only the similarity condition of the patients, instead of their features. Our results demonstrate the impressive merits of the proposed medical EGP (MedEGP) method, in comparison to the single GP, and Linear Regression (LR) models to predict the FFR index, with well-calibrated uncertainty.Clinical Relevance- This paper establishes an accurate non-invasive approach to predict the FFR for the diagnosis of coronary artery disease.
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18
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Lin YC, Torsi R, Younas R, Hinkle CL, Rigosi AF, Hill HM, Zhang K, Huang S, Shuck CE, Chen C, Lin YH, Maldonado-Lopez D, Mendoza-Cortes JL, Ferrier J, Kar S, Nayir N, Rajabpour S, van Duin ACT, Liu X, Jariwala D, Jiang J, Shi J, Mortelmans W, Jaramillo R, Lopes JMJ, Engel-Herbert R, Trofe A, Ignatova T, Lee SH, Mao Z, Damian L, Wang Y, Steves MA, Knappenberger KL, Wang Z, Law S, Bepete G, Zhou D, Lin JX, Scheurer MS, Li J, Wang P, Yu G, Wu S, Akinwande D, Redwing JM, Terrones M, Robinson JA. Recent Advances in 2D Material Theory, Synthesis, Properties, and Applications. ACS NANO 2023; 17:9694-9747. [PMID: 37219929 PMCID: PMC10324635 DOI: 10.1021/acsnano.2c12759] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Two-dimensional (2D) material research is rapidly evolving to broaden the spectrum of emergent 2D systems. Here, we review recent advances in the theory, synthesis, characterization, device, and quantum physics of 2D materials and their heterostructures. First, we shed insight into modeling of defects and intercalants, focusing on their formation pathways and strategic functionalities. We also review machine learning for synthesis and sensing applications of 2D materials. In addition, we highlight important development in the synthesis, processing, and characterization of various 2D materials (e.g., MXnenes, magnetic compounds, epitaxial layers, low-symmetry crystals, etc.) and discuss oxidation and strain gradient engineering in 2D materials. Next, we discuss the optical and phonon properties of 2D materials controlled by material inhomogeneity and give examples of multidimensional imaging and biosensing equipped with machine learning analysis based on 2D platforms. We then provide updates on mix-dimensional heterostructures using 2D building blocks for next-generation logic/memory devices and the quantum anomalous Hall devices of high-quality magnetic topological insulators, followed by advances in small twist-angle homojunctions and their exciting quantum transport. Finally, we provide the perspectives and future work on several topics mentioned in this review.
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Affiliation(s)
- Yu-Chuan Lin
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Materials Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan
| | - Riccardo Torsi
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Rehan Younas
- Department of Electrical Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Christopher L Hinkle
- Department of Electrical Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States
| | - Albert F Rigosi
- National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Heather M Hill
- National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Kunyan Zhang
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States
- Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Shengxi Huang
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas 77005, United States
- Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Christopher E Shuck
- A.J. Drexel Nanomaterials Institute and Department of Materials Science and Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States
| | - Chen Chen
- Two-Dimensional Crystal Consortium, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Yu-Hsiu Lin
- Department of Chemical Engineering & Materials Science, Michigan State University, East Lansing, Michigan 48824, United States
| | - Daniel Maldonado-Lopez
- Department of Chemical Engineering & Materials Science, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jose L Mendoza-Cortes
- Department of Chemical Engineering & Materials Science, Michigan State University, East Lansing, Michigan 48824, United States
| | - John Ferrier
- Department of Physics and Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Swastik Kar
- Department of Physics and Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nadire Nayir
- Two-Dimensional Crystal Consortium, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Mechanical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Physics, Karamanoglu Mehmet University, Karaman 70100, Turkey
| | - Siavash Rajabpour
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Adri C T van Duin
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Two-Dimensional Crystal Consortium, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Mechanical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Xiwen Liu
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Deep Jariwala
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Jie Jiang
- Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Jian Shi
- Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, United States
| | - Wouter Mortelmans
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
| | - Rafael Jaramillo
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142, United States
| | - Joao Marcelo J Lopes
- Paul-Drude-Institut für Festkörperelektronik, Leibniz-Institut im Forschungsverbund Berlin e.V., Hausvogteiplaz 5-7, 10117 Berlin, Germany
| | - Roman Engel-Herbert
- Paul-Drude-Institut für Festkörperelektronik, Leibniz-Institut im Forschungsverbund Berlin e.V., Hausvogteiplaz 5-7, 10117 Berlin, Germany
| | - Anthony Trofe
- Department of Nanoscience, Joint School of Nanoscience & Nanoengineering, University of North Carolina at Greensboro, Greensboro, North Carolina 27401, United States
| | - Tetyana Ignatova
- Department of Nanoscience, Joint School of Nanoscience & Nanoengineering, University of North Carolina at Greensboro, Greensboro, North Carolina 27401, United States
