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Chen Z, Chen C, Yang G, He X, Chi X, Zeng Z, Chen X. Research integrity in the era of artificial intelligence: Challenges and responses. Medicine (Baltimore) 2024; 103:e38811. [PMID: 38968491 PMCID: PMC11224801 DOI: 10.1097/md.0000000000038811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 06/13/2024] [Indexed: 07/07/2024] Open
Abstract
The application of artificial intelligence (AI) technologies in scientific research has significantly enhanced efficiency and accuracy but also introduced new forms of academic misconduct, such as data fabrication and text plagiarism using AI algorithms. These practices jeopardize research integrity and can mislead scientific directions. This study addresses these challenges, underscoring the need for the academic community to strengthen ethical norms, enhance researcher qualifications, and establish rigorous review mechanisms. To ensure responsible and transparent research processes, we recommend the following specific key actions: Development and enforcement of comprehensive AI research integrity guidelines that include clear protocols for AI use in data analysis and publication, ensuring transparency and accountability in AI-assisted research. Implementation of mandatory AI ethics and integrity training for researchers, aimed at fostering an in-depth understanding of potential AI misuses and promoting ethical research practices. Establishment of international collaboration frameworks to facilitate the exchange of best practices and development of unified ethical standards for AI in research. Protecting research integrity is paramount for maintaining public trust in science, making these recommendations urgent for the scientific community consideration and action.
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Affiliation(s)
- Ziyu Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
| | - Changye Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
| | - Guozhao Yang
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
| | - Xiangpeng He
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
| | - Xiaoxia Chi
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
| | - Zhuoying Zeng
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
- Chemical Analysis & Physical Testing Institute, Shenzhen Center for Disease Control and Prevention, Shenzhen, China
| | - Xuhong Chen
- The First Affiliated Hospital of Shenzhen University, Shenzhen University, Shenzhen, China
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2
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Gunder C, Zamani-Alavijeh M, Wangila E, Maia de Oliveira F, Sheibani A, Kryvyi S, Attwood PC, Mazur YI, Yu SQ, Salamo GJ. Algorithm-Based Linearly Graded Compositions of GeSn on GaAs (001) via Molecular Beam Epitaxy. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:909. [PMID: 38869534 PMCID: PMC11173740 DOI: 10.3390/nano14110909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 05/20/2024] [Accepted: 05/20/2024] [Indexed: 06/14/2024]
Abstract
The growth of high-composition GeSn films in the future will likely be guided by algorithms. In this study, we show how a logarithmic-based algorithm can be used to obtain high-quality GeSn compositions up to 16% on GaAs (001) substrates via molecular beam epitaxy. Herein, we use composition targeting and logarithmic Sn cell temperature control to achieve linearly graded pseudomorph Ge1-xSnx compositions up to 10% before partial relaxation of the structure and a continued gradient up to 16% GeSn. In this report, we use X-ray diffraction, simulation, secondary ion mass spectrometry, and atomic force microscopy to analyze and demonstrate some of the possible growths that can be produced with the enclosed algorithm. This methodology of growth is a major step forward in the field of GeSn development and the first ever demonstration of algorithmically driven, linearly graded GeSn films.
