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Unexpected Transitions Yield Interesting Science and High-Performance Materials
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Unexpected Transitions Yield Interesting Science and High-Performance Materials John M. Gregoire1,* Solid-state materials are among the most promising enablers of future technologies and pose substantial challenges for accelerating scientific discovery. Combinatorial methods excel at finding surprising transitions in materials properties, and deeper materials understanding often follows. The technological promise and the discovery challenges of solid-state materials both arise from the incredible range of properties they exhibit. Computational screening, machine learning for property prediction, and autonomous (closed-loop) experimentation are among the strategies developed over the past couple of decades to accelerate the identification of materials with specific properties. These techniques complement and are sometimes combined with combinatorial materials science methods, which can be traced back to original concepts from the 1970s1 with more substantial development and deployment since the 1990s.2 Continued sophistication of this subfield of materials science has resulted from not only the expansion of combinatorial synthesis and characterization techniques, which enable deployment of combinatorial workflows as illustrated in Figure 1, but also the increased incorporation of automation, theory, and data science.4 In this issue of Matter, Woods-Robinson et al.5 exemplify the state of the art in combinatorial methods for fundamental understanding of functional materials with their incorporation of theory and a wide variety of metrologies to reveal the underpinnings of the observed trends in materials performance (transparency and conductance, in this case).
790
Combining materials with disparate properties can enable unique properties, often due to the elements crystallizing into different structures and/or alloying to create variants of prototype structures, providing compositional tuning of properties such as electronic structure and defect concentration. In addition to the intriguing materials science that occurs with materials whose compositions differ from the small integer ratios of prototype structures, the tuning of fundamental properties can also be leveraged to design application-tailored materials, as discussed in this work for the development of a p-type transparent conductor for photovoltaics. In this context, semiconductor doping is a primary phenomenon of interest, and traditional experimental investigation would involve addition of the dopant element at one or a few compositions in the sub1 atom% range. The use of continuous composition spreads enables not only parallel synthesis of a broad range of compositions—in this case the full range of Zn-Cu compositions synthesized under a quasi-static chemical potential of S—but also observation of functional relationships between composition and properties such as band gap and conductivity. Combined with structural characterization along the composition gradient, composition-structure-property relationships,
Matter 1, 788–800, October 2, 2019 ª 2019 Published by Elsevier Inc.
which facilitate interpretation of the underlying materials science, are readily established. A key discovery in this work is that strain-induced stabilization of the wurtzite structure of ZnS via alloying of Cu increases hole transport, optimizing performance as a transparent conductor and exhibiting a combination of large band gap and high conductivity observed in only a handful of other known materials. This discovery, as well as the observed property trends along the one-dimensional composition gradient, are emblematic of broader themes in scientific discovery. First, there is a general notion across scientific disciplines that interesting science occurs at the boundaries or transitions, which is highlighted in this work by the optimized performance at an unexpected phase boundary between the two different Cu-alloyed ZnS structures; the importance of continuous compositional variation in identifying such boundaries is notable. Second, high variability of properties within a small, local parameter space illustrates the difficulty of obtaining models that are accurate across the global parameter space. Grand visions have emerged for revolutionizing materials discovery via machine learning6 and broader applications of artificial intelligence,7 and a commonly employed machine-learning strategy involves training of a model that takes a composition or compound as input and predicts properties, which accelerates screening of candidate materials. Available training data are typically sparse in the many-dimensional materials parameter space, limiting the ability to learn rapid changes in
1Division
of Engineering and Applied Science and Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, CA, USA *Correspondence: [email protected] https://doi.org/10.1016/j.matt.2019.09.006
holds great promise for enabling a host of future technologies.
ACKNOWLEDGMENTS
Figure 1. Workflows for Accelerating Materials Discovery and Understanding where Designed Materials Are Rapidly Explored with Combinatorial Materials Science Techniques to Identify Technology-Enabling Materials Reproduced from Zakutayev et al. 3 under Creative Commons Attribution 4.0 International License.