| | - Seng Huat Lee
- Two-Dimensional Crystal Consortium, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Zhiqiang Mao
- Two-Dimensional Crystal Consortium, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Leticia Damian
- Department of Physics, University of North Texas, Denton, Texas 76203, United States
| | - Yuanxi Wang
- Department of Physics, University of North Texas, Denton, Texas 76203, United States
| | - Megan A Steves
- Institute for Quantitative Biosciences, University of California Berkeley, Berkeley, California 94720, United States
| | - Kenneth L Knappenberger
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Zhengtianye Wang
- Two-Dimensional Crystal Consortium, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Stephanie Law
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Two-Dimensional Crystal Consortium, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - George Bepete
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Center for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Center for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Da Zhou
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Center for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Jiang-Xiazi Lin
- Department of Physics, Brown University, Providence, Rhode Island 02906, United States
| | - Mathias S Scheurer
- Institute for Theoretical Physics, University of Innsbruck, Innsbruck A-6020, Austria
| | - Jia Li
- Department of Physics, Brown University, Providence, Rhode Island 02906, United States
| | - Pengjie Wang
- Department of Physics, Princeton University, Princeton, New Jersey 08540, United States
| | - Guo Yu
- Department of Physics, Princeton University, Princeton, New Jersey 08540, United States
- Department of Electrical and Computer Engineering, Princeton University, Princeton, New Jersey 08540, United States
| | - Sanfeng Wu
- Department of Physics, Princeton University, Princeton, New Jersey 08540, United States
| | - Deji Akinwande
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
- Microelectronics Research Center, The University of Texas, Austin, Texas 78758, United States
| | - Joan M Redwing
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Electrical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Two-Dimensional Crystal Consortium, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Mauricio Terrones
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Center for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Center for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Research Initiative for Supra-Materials and Global Aqua Innovation Center, Shinshu University, Nagano 380-8553, Japan
| | - Joshua A Robinson
- Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Two-Dimensional Crystal Consortium, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Chemistry, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Center for 2-Dimensional and Layered Materials, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Center for Atomically Thin Multifunctional Coatings, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
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Diot J, Iwata H. Bayesian optimisation for breeding schemes. FRONTIERS IN PLANT SCIENCE 2023; 13:1050198. [PMID: 36714776 PMCID: PMC9875003 DOI: 10.3389/fpls.2022.1050198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 11/14/2022] [Indexed: 06/18/2023]
Abstract
INTRODUCTION Advances in genotyping technologies have provided breeders with access to the genotypic values of several thousand genetic markers in their breeding materials. Combined with phenotypic data, this information facilitates genomic selection. Although genomic selection can benefit breeders, it does not guarantee efficient genetic improvement. Indeed, multiple components of breeding schemes may affect the efficiency of genetic improvement and controlling all components may not be possible. In this study, we propose a new application of Bayesian optimisation for optimizing breeding schemes under specific constraints using computer simulation. METHODS Breeding schemes are simulated according to nine different parameters. Five of those parameters are considered constraints, and 4 can be optimised. Two optimisation methods are used to optimise those parameters, Bayesian optimisation and random optimisation. RESULTS The results show that Bayesian optimisation indeed finds breeding scheme parametrisations that provide good breeding improvement with regard to the entire parameter space and outperforms random optimisation. Moreover, the results also show that the optimised parameter distributions differ according to breeder constraints. DISCUSSION This study is one of the first to apply Bayesian optimisation to the design of breeding schemes while considering constraints. The presented approach has some limitations and should be considered as a first proof of concept that demonstrates the potential of Bayesian optimisation when applied to breeding schemes. Determining a general "rule of thumb" for breeding optimisation may be difficult and considering the specific constraints of each breeding campaign is important for finding an optimal breeding scheme.
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20
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Uncertainty-aware mixed-variable machine learning for materials design. Sci Rep 2022; 12:19760. [PMID: 36396678 PMCID: PMC9672324 DOI: 10.1038/s41598-022-23431-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/31/2022] [Indexed: 11/18/2022] Open
Abstract
Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.
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21
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Kumar R. Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis? ACS APPLIED BIO MATERIALS 2022; 5:2507-2535. [PMID: 35642794 DOI: 10.1021/acsabm.2c00346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.