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Affiliation(s)
- Calbi Gunder
- Materials Science and Engineering, University of Arkansas, Fayetteville, AR 72701, USA
| | | | - Emmanuel Wangila
- Materials Science and Engineering, University of Arkansas, Fayetteville, AR 72701, USA
| | | | - Aida Sheibani
- Department of Physics, University of Arkansas, Fayetteville, AR 72701, USA
| | - Serhii Kryvyi
- Institute for Nanoscience and Engineering, University of Arkansas, Fayetteville, AR 72701, USA
| | - Paul C. Attwood
- Gunder and Attwood Armories, El Dorado Springs, MO 64744, USA
| | - Yuriy I. Mazur
- Institute for Nanoscience and Engineering, University of Arkansas, Fayetteville, AR 72701, USA
| | - Shui-Qing Yu
- Institute for Nanoscience and Engineering, University of Arkansas, Fayetteville, AR 72701, USA
- Department of Electrical Engineering, University of Arkansas, Fayetteville, AR 72701, USA
| | - Gregory J. Salamo
- Department of Physics, University of Arkansas, Fayetteville, AR 72701, USA
- Institute for Nanoscience and Engineering, University of Arkansas, Fayetteville, AR 72701, USA
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Dimassi S, Demoly F, Belkebir H, Cruz C, Kim KY, Gomes S, Qi HJ, André JC. A knowledge recommendation approach in design for multi-material 4D printing based on semantic similarity vector space model and case-based reasoning. COMPUT IND 2023. [DOI: 10.1016/j.compind.2022.103824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Jeong KJ, Jeong S, Lee S, Son CY. Predictive Molecular Models for Charged Materials Systems: From Energy Materials to Biomacromolecules. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2204272. [PMID: 36373701 DOI: 10.1002/adma.202204272] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/05/2022] [Indexed: 06/16/2023]
Abstract
Electrostatic interactions play a dominant role in charged materials systems. Understanding the complex correlation between macroscopic properties with microscopic structures is of critical importance to develop rational design strategies for advanced materials. But the complexity of this challenging task is augmented by interfaces present in the charged materials systems, such as electrode-electrolyte interfaces or biological membranes. Over the last decades, predictive molecular simulations that are founded in fundamental physics and optimized for charged interfacial systems have proven their value in providing molecular understanding of physicochemical properties and functional mechanisms for diverse materials. Novel design strategies utilizing predictive models have been suggested as promising route for the rational design of materials with tailored properties. Here, an overview of recent advances in the understanding of charged interfacial systems aided by predictive molecular simulations is presented. Focusing on three types of charged interfaces found in energy materials and biomacromolecules, how the molecular models characterize ion structure, charge transport, morphology relation to the environment, and the thermodynamics/kinetics of molecular binding at the interfaces is discussed. The critical analysis brings two prominent field of energy materials and biological science under common perspective, to stimulate crossover in both research field that have been largely separated.
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Affiliation(s)
- Kyeong-Jun Jeong
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
| | - Seungwon Jeong
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
| | - Sangmin Lee
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
| | - Chang Yun Son
- Department of Chemistry, Pohang University of Science and Technology (POSTECH), Pohang, 790-784, South Korea
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5
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Ziatdinov MA, Ghosh A, Kalinin SV. Physics makes the difference: Bayesian optimization and active learning via augmented Gaussian process. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac4baa] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Abstract
Both experimental and computational methods for the exploration of structure, functionality, and properties of materials often necessitate the search across broad parameter spaces to discover optimal experimental conditions and regions of interest in the image space or parameter space of computational models. The direct grid search of the parameter space tends to be extremely time-consuming, leading to the development of strategies balancing exploration of unknown parameter spaces and exploitation towards required performance metrics. However, classical Bayesian optimization (BO) strategies based on the Gaussian process (GP) do not readily allow for the incorporation of the known physical behaviors or past knowledge. Here we explore a hybrid optimization/exploration algorithm created by augmenting the standard GP with a structured probabilistic model of the expected system’s behavior. This approach balances the flexibility of the non-parametric GP approach with a rigid structure of physical knowledge encoded into the parametric model. The fully Bayesian treatment of the latter allows additional control over the optimization via the selection of priors for the model parameters. The method is demonstrated for a noisy version of a standard univariate test function used to evaluate optimization algorithms and further extended to physical lattice models. This methodology is expected to be universally suitable for injecting prior knowledge in the form of physical models and past data in the BO framework.
