Journal of the Chinese Ceramic Society, Volume. 50, Issue 3, 863(2022)
Machine Learning Embedded with Materials Domain Knowledge
[1] [1] PARK H, JUNG K, NEZAFATI M, et al. Sodium ion diffusion in Nasicon (Na3Zr2Si2PO12) solid electrolytes: effects of excess sodium[J]. ACS Appl Mater Inter, 2016, 8(41): 27814-27824.
[2] [2] LIU Y, ZHAO T L, JU W W, et al. Materials discovery and design using machine learning[J]. J Materiomics, 2017, 3(3): 159-177.
[3] [3] SCHMIDT J, MARQUES M R G, BOTTI S, et al. Recent advances and applications of machine learning in solid-state materials science[J]. NPJ Comput Mater, 2019, 5(83): 1-36.
[4] [4] CHEN C, ZUO Y, YE W, et al. A critical review of machine learning of energy materials[J]. Adv Energy Mater, 2020, 10(8): 1903242.
[5] [5] CERIOTTI M. Unsupervised machine learning in atomistic simulations, between predictions and understanding[J]. J Chem Phys, 2019, 150(15): 150901.
[6] [6] JING L L, TIAN Y L. Self-supervised visual feature learning with deep neural networks: a survey[J]. IEEE T Pattern Anal, 2021, 43(11): 4037-4058.
[9] [9] RAJAK P, WANG B B, NOMURA K, et al. Autonomous reinforcement learning agent for stretchable kirigami design of 2D materials[J]. NPJ Comput Mater, 2021, 7(1): 102.
[10] [10] DEL VECCHIO C, FENU G, PELLEGRINO F A, et al. Support vector representation machine for superalloy investment casting optimization[J]. Appl Math Model, 2019, 72: 324-336.
[11] [11] LIU Y, WU J M, WANG Z C, et al. Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning[J]. Acta Mater, 2020, 195: 454-467.
[12] [12] TAYLOR P L, CONDUIT G. Machine learning predictions of superalloy microstructure[J]. Comp Mater Sci, 2022, 201: 110916.
[13] [13] WEN C, ZHANG Y, WANG C X, et al. Machine learning assisted design of high entropy alloys with desired property[J]. Acta Mater, 2019, 170: 109-117.
[14] [14] BATCHELOR T A A, PEDERSEN J K, WINTHER S H, et al. High-entropy alloys as a discovery platform for electrocatalysis[J]. Joule, 2019, 3(3): 834-845.
[15] [15] ZHANG Y, WEN C, WANG C X, et al. Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models[J]. Acta Mater, 2020, 185: 528-539.
[16] [16] LIU Y, WU J M, YANG G, et al. Predicting the onset temperature (Tg) of GexSe1-x glass transition: a feature selection based two-stage support vector regression method[J]. Sci Bull, 2019, 64(16): 1195-1203.
[17] [17] SENDEK A D, YANG Q, CUBUK E D, et al. Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials[J]. Energ Environ Sci, 2017, 10(2): 306-320.
[18] [18] SENDEK A D, CUBUK E D, ANTONIUK E R, et al. Machine learning-assisted discovery of solid Li-ion conducting materials[J]. Chem Mater, 2019, 31, 2: 342-352.
[19] [19] CUBUK E D, SENDEK A D, REED E J. Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data[J]. J Chem Phys, 2019, 150(21): 214701.
[20] [20] ZHANG Y, HE X F, CHEN Z Q, et al. Unsupervised discovery of solid-state lithium ion conductors[J]. Nat Commun, 2019, 10: 5260.
[21] [21] IWASAKI Y, SAWADA R, STANEV V, et al. Identification of advanced spin-driven thermoelectric materials via interpretable machine learning[J]. NPJ Comput Mater, 2019, 5: 103.
[22] [22] MIN K, CHO E. Accelerated discovery of potential ferroelectric perovskite via active learning[J]. J Mater Chem C, 2020, 8: 7866-7872.
[23] [23] MA W, LIU Y M. A data-efficient self-supervised deep learning model for design and characterization of nanophotonic structures[J]. Sci China Phys Mech, 2020, 63(8): 284212.
[24] [24] HO C T, WANG D W. Robust identification of topological phase transition by self-supervised machine learning approach[J]. New J Phys, 2021, 23(8): 083021.