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Affiliation(s)
- Ramya Kumar
- Department of Chemical & Biological Engineering, Colorado School of Mines, 1613 Illinois St, Golden, Colorado 80401, United States
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22
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Synthesizing controlled microstructures of porous media using generative adversarial networks and reinforcement learning. Sci Rep 2022; 12:9034. [PMID: 35641549 PMCID: PMC9156766 DOI: 10.1038/s41598-022-12845-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 05/17/2022] [Indexed: 11/09/2022] Open
Abstract
For material modeling and discovery, synthetic microstructures play a critical role as digital twins. They provide stochastic samples upon which direct numerical simulations can be conducted to populate material databases. A large ensemble of simulation data on synthetic microstructures may provide supplemental data to inform and refine macroscopic material models, which might not be feasible from physical experiments alone. However, synthesizing realistic microstructures with realistic microstructural attributes is highly challenging. Thus, it is often oversimplified via rough approximations that may yield an inaccurate representation of the physical world. Here, we propose a novel deep learning method that can synthesize realistic three-dimensional microstructures with controlled structural properties using the combination of generative adversarial networks (GAN) and actor-critic (AC) reinforcement learning. The GAN-AC combination enables the generation of microstructures that not only resemble the appearances of real specimens but also yield user-defined physical quantities of interest (QoI). Our validation experiments confirm that the properties of synthetic microstructures generated by the GAN-AC framework are within a 5% error margin with respect to the target values. The scientific contribution of this paper resides in the novel design of the GAN-AC microstructure generator and the mathematical and algorithmic foundations therein. The proposed method will have a broad and substantive impact on the materials community by providing lenses for analyzing structure-property-performance linkages and for implementing the notion of 'materials-by-design'.
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23
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Abstract
Optimal design of polymers is a challenging task due to their enormous chemical and configurational space. Recent advances in computations, machine learning, and increasing trends in data and software availability can potentially address this problem and accelerate the molecular-scale design of polymers. Here, the central problem of polymer design is reviewed, and the general ideas of data-driven methods and their working principles in the context of polymer design are discussed. This Review provides a historical perspective and a summary of current trends and outlines future scopes of data-driven methods for polymer research. A few representative case studies on the use of such data-driven methods for discovering new polymers with exceptional properties are presented. Moreover, attempts are made to highlight how data-driven strategies aid in establishing new correlations and advancing the fundamental understanding of polymers. This Review posits that the combination of machine learning, rapid computational characterization of polymers, and availability of large open-sourced homogeneous data will transform polymer research and development over the coming decades. It is hoped that this Review will serve as a useful reference to researchers who wish to develop and deploy data-driven methods for polymer research and education.
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Affiliation(s)
- Tarak K. Patra
- Department of Chemical Engineering,
Center for Atomistic Modeling and Materials Design and Center for
Carbon Capture Utilization and Storage, Indian Institute of Technology Madras, Chennai, TN 600036, India
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Vangelatos Z, Sheikh HM, Marcus PS, Grigoropoulos CP, Lopez VZ, Flamourakis G, Farsari M. Strength through defects: A novel Bayesian approach for the optimization of architected materials. SCIENCE ADVANCES 2021; 7:eabk2218. [PMID: 34623909 PMCID: PMC8500519 DOI: 10.1126/sciadv.abk2218] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/17/2021] [Indexed: 06/13/2023]
Abstract
We use a previously unexplored Bayesian optimization framework, “evolutionary Monte Carlo sampling,” to systematically design the arrangement of defects in an architected microlattice to maximize its strain energy density before undergoing catastrophic failure. Our algorithm searches a design space with billions of 4 × 4 × 5 3D lattices, yet it finds the global optimum with only 250 cost function evaluations. Our optimum has a normalized strain energy density 12,464 times greater than its commonly studied defect-free counterpart. Traditional optimization is inefficient for this microlattice because (i) the design space has discrete, qualitative parameter states as input variables, (ii) the cost function is computationally expensive, and (iii) the design space is large. Our proposed framework is useful for architected materials and for many optimization problems in science and elucidates how defects can enhance the mechanical performance of architected materials.