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Deagen ME, Brinson LC, Vaia RA, Schadler LS. The materials tetrahedron has a "digital twin". MRS BULLETIN 2022; 47:379-388. [PMID: 35968542 PMCID: PMC9365726 DOI: 10.1557/s43577-021-00214-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/06/2021] [Indexed: 05/16/2023]
Abstract
Abstract For over three decades, the materials tetrahedron has captured the essence of materials science and engineering with its interdependent elements of processing, structure, properties, and performance. As modern computational and statistical techniques usher in a new paradigm of data-intensive scientific research and discovery, the rate at which the field of materials science and engineering capitalizes on these advances hinges on collaboration between numerous stakeholders. Here, we provide a contemporary extension to the classic materials tetrahedron with a dual framework-adapted from the concept of a "digital twin"-which offers a nexus joining materials science and information science. We believe this high-level framework, the materials-information twin tetrahedra (MITT), will provide stakeholders with a platform to contextualize, translate, and direct efforts in the pursuit of propelling materials science and technology forward. Impact statement This article provides a contemporary reimagination of the classic materials tetrahedron by augmenting it with parallel notions from information science. Since the materials tetrahedron (processing, structure, properties, performance) made its first debut, advances in computational and informational tools have transformed the landscape and outlook of materials research and development. Drawing inspiration from the notion of a digital twin, the materials-information twin tetrahedra (MITT) framework captures a holistic perspective of materials science and engineering in the presence of modern digital tools and infrastructures. This high-level framework incorporates sustainability and FAIR data principles (Findable, Accessible, Interoperable, Reusable)-factors that recognize how systems impact and interact with other systems-in addition to the data and information flows that play a pivotal role in knowledge generation. The goal of the MITT framework is to give stakeholders from academia, industry, and government a communication tool for focusing efforts around the design, development, and deployment of materials in the years ahead. Graphic abstract
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Affiliation(s)
- Michael E. Deagen
- Department of Mechanical Engineering, The University of Vermont, Burlington, VT 05405 USA
| | - L. Catherine Brinson
- Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708 USA
| | - Richard A. Vaia
- Air Force Research Laboratory, Wright-Patterson Air Force Base, OH 45433 USA
| | - Linda S. Schadler
- Department of Mechanical Engineering, The University of Vermont, Burlington, VT 05405 USA
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Matsumoto Y, Maruyama S, Kaminaga K. Compositionally graded crystals as a revived approach for new crystal engineering for the exploration of novel functionalities. CrystEngComm 2022. [DOI: 10.1039/d2ce00041e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Nanoscale compositionally graded crystals have huge potential to allow the exploration of new functionalities through crystal lattice modulation.
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Affiliation(s)
- Yuji Matsumoto
- Department of Applied Chemistry, School of Engineering, Tohoku University, 6-6-07 Aramaki Aza Aoba, Aoba-ku Sendai, 980-8579 Japan
| | - Shingo Maruyama
- Department of Applied Chemistry, School of Engineering, Tohoku University, 6-6-07 Aramaki Aza Aoba, Aoba-ku Sendai, 980-8579 Japan
| | - Kenichi Kaminaga
- Department of Applied Chemistry, School of Engineering, Tohoku University, 6-6-07 Aramaki Aza Aoba, Aoba-ku Sendai, 980-8579 Japan
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8
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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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Butakova MA, Chernov AV, Kartashov OO, Soldatov AV. Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials. NANOMATERIALS (BASEL, SWITZERLAND) 2021; 12:12. [PMID: 35009962 PMCID: PMC8746699 DOI: 10.3390/nano12010012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/08/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
Artificial intelligence (AI) approaches continue to spread in almost every research and technology branch. However, a simple adaptation of AI methods and algorithms successfully exploited in one area to another field may face unexpected problems. Accelerating the discovery of new functional materials in chemical self-driving laboratories has an essential dependence on previous experimenters' experience. Self-driving laboratories help automate and intellectualize processes involved in discovering nanomaterials with required parameters that are difficult to transfer to AI-driven systems straightforwardly. It is not easy to find a suitable design method for self-driving laboratory implementation. In this case, the most appropriate way to implement is by creating and customizing a specific adaptive digital-centric automated laboratory with a data fusion approach that can reproduce a real experimenter's behavior. This paper analyzes the workflow of autonomous experimentation in the self-driving laboratory and distinguishes the core structure of such a laboratory, including sensing technologies. We propose a novel data-centric research strategy and multilevel data flow architecture for self-driving laboratories with the autonomous discovery of new functional nanomaterials.