[25] [25] CHEN D, ZHENG J X, WEI G W, et al. Extracting predictive representations from hundreds of millions of molecules[J]. J Phys Chem Lett, 2021, 12(44): 10793-10801.
[26] [26] MA W, CHENG F, XU Y, et al. Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi-supervised learning strategy[J]. Adv Mater, 2019, 31(35): 1901111.
[27] [27] SAHOO P, ROY I, WANG Z, et al. MultiCon: a semi-supervised approach for predicting drug function from chemical structure analysis[J]. J Chem Inf Model, 2020, 60(12): 5995-6006.
[28] [28] KUNSELMAN C, ATTARI V, MCCLENNY L, et al. Semi-supervised learning approaches to class assignment in ambiguous microstructures[J]. Acta Mater, 2020, 188: 49-62.
[29] [29] CHEN D, SUN D, FU J, et al. Semi-supervised learning framework for aluminum alloy metallographic image segmentation[J]. IEEE Access, 2021, 9: 30858-30867.
[31] [31] XUE D Z, BALACHANDRAN P V, HOGDEN J, et al. Accelerated search for materials with targeted properties by adaptive design[J]. Nat Commun, 2016, 7: 11241.
[32] [32] DOAN H A, AGARWAL G, QIAN H, et al. Quantum chemistry- informed active learning to accelerate the design and discovery of sustainable energy storage materials[J]. Chem Mater, 2020, 32: 6338-6346.
[33] [33] SAIEDIAN I, BADLOE T, LEE H, et al. Deep Q-network to produce polarization-independent perfect solar absorbers: a statistical report[J]. Nano Converg, 2020, 7(1): 26.
[34] [34] BUTLER K T, DAVIES D W, CARTWRIGHT H, et al. Machine learning for molecular and materials science[J]. Nature, 2018, 559(7715): 547-555.
[36] [36] WOHLRAB L, FURNKRANZ J. A review and comparison of strategies for handling missing values in separate-and-conquer rule learning[J]. J Intell Inf Syst, 2011, 36(1): 73-98.
[37] [37] XU X D, LIU H W, YAO M H. Recent progress of anomaly detection[J]. Complexity, 2019: 2686378.
[38] [38] GUO H X, LI Y J, SHANG J, et al. Learning from class-imbalanced data: Review of methods and applications[J]. Expert Syst Appl, 2017, 73: 220-239.
[39] [39] BERTI-QUILLE L. Measuring and modelling data quality for quality-awareness in data mining[M]. Quality Measures in Data Mining. Springer Berlin Heidelberg, 2007.
[40] [40] WANG Y D, PAN Z B, PAN Y W, et al. A training data set cleaning method by classification ability ranking for the k-nearest neighbor classifier[J]. IEEE T Neur Net Lear, 2020, 31(5): 1544-1556.
[41] [41] ROY K, KAR S, DAS R N. A primer on QSAR/QSPR modeling: fundamental concepts[M]. Springer, 2015.
[42] [42] GHIRINGHELLI L M, VYBIRAL J, LEVCHENKO S V, et al. Big data of materials science: critical role of the descriptor[J]. Phys Rev Lett, 2015, 114(10): 105503.
[43] [43] SHANDIZ M A, GAUYIN R. Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries[J]. Comp Mater Sci, 2016, 117: 270-278.
[44] [44] Li Y, ZOU C F, BERECIBAR M, et al. Random forest regression for online capacity estimation of lithium-ion batteries[J]. Appl Energ, 2018, 232: 197-210.
[45] [45] CHELGANI S C, MATIN S S, HOWER J C. Explaining relationships between coke quality index and coal properties by random forest method[J]. Fuel, 2016, 182: 754-760.
[46] [46] IM J, LEE S, KO T W, et al. Identifying Pb-free perovskites for solar cells by machine learning[J]. NPJ Comput Mater, 2019, 5: 37.
[47] [47] WANG X M, XU Y L, YANG J, et al. ThermoEPred-EL: Robust bandgap predictions of chalcogenides with diamond-like structure via feature cross-based stacked ensemble learning[J]. Comp Mater Sci, 2019, 169: 109117.
[48] [48] WEN C, WANG C X, ZHANG Y, et al. Modeling solid solution strengthening in high entropy alloys using machine learning[J]. Acta Mater, 2021, 212: 116917.
[49] [49] LIU Y, GUO B R, ZOU X X, et al. Machine learning assisted materials design and discovery for rechargeable batteries[J]. Energy Storage Mater, 2020, 31: 434-450.