properties over small intervals of the parameter space. Consider the anecdotal evidence from the present work that conductivity spans five orders of magnitude by varying a single compositional degree of freedom with the same three elements. What training data would be required to predict this enormous dynamic range in properties over such a small variation, and are there methods for collecting those data that are more efficient than the continuous composition spread strategy of the present work? While the community continues to build a framework for addressing these important questions, the integration of combinatorial materials science and computational materials science will continue to thrive as a prolific strategy for discovery and understanding of complex functional materials. The promise of combinatorial techniques illustrated by this work is countered by its demonstration of remaining challenges. The role of grain size versus crystal structure in optimizing
performance is not differentiated in the present work, as both properties change across the Cu-alloyed ZnS zincblende-wurzite phase boundary. Such convolutions of materials properties highlight a persistent disconnect between the desired and available axes for combinatorial exploration. Independent variation of materials properties such as composition, grain size, and crystal orientation is not possible due to the convolution of these properties with respect to experiment controls such as annealing temperature and position along a compositionally graded thin film. Computational methods play a key role in bridging these gaps by systematically varying properties of model structures and calculating the resulting changes in fundamental properties. Given the ample space for improvement for both experimental and computational methods, and especially their integration into materials discovery workflows, the community’s concerted effort to continually accelerate materials discovery and understanding
J.M.G. acknowledges support from the Joint Center for Artificial Photosynthesis, a DOE Energy Innovation Hub, supported through the Office of Science of the US Department of Energy under award number DE-SC0004993. 1. Hanak, J.J. (1970). The ‘‘multiple-sample concept’’ in materials research: Synthesis, compositional analysis and testing of entire multicomponent systems. J. Mater. Sci. 5, 964–971. 2. Green, M.L., Choi, C.L., Hattrick-Simpers, J.R., Joshi, A.M., Takeuchi, I., Barron, S.C., Campo, E., Chiang, T., Empedocles, S., Gregoire, J.M., et al. (2017). Fulfilling the promise of the materials genome initiative with highthroughput experimental methodologies. Appl. Phys. Rev. 4, 011105. 3. Zakutayev, A., Wunder, N., Schwarting, M., Perkins, J.D., White, R., Munch, K., Tumas, W., and Phillips, C. (2018). An open experimental database for exploring inorganic materials. Sci. Data 5, 180053. 4. Correa-Baena, J.-P., Hippalgaonkar, K., van Duren, J., Jaffer, S., Chandrasekhar, V.R., Stevanovic, V., Wadia, C., Guha, S., and Buonassisi, T. (2018). Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing. Joule 2, 1410–1420. 5. Woods-Robinson, R., Han, Y., Mangum, J.S., Melamed, C.L., Gorman, B.P., Mehta, A., Persson, K.A., and Zakutayev, A. (2019). Combinatorial Tuning of Structural and Optoelectronic Properties in CuxZn1 xS. Matter 1, this issue, 862–880. 6. Butler, K.T., Davies, D.W., Cartwright, H., Isayev, O., and Walsh, A. (2018). Machine learning for molecular and materials science. Nature 559, 547–555. 7. Gomes, C.P., Selman, B., and Gregoire, J.M. (2019). Artificial intelligence for materials discovery. MRS Bull. 44, 538–544.
Matter 1, 788–800, October 2, 2019
791
Unexpected Transitions Yield Interesting Science and High-Performance Materials John M. Gregoire1,* Solid-state materials are among the most promising enablers of future technologies and pose substantial challenges for accelerating scientific discovery. Combinatorial methods excel at finding surprising transitions in materials properties, and deeper materials understanding often follows. The technological promise and the discovery challenges of solid-state materials both arise from the incredible range of properties they exhibit. Computational screening, machine learning for property prediction, and autonomous (closed-loop) experimentation are among the strategies developed over the past couple of decades to accelerate the identification of materials with specific properties. These techniques complement and are sometimes combined with combinatorial materials science methods, which can be traced back to original concepts from the 1970s1 with more substantial development and deployment since the 1990s.2 Continued sophistication of this subfield of materials science has resulted from not only the expansion of combinatorial synthesis and characterization techniques, which enable deployment of combinatorial workflows as illustrated in Figure 1, but also the increased incorporation of automation, theory, and data science.4 In this issue of Matter, Woods-Robinson et al.5 exemplify the state of the art in combinatorial methods for fundamental understanding of functional materials with their incorporation of theory and a wide variety of metrologies to reveal the underpinnings of the observed trends in materials performance (transparency and conductance, in this case).
790
Combining materials with disparate properties can enable unique properties, often due to the elements crystallizing into different structures and/or alloying to create variants of prototype structures, providing compositional tuning of properties such as electronic structure and defect concentration. In addition to the intriguing materials science that occurs with materials whose compositions differ from the small integer ratios of prototype structures, the tuning of fundamental properties can also be leveraged to design application-tailored materials, as discussed in this work for the development of a p-type transparent conductor for photovoltaics. In this context, semiconductor doping is a primary phenomenon of interest, and traditional experimental investigation would involve addition of the dopant element at one or a few compositions in the sub1 atom% range. The use of continuous composition spreads enables not only parallel synthesis of a broad range of compositions—in this case the full range of Zn-Cu compositions synthesized under a quasi-static chemical potential of S—but also observation of functional relationships between composition and properties such as band gap and conductivity. Combined with structural characterization along the composition gradient, composition-structure-property relationships,
Matter 1, 788–800, October 2, 2019 ª 2019 Published by Elsevier Inc.