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Affiliation(s)
- Zacharias Vangelatos
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
- Laser Thermal Lab, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Haris Moazam Sheikh
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
- Computational Fluid Dynamics Laboratory, University of California, Berkeley, CA 94720, USA
| | - Philip S. Marcus
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
- Computational Fluid Dynamics Laboratory, University of California, Berkeley, CA 94720, USA
| | - Costas P. Grigoropoulos
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
- Laser Thermal Lab, University of California, Berkeley, Berkeley, CA 94720, USA
| | | | - George Flamourakis
- Institute of Electronic Structure and Laser (IESL), Foundation of Research and Technology–Hellas (FORTH), Heraklion 70013, Crete, Greece
| | - Maria Farsari
- Institute of Electronic Structure and Laser (IESL), Foundation of Research and Technology–Hellas (FORTH), Heraklion 70013, Crete, Greece
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Ebrahimian A, Tang H, Furlong C, Cheng JT, Maftoon N. Material characterization of thin planar structures using full-field harmonic vibration response measured with stroboscopic holography. INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2021; 198:106390. [PMID: 34565830 PMCID: PMC8457049 DOI: 10.1016/j.ijmecsci.2021.106390] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
We propose a novel material characterization method to estimate the Young's modulus of thin 2-D structures using non-modal noisy single frequency harmonic vibration data measured with holography. The method uses finite-difference discretization to apply the plate equation to all measured pixels inside the boundary of the vibrating structure and then treats the problem as a Bayesian optimization process to find the value of the Young's modulus by minimizing the Euclidian distance between the measured displacement field and repeatedly calculated displacement field using the plate equation. In order to assess the accuracy of the method, ground truth harmonic displacement magnitude fields of different plates were obtained using analytical solutions and the finite-element method and were used to estimate the Young's moduli. We applied Gaussian and non-Gaussian noise with different intensities to assess the robustness and accuracy of the proposed material characterization method in the presence of noise. We demonstrated that for multiple benchmarks for signal to noise ratio of down to 0 dB, our proposed method had errors of less than 5%. We also quantified the effects of uncertainties in the geometrical and material parameters as well as boundary conditions on the estimated Young's modulus. Furthermore, we studied the effects of the mesh size on the runtime and applied the method to experimental holography vibration measurement data of a copper plate.
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Affiliation(s)
- Arash Ebrahimian
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Haimi Tang
- Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
| | - Cosme Furlong
- Center for Holographic Studies and Laser Micro-mechaTronics, Worcester, MA, USA
- Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
- Eaton-Peabody Laboratory, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
- Department of Otolaryngology, Harvard Medical School, Boston, MA, USA
| | - Jeffrey Tao Cheng
- Eaton-Peabody Laboratory, Massachusetts Eye and Ear Infirmary, Boston, MA, USA
- Department of Otolaryngology, Harvard Medical School, Boston, MA, USA
| | - Nima Maftoon
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
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Upadhya R, Kosuri S, Tamasi M, Meyer TA, Atta S, Webb MA, Gormley AJ. Automation and data-driven design of polymer therapeutics. Adv Drug Deliv Rev 2021; 171:1-28. [PMID: 33242537 PMCID: PMC8127395 DOI: 10.1016/j.addr.2020.11.009] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/10/2020] [Accepted: 11/12/2020] [Indexed: 01/01/2023]
Abstract
Polymers are uniquely suited for drug delivery and biomaterial applications due to tunable structural parameters such as length, composition, architecture, and valency. To facilitate designs, researchers may explore combinatorial libraries in a high throughput fashion to correlate structure to function. However, traditional polymerization reactions including controlled living radical polymerization (CLRP) and ring-opening polymerization (ROP) require inert reaction conditions and extensive expertise to implement. With the advent of air-tolerance and automation, several polymerization techniques are now compatible with well plates and can be carried out at the benchtop, making high throughput synthesis and high throughput screening (HTS) possible. To avoid HTS pitfalls often described as "fishing expeditions," it is crucial to employ intelligent and big data approaches to maximize experimental efficiency. This is where the disruptive technologies of machine learning (ML) and artificial intelligence (AI) will likely play a role. In fact, ML and AI are already impacting small molecule drug discovery and showing signs of emerging in drug delivery. In this review, we present state-of-the-art research in drug delivery, gene delivery, antimicrobial polymers, and bioactive polymers alongside data-driven developments in drug design and organic synthesis. From this insight, important lessons are revealed for the polymer therapeutics community including the value of a closed loop design-build-test-learn workflow. This is an exciting time as researchers will gain the ability to fully explore the polymer structural landscape and establish quantitative structure-property relationships (QSPRs) with biological significance.
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Affiliation(s)
| | | | | | | | - Supriya Atta
- Rutgers, The State University of New Jersey, USA
| | - Michael A Webb
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08540, USA
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Ghosh D. Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences. Int Stat Rev 2021. [DOI: 10.1111/insr.12439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Debashis Ghosh
- Department of Biostatistics and Informatics Colorado School of Public Health 13001 E. 17th Place University of Colorado Anschutz Medical Campus Aurora CO 80045 USA
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