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| 1 | Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks显示文摘X-ray diffraction(XRD)data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials.We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns.We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic,physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database(ICSD)and experimental data.As a test case,115 thin-film metalhalides spanning three dimensionalities and seven space groups are synthesized and classified.After testing various algorithms,we develop and implement an all convolutional neural network,with cross-validated accuracies for dimensionality and space group classification of 93 and 89%,respectively.We propose average class activation maps,computed from a global average pooling layer,to allow high model interpretability by human experimentalists,elucidating the root causes of misclassification.Finally,we systematically evaluate the maximum XRD pattern step size(data acquisition rate)before loss of predictive accuracy occurs,and determine it to be 0.16°2θ,which enables an XRD pattern to be obtained and classified in 5.5 min or less. | Felipe Oviedo Zekun Ren Shijing Sun Charles Settens Zhe Liu Noor Titan Putri Hartono Savitha Ramasamy Brian L.DeCost Siyu I.P.Tian Giuseppe Romano Aaron Gilad Kusne Tonio Buonassisi | 2019 | npj Computational Materials2019，，1： | 15 |
| 2 | Machine learning enables polymer cloud-point engineering via inverse design显示文摘Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers,particularly when designing polymers with desired phase behavior.Here we demonstrate high-accuracy tuning of poly(2-oxazoline)cloud point via machine learning.With a design space of four repeating units and a range of molecular masses,we achieve an accuracy of 4℃ root mean squared error(RMSE)in a temperature range of 24–90℃,employing gradient boosting with decision trees.The RMSE is>3x better than linear and polynomial regression.We perform inverse design via particle-swarm optimization,predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80℃.Our approach challenges the status quo in polymer design with a machine learning algorithm,that is capable of fast and systematic discovery of new polymers. | Jatin N.Kumar Qianxiao Li Karen Y.T.Tang Tonio Buonassisi Anibal L.Gonzalez-Oyarce Jun Ye | 2019 | npj Computational Materials2019，，1： | 4 |
| 3 | Two-step machine learning enables optimized nanoparticle synthesis显示文摘In materials science,the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming.In this study,we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum.Combining a Gaussian process-based Bayesian optimization(BO)with a deep neural network(DNN),the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions.Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum,the DNN is used to predict the colour palette accessible with the reaction synthesis.While remaining interpretable by humans,the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties,such as the role of each reactant on the shape and amplitude of the absorbance spectrum. | Flore Mekki-Berrada Zekun Ren Tan Huang Wai Kuan Wong Fang Zheng Jiaxun Xie Isaac Parker Siyu Tian Senthilnath Jayavelu Zackaria Mahfoud Daniil Bash Kedar Hippalgaonkar Saif Khan Tonio Buonassisi Qianxiao Li Xiaonan Wang | 2021 | npj Computational Materials2021，，1： | 3 |
| 4 | SnS thin-films by RF sputtering at room temperature显示文摘 | Katy Hartman J.L. Johnson Mariana I. Bertoni Daniel Recht Michael J. Aziz Michael A. Scarpulla Tonio Buonassisi | 2011 | Thin Solid Films2011，，21： | 2 |
| 5 | Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains显示文摘Bayesian optimization(BO)has been leveraged for guiding autonomous and high-throughput experiments in materials science.However,few have evaluated the efficiency of BO across a broad range of experimental materials domains.In this work,we quantify the performance of BO with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems.By defining acceleration and enhancement metrics for materials optimization objectives,we find that surrogate models such as Gaussian Process(GP)with anisotropic kernels and Random Forest(RF)have comparable performance in BO,and both outperform the commonly used GP with isotropic kernels.GP with anisotropic kernels has demonstrated the most robustness,yet RF is a close alternative and warrants more consideration because it is free from distribution assumptions,has smaller time complexity,and requires less effort in initial hyperparameter selection.We also raise awareness about the benefits of using GP with anisotropic kernels in future materials optimization campaigns. | Qiaohao Liang Aldair E.Gongora Zekun Ren Armi Tiihonen Zhe Liu Shijing Sun James R.Deneault Daniil Bash Flore Mekki-Berrada Saif A.Khan Kedar Hippalgaonkar Benji Maruyama Keith A.Brown John Fisher III Tonio Buonassisi | 2021 | npj Computational Materials2021，，1： | 2 |
| 6 | Interpretable and Explainable Machine Learning for Materials Science and Chemistry显示文摘Machine learning has become a common and powerful tool in materials research.As more data become available,with the use of high-performance computing and high-throughput experimentation,machine learning has proven potential to accelerate scientific research and technology development.Though the uptake of data-driven approaches for materials science is at an exciting,early stage,to realize the true potential of machine learning models for successful scientific discovery,they must have qualities beyond purely predictive power.The predictions and inner workings of models should provide a certain degree of explainability by human experts,permitting the identification of potential model issues or limitations,building trust in model predictions,and unveiling unexpected correlations that may lead to scientific insights.In this work,we summarize applications of interpretability