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10
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Yang L, Haber JA, Armstrong Z, Yang SJ, Kan K, Zhou L, Richter MH, Roat C, Wagner N, Coram M, Berndl M, Riley P, Gregoire JM. Discovery of complex oxides via automated experiments and data science. Proc Natl Acad Sci U S A 2021; 118:e2106042118. [PMID: 34508002 PMCID: PMC8449358 DOI: 10.1073/pnas.2106042118] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/02/2021] [Indexed: 11/18/2022] Open
Abstract
The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search. Herein, we use high-throughput measurements of optical properties to identify novel regions in three-cation metal oxide composition spaces by identifying compositions whose optical trends cannot be explained by simple phase mixtures. We screen 376,752 distinct compositions from 108 three-cation oxide systems based on the cation elements Mg, Fe, Co, Ni, Cu, Y, In, Sn, Ce, and Ta. Data models for candidate phase diagrams and three-cation compositions with emergent optical properties guide the discovery of materials with complex phase-dependent properties, as demonstrated by the discovery of a Co-Ta-Sn substitutional alloy oxide with tunable transparency, catalytic activity, and stability in strong acid electrolytes. These results required close coupling of data validation to experiment design to generate a reliable end-to-end high-throughput workflow for accelerating scientific discovery.
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Affiliation(s)
- Lusann Yang
- Google Research, Google Applied Science, Mountain View, CA, 94043
| | - Joel A Haber
- Division of Engineering and Applied Science and Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, CA 91125
| | - Zan Armstrong
- Google Research, Google Applied Science, Mountain View, CA, 94043
| | - Samuel J Yang
- Google Research, Google Applied Science, Mountain View, CA, 94043
| | - Kevin Kan
- Division of Engineering and Applied Science and Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, CA 91125
| | - Lan Zhou
- Division of Engineering and Applied Science and Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, CA 91125
| | - Matthias H Richter
- Division of Engineering and Applied Science and Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, CA 91125
| | - Christopher Roat
- Google Research, Google Applied Science, Mountain View, CA, 94043
| | - Nicholas Wagner
- Google Research, Google Applied Science, Mountain View, CA, 94043
| | - Marc Coram
- Google Research, Google Applied Science, Mountain View, CA, 94043
| | - Marc Berndl
- Google Research, Google Applied Science, Mountain View, CA, 94043
| | - Patrick Riley
- Google Research, Google Applied Science, Mountain View, CA, 94043
| | - John M Gregoire
- Division of Engineering and Applied Science and Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, CA 91125
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11
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Vasudevan RK, Kelley KP, Hinkle J, Funakubo H, Jesse S, Kalinin SV, Ziatdinov M. Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics. ACS NANO 2021; 15:11253-11262. [PMID: 34228427 DOI: 10.1021/acsnano.0c10239] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO3, with results showing that, with just 20% of the area sampled, most high-response clusters were captured. This approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.