[50] [50] DE JONG M, CHEN M, NOTESTINE R, et al. A statistical learning framework for materials science: application to elastic moduli of k-nary inorganic polycrystalline compounds[J]. Sci Rep, 2016, 6: 34256.
[51] [51] ZHANG Y, LING C. A strategy to apply machine learning to small datasets in materials science[J]. NPJ Comput Mater, 2019, 3(5): 71-78.
[52] [52] FABER F A, LINDMAA A, VON Lilienfeld O A, et al. Machine learning energies of 2million Elpasolite (ABC2D6) crystals[J]. Phys Rev Lett, 2016, 117(13): 135502.
[53] [53] SCHMIDT J, SHI J M, BORLIDO P, et al. Predicting the thermodynamic stability of solids combining density functional theory and machine learning[J]. Chem Mater, 2017, 29(12): 5090-5103.
[54] [54] LIU Y, WU J M, AVDEEV M, et al. Multi-layer feature selection incorporating weighted score-based expert knowledge toward modeling materials with targeted properties[J]. Adv Theor Simul, 2020, 3(2): 1900215.
[55] [55] WANG X L, XIAO R J, LI H, et al. Quantitative structure-property relationship study of cathode volume changes in lithium ion batteries using ab-initio and partial least squares analysis[J]. J Materiomics, 2017, 3(3): 178-183.
[56] [56] ZHAO Y L, ZHANG K, ZHANG Y, et al. Prediction of air voids of asphalt layers by intelligent algorithm[J]. Constr Build Mater, 2022, 317: 125908.
[57] [57] JIANG D W, WANG Z Y, ZHANG J L, et al. Predictive modelling for contact angle of liquid metals and oxide ceramics by comparing Gaussian process regression with other machine learning methods[J]. Ceram Int, 2022, 48(1): 665-673.
[58] [58] YE W K, CHEN C, WANG Z B, et al. Deep neural networks for accurate predictions of crystal stability[J]. Nat Commun, 2018, 9: 3800.
[59] [59] HU C, JAIN G, ZHANG P Q, et al. Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery[J]. Appl Energ, 2014, 129: 49-55.
[60] [60] HALEVY A, NORVIG P, PEREIRA F. The unreasonable effectiveness of data[J]. IEEE Intell Syst, 2009, 24(2): 8-12.
[62] [62] AGRAWAL A, CHOUDHARY A. Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in materials science[J]. Apl Mater, 2016, 4(5): 053208.
[63] [63] MA B Y, WEI X Y, LIU C N, et al. Data augmentation in microscopic images for material data mining[J]. NPJ Comput Mater, 2020, 6(1): 125.
[64] [64] RESHEF D N, RESHEF Y A, FINUCANE H K, et al. Detecting novel associations in large data sets[J]. Science, 2011, 334(6062): 1518-1524.
[65] [65] LUNDBERG S M, LEE S I. A unified approach to interpreting model predictions[C]//31st Annual Conference on Neural Information Processing Systems, Long Beach, CA, 2017.
[67] [67] LOOKMAN T, BALACHANDRAN P V, XUE D Z, et al. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design[J]. NPJ Comput Mater, 2019, 5: 21.
[68] [68] ZHUANG F Z, QI Z Y, DUAN K Y, et al. A comprehensive survey on transfer learning[J]. P IEEE, 2021, 109(1): 43-76.
[69] [69] XU Q C, LI Z Z, LIU M, et al. Rationalizing perovskite data for machine learning and materials design[J]. J Phys Chem Lett, 2018, 9(24): 6948-6954.
[70] [70] HAFIZ H, KHAIR A I, CHOI H, et al. A high-throughput data analysis and materials discovery tool for strongly correlated materials[J]. NPJ Comput Mater, 2018, 4: 63.
[71] [71] LI W, JACOBS R, MORGAN D. Predicting the thermodynamic stability of perovskite oxides using machine learning models[J]. Comput Mater Sci, 2018, 150: 454-463.
[72] [72] WANG A Y T, MURDOCK R J, KAUWE S K, et al. Machine learning for materials scientists: an introductory guide toward best practices[J]. Chem Mater, 2020, 32(12): 4954-4965.
[73] [73] GHIRINGHCLLI LM, CARBOGNO C, LEVCHCNKO S, et al. Towards a common format for computational material science data[J]. arXiv Materials Science: 1607.04738v1.
