Ghosh K, Stuke A, Todorović M, et al. Deep learning spectroscopy: Neural networks for molecular excitation spectra. Adv Sci 2019, 6: 1801367.
Iwasaki Y, Kusne AG, Takeuchi I. Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries. npj Comput Mater 2017, 3: 4.
Xing H, Zhao BB, Wang YJ, et al. Rapid construction of Fe–Co–Ni composition-phase map by combinatorial materials chip approach. ACS Comb Sci 2018, 20: 127–131.
Oviedo F, Ren ZK, Sun SJ, et al. Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks. npj Comput Mater 2019, 5: 60.
Sun SJ, Hartono NTP, Ren ZD, et al. Accelerated development of perovskite-inspired materials via high-throughput synthesis and machine-learning diagnosis. Joule 2019, 3: 1437–1451.
He TF, Cao ZZ, Li GR, et al. High efficiently harvesting visible light and vibration energy in (1−x)AgNbO3−xLiTaO3 solid solution around antiferroelectric−ferroelectric phase boundary for dye degradation. J Adv Ceram 2022, 11: 1641–1653.
Suzuki Y, Hino H, Kotsugi M, et al. Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures. npj Comput Mater 2019, 5: 39.
Zheng C, Mathew K, Chen C, et al. Automated generation and ensemble-learned matching of X-ray absorption spectra. npj Comput Mater 2018, 4: 12.
Zheng C, Chen C, Chen YM, et al. Random forest models for accurate identification of coordination environments from X-ray absorption near-edge structure. Patterns 2020, 1: 100013.
Timoshenko J, Lu DY, Lin YW, et al. Supervised machine-learning-based determination of three-dimensional structure of metallic nanoparticles. J Phys Chem Lett 2017, 8: 5091–5098.
Miyazato I, Takahashi L, Takahashi K. Automatic oxidation threshold recognition of XAFS data using supervised machine learning. Mol Syst Des Eng 2019, 4: 1014–1018.
Torrisi SB, Carbone MR, Rohr BA, et al. Random forest machine learning models for interpretable X-ray absorption near-edge structure spectrum-property relationships. npj Comput Mater 2020, 6: 109.
Huang QC, Fan Z, Hong LQ, et al. Machine learning based distinguishing between ferroelectric and non-ferroelectric polarization–electric field hysteresis loops. Adv Theory Simul 2020, 3: 2000106.
Li JJ, Yin RW, Li JT, et al. Correlation between multi-factor phase diagrams and complex electrocaloric behaviors in PNZST antiferroelectric ceramic system. J Adv Ceram 2023, 12: 463–473.
Ueno T, Hino H, Hashimoto A, et al. Adaptive design of an X-ray magnetic circular dichroism spectroscopy experiment with Gaussian process modelling. npj Comput Mater 2018, 4: 4.
Li MX, Zhao SF, Lu Z, et al. High-temperature bulk metallic glasses developed by combinatorial methods. Nature 2019, 569: 99–103.
Hattrick-Simpers JR, Gregoire JM, Kusne AG. Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge. APL Mater 2016, 4: 053211.
Kusne AG, Gao TR, Mehta A, et al. On-the-fly machine-learning for high-throughput experiments: Search for rare-earth-free permanent magnets. Sci Rep 2014, 4: 6367.
Yoo YK, Ohnishi T, Wang G, et al. Continuous mapping of structure-property relations in Fe1−xNix metallic alloys fabricated by combinatorial synthesis. Intermetallics 2001, 9: 541–545.
Wang L. Discovering phase transitions with unsupervised learning. Phys Rev B 2016, 94: 195105.
Zhu Q, Samanta A, Li BX, et al. Predicting phase behavior of grain boundaries with evolutionary search and machine learning. Nat Commun 2018, 9: 467.
Hu CZ, Zuo YX, Chen C, et al. Genetic algorithm-guided deep learning of grain boundary diagrams: Addressing the challenge of five degrees of freedom. Mater Today 2020, 38: 49–57.
Yuan RH, Liu Z, Balachandran PV, et al. Accelerated discovery of large electrostrains in BaTiO3-based piezoelectrics using active learning. Adv Mater 2018, 30: 1702884.
Yuan RH, Tian Y, Xue DZ, et al. Accelerated search for BaTiO3-based ceramics with large energy storage at low fields using machine learning and experimental design. Adv Sci 2019, 6: 1901395.
Im J, Lee S, Ko TW, et al. Identifying Pb-free perovskites for solar cells by machine learning. npj Comput Mater 2019, 5: 37.
Balachandran PV, Kowalski B, Sehirlioglu A, et al. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning. Nat Commun 2018, 9: 1668.
Chen CT, Gu GX. Generative deep neural networks for inverse materials design using backpropagation and active learning. Adv Sci 2020, 7: 1902607.
Lu SH, Zhou QH, Guo YL, et al. Coupling a crystal graph multilayer descriptor to active learning for rapid discovery of 2D ferromagnetic semiconductors/half-metals/metals. Adv Mater 2020, 32: 2002658.
Kunkel C, Margraf JT, Chen K, et al. Active discovery of organic semiconductors. Nat Commun 2021, 12: 2422.
Wang XK, Rai N, Merchel Piovesan Pereira B, et al. Accelerated knowledge discovery from omics data by optimal experimental design. Nat Commun 2020, 11: 5026.
Duan XJ, Fang Z, Yang T, et al. Maximizing the mechanical performance of Ti3AlC2-based MAX phases with aid of machine learning. J Adv Ceram 2022, 11: 1307–1318.
Zeng QF, Gao Y, Guan K, et al. Machine learning and a computational fluid dynamic approach to estimate phase composition of chemical vapor deposition boron carbide. J Adv Ceram 2021, 10: 537–550.
Kiyohara S, Miyata T, Tsuda K, et al. Data-driven approach for the prediction and interpretation of core−electron loss spectroscopy. Sci Rep 2018, 8: 13548.
Liu JC, Osadchy M, Ashton L, et al. Deep convolutional neural networks for Raman spectrum recognition: A unified solution. Analyst 2017, 142: 4067–4074.
Pradhan DK, Kumari S, Strelcov E, et al. Reconstructing phase diagrams from local measurements via Gaussian processes: Mapping the temperature-composition space to confidence. npj Comput Mater 2018, 4: 23.
Malkiel I, Mrejen M, Nagler A, et al. Plasmonic nanostructure design and characterization via Deep Learning. Light Sci Appl 2018, 7: 60.
Ma W, Cheng F, Liu YM. Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano 2018, 12: 6326–6334.
Liu DJ, Tan YX, Khoram E, et al. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics 2018, 5: 1365–1369.
Li Y, Xu YJ, Jiang ML, et al. Self-learning perfect optical chirality via a deep neural network. Phys Rev Lett 2019, 123: 213902.
Timoshenko J, Anspoks A, Cintins A, et al. Neural network approach for characterizing structural transformations by X-ray absorption fine structure spectroscopy. Phys Rev Lett 2018, 120: 225502.
Liu WF, Ren XB. Large piezoelectric effect in Pb-free ceramics. Phys Rev Lett 2009, 103: 257602.
Kalyani AK, Senyshyn A, Ranjan R. Polymorphic phase boundaries and enhanced piezoelectric response in extended composition range in the lead free ferroelectric BaTi1−xZrxO3. J Appl Phys 2013, 114: 014102.
Kalyani AK, Brajesh K, Senyshyn A, et al. Orthorhombic-tetragonal phase coexistence and enhanced piezo-response at room temperature in Zr, Sn, and Hf modified BaTiO3. Appl Phys Lett 2014, 104: 252906.
Krishnan H, Sen A, Senyshyn A, et al. Polarization switching and high piezoelectric response in Sn-modified BaTiO3. Phys Rev B 2015, 91: 024101.