which facilitate interpretation of the underlying materials science, are readily established. A key discovery in this work is that strain-induced stabilization of the wurtzite structure of ZnS via alloying of Cu increases hole transport, optimizing performance as a transparent conductor and exhibiting a combination of large band gap and high conductivity observed in only a handful of other known materials. This discovery, as well as the observed property trends along the one-dimensional composition gradient, are emblematic of broader themes in scientific discovery. First, there is a general notion across scientific disciplines that interesting science occurs at the boundaries or transitions, which is highlighted in this work by the optimized performance at an unexpected phase boundary between the two different Cu-alloyed ZnS structures; the importance of continuous compositional variation in identifying such boundaries is notable. Second, high variability of properties within a small, local parameter space illustrates the difficulty of obtaining models that are accurate across the global parameter space. Grand visions have emerged for revolutionizing materials discovery via machine learning6 and broader applications of artificial intelligence,7 and a commonly employed machine-learning strategy involves training of a model that takes a composition or compound as input and predicts properties, which accelerates screening of candidate materials. Available training data are typically sparse in the many-dimensional materials parameter space, limiting the ability to learn rapid changes in
1Division
of Engineering and Applied Science and Joint Center for Artificial Photosynthesis, California Institute of Technology, Pasadena, CA, USA *Correspondence: [email protected] https://doi.org/10.1016/j.matt.2019.09.006
holds great promise for enabling a host of future technologies.
ACKNOWLEDGMENTS
Figure 1. Workflows for Accelerating Materials Discovery and Understanding where Designed Materials Are Rapidly Explored with Combinatorial Materials Science Techniques to Identify Technology-Enabling Materials Reproduced from Zakutayev et al. 3 under Creative Commons Attribution 4.0 International License.
properties over small intervals of the parameter space. Consider the anecdotal evidence from the present work that conductivity spans five orders of magnitude by varying a single compositional degree of freedom with the same three elements. What training data would be required to predict this enormous dynamic range in properties over such a small variation, and are there methods for collecting those data that are more efficient than the continuous composition spread strategy of the present work? While the community continues to build a framework for addressing these important questions, the integration of combinatorial materials science and computational materials science will continue to thrive as a prolific strategy for discovery and understanding of complex functional materials. The promise of combinatorial techniques illustrated by this work is countered by its demonstration of remaining challenges. The role of grain size versus crystal structure in optimizing
performance is not differentiated in the present work, as both properties change across the Cu-alloyed ZnS zincblende-wurzite phase boundary. Such convolutions of materials properties highlight a persistent disconnect between the desired and available axes for combinatorial exploration. Independent variation of materials properties such as composition, grain size, and crystal orientation is not possible due to the convolution of these properties with respect to experiment controls such as annealing temperature and position along a compositionally graded thin film. Computational methods play a key role in bridging these gaps by systematically varying properties of model structures and calculating the resulting changes in fundamental properties. Given the ample space for improvement for both experimental and computational methods, and especially their integration into materials discovery workflows, the community’s concerted effort to continually accelerate materials discovery and understanding
J.M.G. acknowledges support from the Joint Center for Artificial Photosynthesis, a DOE Energy Innovation Hub, supported through the Office of Science of the US Department of Energy under award number DE-SC0004993. 1. Hanak, J.J. (1970). The ‘‘multiple-sample concept’’ in materials research: Synthesis, compositional analysis and testing of entire multicomponent systems. J. Mater. Sci. 5, 964–971. 2. Green, M.L., Choi, C.L., Hattrick-Simpers, J.R., Joshi, A.M., Takeuchi, I., Barron, S.C., Campo, E., Chiang, T., Empedocles, S., Gregoire, J.M., et al. (2017). Fulfilling the promise of the materials genome initiative with highthroughput experimental methodologies. Appl. Phys. Rev. 4, 011105. 3. Zakutayev, A., Wunder, N., Schwarting, M., Perkins, J.D., White, R., Munch, K., Tumas, W., and Phillips, C. (2018). An open experimental database for exploring inorganic materials. Sci. Data 5, 180053. 4. Correa-Baena, J.-P., Hippalgaonkar, K., van Duren, J., Jaffer, S., Chandrasekhar, V.R., Stevanovic, V., Wadia, C., Guha, S., and Buonassisi, T. (2018). Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing. Joule 2, 1410–1420. 5. Woods-Robinson, R., Han, Y., Mangum, J.S., Melamed, C.L., Gorman, B.P., Mehta, A., Persson, K.A., and Zakutayev, A. (2019). Combinatorial Tuning of Structural and Optoelectronic Properties in CuxZn1 xS. Matter 1, this issue, 862–880. 6. Butler, K.T., Davies, D.W., Cartwright, H., Isayev, O., and Walsh, A. (2018). Machine learning for molecular and materials science. Nature 559, 547–555. 7. Gomes, C.P., Selman, B., and Gregoire, J.M. (2019). Artificial intelligence for materials discovery. MRS Bull. 44, 538–544.
Matter 1, 788–800, October 2, 2019
791