[74] [74] WILKINSON M D, DUMONTIER M, AALBERSBERG I J, et al. Comment: the FAIR guiding principles for scientific data management and stewardship[J]. Sci Data, 2016, 3: 160018.
[76] [76] DRAXL C, SCHEFFLER M. NOMAD: The FAIR concept for big data-driven materials science[J]. MRS Bull, 2018, 43(9): 676-682.
[77] [77] The NOMAD (Novel Materials Discovery) Center of Excellence (CoE): https://nomad-coe.eu.
[78] [78] ALLEN F H. The Cambridge Structural Database: a quarter of a million crystal structures and rising [J]. Acta Crystallogr B, 2002, 58: 380-388.
[79] [79] BERGERHOFF G, HUNDT R, SIEVERS R, et al. The inorganic crystal structure data base[J]. J Chem Info Comput Sci, 1983, 23(2): 66-69.
[80] [80] SAAL J E, KIRKLIN S, AYKOL M, et al. Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD)[J]. JOM, 2013, 65: 1501-1509.
[81] [81] VILLARS P, BERNDT M, BRANDENBURG K, et al. The Pauling File, binaries edition[J]. J Alloy Compd, 2004, 367: 293-297.
[82] [82] JAIN A, ONG S P, HAUTIER G, et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation [J]. APL Mater, 2013, 1: 011002.
[83] [83] CURTAROLO S, SETYAWAN W, WANG S, et al. AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations[J]. Comp Mater Sci, 2012, 58: 227-235.
[84] [84] SANCHEZ-LENGELING B, ASPURU-GUZIK A. Inverse molecular design using machine learning: Generative models for matter engineering[J]. Science, 2018, 361(6400): 360-365.
[85] [85] DAN Y B, ZHAO Y, LI X, et al. Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials[J]. NPJ Comput Mater, 2020, 6(1): 84.
[86] [86] XU X F, LEI Y G, LI Z D. An incorrect data detection method for big data cleaning of machinery condition monitoring[J]. IEEE T Ind Electron, 2020, 67(3): 2326-2336.
[87] [87] STORKEY A J, HAMBLY N C, WILLIAMS C K I, et al. Cleaning sky survey data bases using Hough transform and renewal string approaches[J]. Mon Not R Astron Soc, 2004, 347(1): 36-51.
[88] [88] WARD L, AGRAWAL A, CHOUDHARY A, WOLVERTON C. A general-purpose machine learning framework for predicting properties of inorganic materials[J]. NPJ Comput Mater, 2016, 2: 16028.
[89] [89] LI Y H, XIAO B, TANG Y C, et al. Center-Environment feature model for machine learning study of spinel oxides based on first-principles computations[J]. J Phys Chem C, 2020, 124(52): 28458-28468.
[90] [90] OUYANG R H, CURTAROLO S, AHMETCIK E, et al. SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates[J]. Phys Rev Mater, 2018, 2(8): 083802.
[91] [91] WANG Y, WAGNER N, RONDINELLI J M. Symbolic regression in materials science[J]. MRS Commun, 2019, 9(3): 793-805.
[92] [92] WENG B C, SONG Z L, ZHU R L, et al. Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts[J]. Nat Commun, 2020, 11(1): 3513.
[93] [93] WEN C, WANG C X, ZHANG Y, et al. Modeling solid solution strengthening in high entropy alloys using machine learning[J]. Acta Mater, 2021, 212: 116917.
[94] [94] IM J, LEE S, KO T W, et al. Identifying Pb-free perovskites for solar cells by machine learning[J]. NPJ Comput Mater, 2019, 5: 37.
[95] [95] TONG Z N, WANG L Y, ZHU G M, et al. Predicting twin nucleation in a polycrystalline Mg alloy using machine learning methods[J]. Metall Mater Trans A, 2019, 50(12): 5543-5560.
[96] [96] KHARKOV Y A, SOTSKOV V E, KARAZEEV A A, et al. Revealing quantum chaos with machine learning[J]. Phys Rev B, 2020, 101(6): 064406.
[97] [97] WANG A P, ZOU Z Y, WANG D, et al. Identifying chemical factors affecting reaction kinetics in Li-air battery via ab initio calculations and machine learning[J]. Energy Storage Mater, 2021, 35: 595-601.
[98] [98] AHMAD A, AHMAD W, ASLAM F, et al. Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques[J]. CASE Stud Constr Mat, 2022, 16: e00840.