Zhao CL, Wang H, Xiong J, et al. Composition-driven phase boundary and electrical properties in (Ba0.94Ca0.06)(Ti1−xMx)O3 (M = Sn, Hf, Zr) lead-free ceramics. Dalton Trans 2016, 45: 6466–6480.
Bai WF, Chen DQ, Zhang JJ, et al. Phase transition behavior and enhanced electromechanical properties in (Ba0.85Ca0.15)(ZrxTi1−x)O3 lead-free piezoceramics. Ceram Int 2016, 42: 3598–3608.
Zhu LF, Zhang BP, Zhao XK, et al. Phase transition and high piezoelectricity in (Ba,Ca)(Ti1−xSnx)O3 lead-free ceramics. Appl Phys Lett 2013, 103: 072905.
Tian Y, Wei LL, Chao XL, et al. Phase transition behavior and large piezoelectricity near the morphotropic phase boundary of lead-free (Ba0.85Ca0.15)(Zr0.1Ti0.9)O3 ceramics. J Am Ceram Soc 2012, 96: 496–502.
Li W, Xu ZJ, Chu RQ, et al. Piezoelectric and dielectric properties of (Ba1−xCax)(Ti0.95Zr0.05)O3 lead-free ceramics. J Am Ceram Soc 2010, 93: 2942–2944.
Li W, Xu ZJ, Chu RQ, et al. Large piezoelectric coefficient in (Ba1−xCax)(Ti0.96Sn0.04)O3 lead-free ceramics. J Am Ceram Soc 2011, 94: 4131–4133.
Xue DZ, Zhou YM, Bao HX, et al. Large piezoelectric effect in Pb-free Ba(Ti,Sn)O3−x(Ba,Ca)TiO3 ceramics. Appl Phys Lett 2011, 99: 122901.
Zhu LF, Zhang BP, Zhao L, et al. High piezoelectricity of BaTiO3–CaTiO3–BaSnO3 lead-free ceramics. J Mater Chem C 2014, 2: 4764–4771.
Li W, Xu ZJ, Chu RQ, et al. Enhanced ferroelectric properties in (Ba1−xCax)(Ti0.94Sn0.06)O3 lead-free ceramics. J Eur Ceram Soc 2012, 32: 517–520.
Zhao CL, Feng YM, Wu HP, et al. Phase boundary design and high piezoelectric activity in (1−x)(Ba0.93Ca0.07) TiO3−xBa(Sn1−yHfy)O3 lead-free ceramics. J Alloy Compd 2016, 666: 372–379.
Zhu LF, Zhang BP, Zhao L, et al. Large piezoelectric effect of (Ba,Ca)TiO3–xBa(Sn,Ti)O3 lead-free ceramics. J Eur Ceram Soc 2016, 36: 1017–1024.
Wang DL, Jiang ZH, Yang B, et al. Phase diagram and enhanced piezoelectric response of lead-free BaTiO3− CaTiO3−BaHfO3 system. J Am Ceram Soc 2014, 97: 3244–3251.
Wang DL, Jiang ZH, Yang B, et al. Phase transition behavior and high piezoelectric properties in lead-free BaTiO3–CaTiO3–BaHfO3 ceramics. J Mater Sci 2014, 49: 62–69.
Huang WJ, Martin P, Zhuang HL. Machine-learning phase prediction of high-entropy alloys. Acta Mater 2019, 169: 225–236.
He JJ, Li JJ, Liu CB, et al. Machine learning identified materials descriptors for ferroelectricity. Acta Mater 2021, 209: 116815.
He JJ, Su XP, Wang CX, et al. Machine learning assisted predictions of multi-component phase diagrams and fine boundary information. Acta Mater 2022, 240: 118341.
Tian Y, Yuan RH, Xue DZ, et al. Determining multi-component phase diagrams with desired characteristics using active learning. Adv Sci 2021, 8: 2003165.