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Affiliation(s)
- Rama K Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Kyle P Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Jacob Hinkle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Hiroshi Funakubo
- Department of Material Science and Engineering, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
| | - Stephen Jesse
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Maxim Ziatdinov
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
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12
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Kitano H. Nobel Turing Challenge: creating the engine for scientific discovery. NPJ Syst Biol Appl 2021; 7:29. [PMID: 34145287 PMCID: PMC8213706 DOI: 10.1038/s41540-021-00189-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 06/03/2021] [Indexed: 12/15/2022] Open
Abstract
Scientific discovery has long been one of the central driving forces in our civilization. It uncovered the principles of the world we live in, and enabled us to invent new technologies reshaping our society, cure diseases, explore unknown new frontiers, and hopefully lead us to build a sustainable society. Accelerating the speed of scientific discovery is therefore one of the most important endeavors. This requires an in-depth understanding of not only the subject areas but also the nature of scientific discoveries themselves. In other words, the "science of science" needs to be established, and has to be implemented using artificial intelligence (AI) systems to be practically executable. At the same time, what may be implemented by "AI Scientists" may not resemble the scientific process conducted by human scientist. It may be an alternative form of science that will break the limitation of current scientific practice largely hampered by human cognitive limitation and sociological constraints. It could give rise to a human-AI hybrid form of science that shall bring systems biology and other sciences into the next stage. The Nobel Turing Challenge aims to develop a highly autonomous AI system that can perform top-level science, indistinguishable from the quality of that performed by the best human scientists, where some of the discoveries may be worthy of Nobel Prize level recognition and beyond.
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Affiliation(s)
- Hiroaki Kitano
- The Systems Biology Institute, Tokyo, Japan; Okinawa Institute of Science and Technology Graduate School, Okinawa, Japan; Sony Computer Science Laboratories, Inc., Tokyo, Japan; Sony AI, Inc., Tokyo, Japan; and The Alan Turing Institute, London, UK.
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13
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Tang Y, Li Z, Nellikkal MAN, Eramian H, Chan EM, Norquist AJ, Hsu DF, Schrier J. Improving Data and Prediction Quality of High-Throughput Perovskite Synthesis with Model Fusion. J Chem Inf Model 2021; 61:1593-1602. [PMID: 33797887 DOI: 10.1021/acs.jcim.0c01307] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Combinatorial fusion analysis (CFA) is an approach for combining multiple scoring systems using the rank-score characteristic function and cognitive diversity measure. One example is to combine diverse machine learning models to achieve better prediction quality. In this work, we apply CFA to the synthesis of metal halide perovskites containing organic ammonium cations via inverse temperature crystallization. Using a data set generated by high-throughput experimentation, four individual models (support vector machines, random forests, weighted logistic classifier, and gradient boosted trees) were developed. We characterize each of these scoring systems and explore 66 possible combinations of the models. When measured by the precision on predicting crystal formation, the majority of the combination models improves the individual model results. The best combination models outperform the best individual models by 3.9 percentage points in precision. In addition to improving prediction quality, we demonstrate how the fusion models can be used to identify mislabeled input data and address issues of data quality. In particular, we identify example cases where all single models and all fusion models do not give the correct prediction. Experimental replication of these syntheses reveals that these compositions are sensitive to modest temperature variations across the different locations of the heating element that can hinder or enhance the crystallization process. In summary, we demonstrate that model fusion using CFA can not only identify a previously unconsidered influence on reaction outcome but also be used as a form of quality control for high-throughput experimentation.
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Affiliation(s)
- Yuanqing Tang
- Laboratory of Informatics and Data Mining (LIDM), Department of Computer and Information Science, Fordham University, 113 West 60th Street, New York, New York 10023, United States
| | - Zhi Li
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, United States
| | | | - Hamed Eramian
- Netrias LLC, 3100 Clarendon Boulevard, Suite 200, Arlington, Virginia 22201, United States
| | - Emory M Chan
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, United States
| | - Alexander J Norquist
- Department of Chemistry, Haverford College, 370 Lancaster Avenue, Haverford, Pennsylvania 19041, United States
| | - D Frank Hsu
- Laboratory of Informatics and Data Mining (LIDM), Department of Computer and Information Science, Fordham University, 113 West 60th Street, New York, New York 10023, United States
| | - Joshua Schrier
- Department of Chemistry, Fordham University, 441 East Fordham Road, The Bronx, New York 10458, United States
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