and explainability techniques for materials science and chemistry and discuss how these techniques can improve the outcome of scientific studies.We start by defining the fundamental concepts of interpretability and explainability in machine learning and making them less abstract by providing examples in the field.We show how interpretability in scientific machine learning has additional constraints compared to general applications.Building upon formal definitions in machine learning,we formulate the basic trade-offs among the explainability,completeness,and scientific validity of model explanations in scientific problems.In the context of these trade-offs,we discuss how interpretable models can be constructed,what insights they provide,and what drawbacks they have.We present numerous examples of the application of interpretable machine learning in a variety of experimental and simulation studies,encompassing first-principles calculations,physicochemical characterization,materials development,and integration into complex systems.We discuss the varied impacts and uses of interpretabiltiy in these cases according to the nature and constraints of the scientific study of interest.We discuss various challenges for interpretable machine learning in materials science and,more broadly,in scientific settings.In particular,we emphasize the risks of inferring causation or reaching generalization by purely interpreting machine learning models and the need for uncertainty estimates for model explanations.Finally,we showcase a number of exciting developments in other fields that could benefit interpretability in material science problems.Adding interpretability to a machine learning model often requires no more technical know-how than building the model itself.By providing concrete examples of studies(many with associated open source code and data),we hope that this Account will encourage all practitioners of machine learning in materials science to look deeper into their models. | Felipe Oviedo Juan Lavista Ferres Tonio Buonassisi Keith T.Butler | 2022 | Accounts of Materials Research2022，3，6： | 2 |
| 7 | Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics显示文摘Process optimization of photovoltaic devices is a time-intensive,trial-and-error endeavor,which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum.Herein,we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide(GaAs)solar cells that identifies the root cause(s)of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization.Our Bayesian network approach links a key GaAs process variable(growth temperature)to material descriptors(bulk and interface properties,e.g.,bulk lifetime,doping,and surface recombination)and device performance parameters(e.g.,cell efficiency).For this purpose,we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100×faster than numerical solvers.With the trained surrogate model and only a small number of experimental samples,our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist.As a demonstration of our method,in only five metal organic chemical vapor depositions,we identify a superior growth temperature profile for the window,bulk,and back surface field layer of a GaAs solar cell,without any secondary measurements,and demonstrate a 6.5%relative AM1.5G efficiency improvement above traditional grid search methods. | Zekun Ren Felipe Oviedo Maung Thway Siyu I.P.Tian Yue Wang Hansong Xue Jose Dario Perea Mariya Layurova Thomas Heumueller Erik Birgersson Armin G.Aberle Christoph J.Brabec Rolf Stangl Qianxiao Li Shijing Sun Fen Lin Ian Marius Peters Tonio Buonassisi | 2020 | npj Computational Materials2020，，1： | 2 |
| 8 | Hyperhomocysteinemia in Liver Cir rhosis 显示文摘 | Carmen Berasain Jose An tonio Rodr i guez | 2001 | Hypertension2001，38，： | 1 |
| 9 | Blood levels of orgaochlorine residues and risk of breast cancer显示文摘 | Wolff MS Tonio PG Lee EW | 1993 | J Nat Cancer Inst1993，85，8： | 1 |
| 10 | The Company' s Pirates: How the Dutch East India Company Tried to Lead a Coalition of Pirates to War against China, 1621-1662显示文摘 | Andrade Tonio | 2004 | Joumal of World History2004，，4： | 1 |
| 11 | Analytical and experimental study on bonded-in CFRP bars in glulam timber 显示文摘 | De Lorenzis Laura Scialpi Vincenza La Tegola An- tonio | 2005 | Composites Part B: Engineering2005，36，4： | 1 |
| 12 | Flexural reinforcement of glulam timber beams and joints with carbon fiber-reinforced polymer rods 显示文摘 | Micelli Francesco Scialpi Vincenza La Tegola An- tonio | 2005 | Journal of Composites for Construction2005，9，4： | 1 |
| 13 | Repression of the CDK activator Cdc25A and cell-cyclin arrast by cytokine TGF-B in cells lacking the CDK inhibitor p15显示文摘 | Tonio Lavarone Jone Massague | 1997 | Nature1997，387，： | 1 |
| 14 | Fractional Reserve versus angiography for guiding pereutaneous coronary intervention 显示文摘 | Tonio PA De Bruyne B Pijls NH | 2009 | N Engl J Med2009，360，3： | 1 |
| 15 | Cigarettesmoke and lipopolysaccharide induce a proliferative airwaysmooth muscle phenotype显示文摘 | TONIO P REINOUD G ANDRIES H | 2010 | Respiratory Res2010，11，： | 1 |
| 16 | HAWT Near-Wake aerodynamics, Part I: Axial flow conditions 显示文摘 | Wouter Haans Tonio Sant | 2008 | Wind Energy2008，11，： | 1 |
| 17 | Determination of trace amounts of bisphe- nol F, bisphenol A and their diglyeidyl ethers in wastewater by gas chromatography-mass spectrome- try显示文摘 | Vlchez Jose Luis Zafra Alberto Gonzaea casado An- tonio | 2001 | Anal Chim Acta2001，431，1： | 1 |
| 18 | HAWT near-wake aerodynamics,part I : Axial flow conditions显示文摘 | Wouter H Tonio S | 2008 | Wind Energy2008，11，3： | 1 |
| 19 | HAWT near-wake aerodynamics,Part I:Axial flow conditions显示文摘 | Wouter Haans Tonio Sant | | 0，，03： | 1 |
| 20 | Arginase and pulmonary diseases显示文摘 | Harm Maarsingh Tonio Pera Herman Meurs | 2008 | Naunyn - Schmiedeberg’s Archives of Pharmacology2008，，2： | 1 |
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