[99] [99] SARKER S, TANG-KONG R, SCHOEPPNER R, et al. Discovering exceptionally hard and wear-resistant metallic glasses by combining machine-learning with high throughput experimentation[J]. Appl Phys Rev, 2022, 9(1): 011403.
[100] [100] ATTIA P M, GROVER A, JIN N, et al. Closed-loop optimization of fast-charging protocols for batteries with machine learning[J]. Nature, 2020, 578(7795): 397-402.
[101] [101] LAMBARD G, SASAKI T T, SODEYAMA K, et al. Optimization of direct extrusion process for Nd-Fe-B magnets using active learning assisted by machine learning and Bayesian optimization[J]. Scripta Mater, 2022, 209: 114341.
[102] [102] PRUKSAWAN S, LAMBARD G, SAMITSU S, et al. Prediction and optimization of epoxy adhesive strength from a small dataset through active learning[J]. Sci Technol Adv Mat, 2020, 20(1): 1010-1021.
[103] [103] SHEN S, SADOUGHI M, LI M, et al. Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries[J]. Appl Energ, 2020, 260: 114296.
[104] [104] TOGO R, SAITO N, OGAWA T, et al. Estimating regions of deterioration in electron microscope images of rubber materials via a transfer learning-based anomaly detection model[J]. IEEE Access, 2019, 7: 162395-162404.
[105] [105] ISAYEV O, OSES C, TOHER C, et al. Universal fragment descriptors for predicting properties of inorganic crystals[J]. Nat Commun, 2017, 8: 15679.
[106] [106] XIE T, GROSSMAN J C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties[J]. Phys Rev Lett, 2018, 120(14): 145301.
[107] [107] AHAMD Z, XIE T, MAHESHWARI C, et al. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes[J]. ACS Central Sci, 2018, 4(8): 996-1006.
[108] [108] ZHOU L M, YAO A M Z, WU Y J, et al. Machine learning assisted prediction of cathode materials for Zn-ion batteries[J]. Adv Theor Simul, 2021, 4(9): 2100196.
[109] [109] MEREDIG B, AGRAWAL A, KIRKLIN S, et al. Combinatorial screening for new materials in unconstrained composition space with machine learning[J]. Phys Rev B, 2014, 89(9): 094104.
[110] [110] KONONOVA O, HE T J, HUO H Y, et al. Opportunities and challenges of text mining in materials research[J]. ISCIENCE, 2021, 24(3): 102155.
[111] [111] OLIVETTI E A, COLE J M, KIM E, et al. Data-driven materials research enabled by natural language processing and information extraction[J]. Appl Phys Rev, 2021, 7(4): 041317.
[112] [112] TSHITOYAN V, DAGDELEN J, WESTON L, et al. Unsupervised word embeddings capture latent knowledge from materials science literature[J]. Nature, 2019, 571(7763): 95-98.
[113] [113] HU Z T, MA X Z, LIU Z Z, et al. Harnessing deep neural networks with logic rules[C]//54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, 2016: 2410-2420.
[114] [114] SONG X M, FENG F L, HAN X J. Neural compatibility modeling with attentive knowledge distillation[C]//41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Univ Michigan, Ann Arbor, MI, 2018: 5-14.
[115] [115] DAI W Z, XU Q L, YU Y, et al. Tunneling neural perception and logic reasoning through abductive Learning. arXiv: 1802.01173, 2018.
[116] [116] CHEN Y T, ZHANG D X. Physics constrained deep learning of geomechanical logs[J]. IEEE T Geosci Remote, 2020, 58(8), 5932-5943.
[117] [117] CHEN Y T, ZHANG D X. Theory guided deep-learning for load forecasting (TgDLF) via ensemble long short-term memory[J]. Adv Appl Energ, 2020, 1: 1-15.
[118] [118] CHEN Y T, HUANG D, ZHANG D X. Theory-guided hard constraint projection (HCP): a knowledge-based data-driven scientific machine learning method[J]. J Comput Phys, 2021, 445: 110624.
Get Citation
Copy Citation Text
LIU Yue, ZOU Xinxin, YANG Zhengwei, SHI Siqi. Machine Learning Embedded with Materials Domain Knowledge[J]. Journal of the Chinese Ceramic Society, 2022, 50(3): 863
Category:
Received: Jan. 30, 2022
Accepted: --
Published Online: Nov. 11, 2022
The Author Email: