DataVXJournal of Materials SciencePaper
journal article·2024-02

Advances in materials informatics: a review

Dawn Sivan, K. Satheesh Kumar, Aziman Abdullah, Veena Raj, Izan Izwan Misnon, Seeram Ramakrishna, Rajan Jose

Journal of Materials Science· Volume 59· Issue 7
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OpenAlex topics: Machine Learning in Materials Science · Computational Drug Discovery Methods · X-ray Diffraction in Crystallography

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References (100+)

Show source-provided references
1. Rajan K (2005) Materials informatics. Mater Today 8(10):38–45. https://doi.org/10.1016/S1369-7021(05)71123-810.1016/S1369-7021(05)71123-8 ↗
2. Jose R, Ramakrishna S (2018) Materials 4.0: materials big data enabled materials discovery. In: Applied Materials Today vol. 10, ed10.1016/j.apmt.2017.12.015 ↗
3. Ramakrishna S et al (2019) Materials informatics. J Intell Manuf 30(6):2307–2326. https://doi.org/10.1007/s10845-018-1392-010.1007/s10845-018-1392-0 ↗
4. Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C (2017) Machine learning in materials informatics: recent applications and prospects. npj Comput Mater 3(1):54–54. https://doi.org/10.1038/s41524-017-0056-510.1038/s41524-017-0056-5 ↗
5. Rickman JM, Lookman T, Kalinin SV (2019) Materials informatics: from the atomic-level to the continuum. Acta Materialia vol. 16810.1016/j.actamat.2019.01.051 ↗
6. Materials Genome Initiative for Global Competitiveness (2011) [Online]. Available: www.ostp.gov/nstc
7. Sukumar N, Krein M, Luo Q, Breneman C (2012) MQSPR modeling in materials informatics: a way to shorten design cycles? J Mater Sci 47(21):7703–7715. https://doi.org/10.1007/s10853-012-6639-010.1007/s10853-012-6639-0 ↗
8. Adedeji O, Wang Z (2019) Intelligent waste classification system using deep learning convolutional neural network. Procedia Manuf 35:607–612. https://doi.org/10.1016/j.promfg.2019.05.08610.1016/j.promfg.2019.05.086 ↗
9. Aggarwal V, Gupta V, Gupta S, Sharma N, Sharma K, Sharma N (2021) Using transfer learning and pattern recognition to implement a smart waste management system. In: 2021 Second international conference on electronics and sustainable communication systems (ICESC), IEEE, pp 1887–1891, https://doi.org/10.1109/ICESC51422.2021.953273210.1109/ICESC51422.2021.9532732 ↗
10. Ahmed S, Mubarak S, Du JT, Wibowo S (2022) Forecasting the status of municipal waste in smart bins using deep learning. Int J Environ Res Public Health 19(24):16798–16798. https://doi.org/10.3390/ijerph19241679810.3390/ijerph192416798 ↗
11. Alzubi Y, Alqawasmeh H, Al-Kharabsheh B, Abed D (2022) Applications of nearest neighbor search algorithm toward efficient rubber-based solid waste management in concrete. Civ Eng J 8(4):695–709. https://doi.org/10.28991/CEJ-2022-08-04-0610.28991/CEJ-2022-08-04-06 ↗
12. Belsare KS, Singh M (2022) Varıous frameworks for IoT-enabled intelligent waste management system using ML for smart cities, pp 797–81710.1007/978-981-19-2069-1_55 ↗
13. Carrera B, Piñol VL, Mata JB, Kim K (2022) A machine learning based classification models for plastic recycling using different wavelength range spectrums. J Clean Prod 374:133883–133883. https://doi.org/10.1016/j.jclepro.2022.13388310.1016/j.jclepro.2022.133883 ↗
14. Ezzahra Yatim F, Boumanchar I, Srhir B, Chhiti Y, Jama C, Ezzahrae M’hamdi Alaoui F (2022) Waste-to-energy as a tool of circular economy: prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR). Waste Manag 153:293–303. https://doi.org/10.1016/j.wasman.2022.09.01310.1016/j.wasman.2022.09.013 ↗
15. Ihsanullah I, Alam G, Jamal A, Shaik F (2022) Recent advances in applications of artificial intelligence in solid waste management: a review. Chemosphere 309:136631–136631. https://doi.org/10.1016/j.chemosphere.2022.13663110.1016/j.chemosphere.2022.136631 ↗
16. Lahcen G, Mohamed E, Mohammed G, Hanaa H, Abdelmoula A (2022) Waste solid management using machine learning approach. In: 2022 8th International conference on optimization and applications (ICOA), IEEE, pp 1–5, https://doi.org/10.1109/ICOA55659.2022.993435610.1109/ICOA55659.2022.9934356 ↗
17. Li H, Jin Z, Krishnamoorthy S (2021) E-waste management using machine learning. In: 2021 6th International conference on big data and computing, New York, NY, USA. ACM, pp 30–35, https://doi.org/10.1145/3469968.346997310.1145/3469968.3469973 ↗
18. Lin K et al (2022) Applying a deep residual network coupling with transfer learning for recyclable waste sorting. Environ Sci Pollut Res 29(60):91081–91095. https://doi.org/10.1007/s11356-022-22167-w10.1007/s11356-022-22167-w ↗
19. Arunkumar MS, Sathishkumar P, Suguna R, Deepa S (2022) An Internet of Things based waste management system using hybrid machine learning technique. In: 2022 6th International conference on electronics, communication and aerospace technology. IEEE, pp 438–443, https://doi.org/10.1109/ICECA55336.2022.1000924210.1109/ICECA55336.2022.10009242 ↗
20. Namoun A, Hussein BR, Tufail A, Alrehaili A, Syed TA, BenRhouma O (2022) An ensemble learning based classification approach for the prediction of household solid waste generation. Sensors 22(9):3506–3506. https://doi.org/10.3390/s2209350610.3390/s22093506 ↗
21. Neelakandan S et al (2022) Metaheuristics with deep transfer learning enabled detection and classification model for industrial waste management. Chemosphere 308:136046–136046. https://doi.org/10.1016/j.chemosphere.2022.13604610.1016/j.chemosphere.2022.136046 ↗
22. Sharma N, Litoriya R, Sharma A (2021) Application and analysis of K-means algorithms on a decision support framework for municipal solid waste management, pp 267–27610.1007/978-981-15-3383-9_24 ↗
23. Taki M, Rohani A (2022) Machine learning models for prediction the higher heating value (HHV) of municipal solid waste (MSW) for waste-to-energy evaluation. In: Case Studies in Thermal Engineering, vol. 31, pp. 101823–101823. https://doi.org/10.1016/j.csite.2022.10182310.1016/j.csite.2022.101823 ↗
24. Valencia Diaz MA, et al (2022) Machine learning techniques to the prediction of variables of the urban solid waste collection process. In: 2022 IEEE Colombian conference on applications of computational intelligence (ColCACI). IEEE, pp 1–6, https://doi.org/10.1109/ColCACI56938.2022.990536310.1109/ColCACI56938.2022.9905363 ↗
25. Vu HL, Ng KTW, Richter A, Li J, Hosseinipooya SA (2022) Impacts of nested forward validation techniques on machine learning and regression waste disposal time series models. Ecol Inform 72:101897–101897. https://doi.org/10.1016/j.ecoinf.2022.10189710.1016/j.ecoinf.2022.101897 ↗
26. Eleftheroglou N et al (2019) Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the lithium-polymer batteries of unmanned aerial vehicles with uncertainty quantification. Appl Energy. https://doi.org/10.1016/j.apenergy.2019.11367710.1016/j.apenergy.2019.113677 ↗
27. Hou P, Jolliet O, Zhu J, Xu M (2020) Estimate ecotoxicity characterization factors for chemicals in life cycle assessment using machine learning models. Environ Int. https://doi.org/10.1016/j.envint.2019.10539310.1016/j.envint.2019.105393 ↗
28. Mulholland GJ, Paradiso SP (2016) Perspective: materials informatics across the product lifecycle: selection, manufacturing, and certification. APL Mater. https://doi.org/10.1063/1.494542210.1063/1.4945422 ↗
29. Pregowska A, Osial M, Urbańska W (2022) The application of artificial intelligence in the effective battery life cycle in the closed circular economy model: a perspective. Recycling 7(6):81–81. https://doi.org/10.3390/recycling706008110.3390/recycling7060081 ↗
30. Roman D, Saxena S, Bruns J, Valentin R, Pecht M, Flynn D (2021) A machine learning degradation model for electrochemical capacitors operated at high temperature. IEEE Access. https://doi.org/10.1109/ACCESS.2021.305795910.1109/ACCESS.2021.3057959 ↗
31. Anand DV, Xu Q, Wee J, Xia K, Sum TC (2022) Topological feature engineering for machine learning based halide perovskite materials design. npj Comput Mater 8(1):203–203. https://doi.org/10.1038/s41524-022-00883-810.1038/s41524-022-00883-8 ↗
32. Fukada K, Seyama M (2022) Designing a multilayer film via machine learning of scientific literature. Sci Rep 12(1):930–930. https://doi.org/10.1038/s41598-022-05010-710.1038/s41598-022-05010-7 ↗
33. Liu Z et al (2022) Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing. Joule 6(4):834–849. https://doi.org/10.1016/j.joule.2022.03.00310.1016/j.joule.2022.03.003 ↗
34. Nakayama M et al (2022) Na superionic conductor-type LiZr2 (PO4)3 as a promising solid electrolyte for use in all-solid-state Li metal batteries. Chem Commun 58(67):9328–9340. https://doi.org/10.1039/D2CC01526A10.1039/D2CC01526A ↗
35. Novikov I, Kovalyova O, Shapeev A, Hodapp M (2022) AI-accelerated materials informatics method for the discovery of ductile alloys. J Mater Res 37(21):3491–3504. https://doi.org/10.1557/s43578-022-00783-z10.1557/s43578-022-00783-z ↗
36. Shi M, Feng C-P, Li J, Guo S-Y (2022) Machine learning to optimize nanocomposite materials for electromagnetic interference shielding. Compos Sci Technol 223:109414–109414. https://doi.org/10.1016/j.compscitech.2022.10941410.1016/j.compscitech.2022.109414 ↗
37. Al Duhayyim M et al (2022) Smart water quality prediction using atom search optimization with fuzzy deep convolutional network. Sustainability 14(24):16465–16465. https://doi.org/10.3390/su14241646510.3390/su142416465 ↗
38. Diwania S, Kumar M, Kumar R, Kumar A, Gupta V, Khetrapal P (2022) Machine learning-based thermo-electrical performance improvement of nanofluid-cooled photovoltaic–thermal system. Energy Environ, pp 0958305X2211469–0958305X2211469. https://doi.org/10.1177/0958305X22114694710.1177/0958305X221146947 ↗
39. Doan TO et al (2022) Evaluation and machine learning-based prediction of Zn2+ treatment by the cyanobacterium biomaterial at packed columns. Environ Technol Innov 28:102948–102948. https://doi.org/10.1016/j.eti.2022.10294810.1016/j.eti.2022.102948 ↗
40. Dongale TD et al (2022) Machine learning-assisted design guidelines and performance prediction of CMOS-compatible metal oxide-based resistive switching memory devices. Appl Mater Today 29:101650–101650. https://doi.org/10.1016/j.apmt.2022.10165010.1016/j.apmt.2022.101650 ↗
41. Iwama R, Kaneko H (2022) Integration of materials and process informatics: metal oxide and process design for CO2 reduction. ACS Omega 7(50):46922–46934. https://doi.org/10.1021/acsomega.2c0600810.1021/acsomega.2c06008 ↗
42. Janairo JIB (2021) Machine learning for the cleaner production of antioxidant peptides. Int J Peptide Res Therap 27(3):2051–2056. https://doi.org/10.1007/s10989-021-10232-w10.1007/s10989-021-10232-w ↗
43. Jun H (2022) Cost-effective mechanism for environmental toxic reduction using deep learning. Sustain Energy Technol Assess 52:102206–102206. https://doi.org/10.1016/j.seta.2022.10220610.1016/j.seta.2022.102206 ↗
44. Kumar R et al (2021) A deep neural network-based approach for prediction of mutagenicity of compounds. Environ Sci Pollut Res 28(34):47641–47650. https://doi.org/10.1007/s11356-021-14028-910.1007/s11356-021-14028-9 ↗
45. Liu G, Ayello F, Vera J, Eckert R, Bhat P (2021) An exploration on the machine learning approaches to determine the erosion rates for liquid hydrocarbon transmission pipelines towards safer and cleaner transportations. J Clean Prod 295:126478–126478. https://doi.org/10.1016/j.jclepro.2021.12647810.1016/j.jclepro.2021.126478 ↗
46. Qureshi KN, Alhudhaif A, Jeon G (2021) Electric-vehicle energy management and charging scheduling system in sustainable cities and society. Sustain Cities Soc 71:102990–102990. https://doi.org/10.1016/j.scs.2021.10299010.1016/j.scs.2021.102990 ↗
47. Senanayake NM, Carter JLW, Bowman CL, Ellis DL, Stuckner J (2022) A data-driven framework to select a cost-efficient subset of parameters to qualify sourced materials. Integr Mater Manuf Innov 11(3):339–351. https://doi.org/10.1007/s40192-022-00266-310.1007/s40192-022-00266-3 ↗
48. Xia W et al (2022) Materials informatics-guided superior electrocatalyst: a case of pyrolysis-free single-atom coordinated with N-graphene nanomesh. Nano Energy. https://doi.org/10.1016/j.nanoen.2021.10686810.1016/j.nanoen.2021.106868 ↗
49. Adamu H, Abba SI, Anyin PB, Sani Y, Yamani ZH, Qamar M (2022) Tuning OER electrocatalysts toward LOM pathway through the lens of multi-descriptor feature selection by artificial intelligence-based approach. ACS Mater Lett 5(2):299–320. https://doi.org/10.1021/acsmaterialslett.2c0073410.1021/acsmaterialslett.2c00734 ↗
50. Xu B et al (2022) Design of Ni-based turbine disc superalloys with improved yield strength using machine learning. J Mater Sci 57(22):10379–10394. https://doi.org/10.1007/s10853-022-07295-510.1007/s10853-022-07295-5 ↗
51. Völker C, Firdous R, Stephan D, Kruschwitz S (2021) Sequential learning to accelerate discovery of alkali-activated binders. J Mater Sci 56(28):15859–15881. https://doi.org/10.1007/s10853-021-06324-z10.1007/s10853-021-06324-z ↗
52. Cao W, Li T, Liang C, Wang C (2022) Design of high energy-storage properties in eco-friendly AgNbO3-based ceramics via two-step sintering method and tuning phase boundary. J Mater Sci 57(45):21000–21008. https://doi.org/10.1007/s10853-022-07964-510.1007/s10853-022-07964-5 ↗
53. Kern J, Chen L, Kim C, Ramprasad R (2021) Design of polymers for energy storage capacitors using machine learning and evolutionary algorithms. J Mater Sci 56(35):19623–19635. https://doi.org/10.1007/s10853-021-06520-x10.1007/s10853-021-06520-x ↗
54. Kankanamge UMHU, Reiner J, Ma X, Gallo SC, Xu W (2022) Machine learning guided alloy design of high-temperature NiTiHf shape memory alloys. J Mater Sci 57(41):19447–19465. https://doi.org/10.1007/s10853-022-07793-610.1007/s10853-022-07793-6 ↗
55. Al-Zyoud I, Laamarti F, Ma X, Tobón D, El Saddik A (2022) Towards a machine learning-based digital twin for non-invasive human bio-signal fusion. Sensors 22(24):9747–9747. https://doi.org/10.3390/s2224974710.3390/s22249747 ↗
56. Cao C et al (2018) Deep learning and its applications in biomedicine. Genomics Proteomics Bioinform 16(1):17–32. https://doi.org/10.1016/j.gpb.2017.07.00310.1016/j.gpb.2017.07.003 ↗
57. Chen X, Cheng G, Wang FL, Tao X, Xie H, Xu L (2022) Machine and cognitive intelligence for human health: systematic review. Brain Inform 9(1):5–5. https://doi.org/10.1186/s40708-022-00153-910.1186/s40708-022-00153-9 ↗
58. Raghupathi W, Raghupathi V (2014) Big data analytics in healthcare: promise and potential. In: Health Information Science and Systems, vol 2, ed10.1186/2047-2501-2-3 ↗
59. Rokni SA, Nourollahi M, Alinia P, Mirzadeh I, Pedram M, Ghasemzadeh H (2021) TransNet. ACM Trans Des Autom Electron Syst 26(1):1–31. https://doi.org/10.1145/341406210.1145/3414062 ↗
60. Suzuki H et al (2022) How AI application in pharmaceutical industries is beneficial to materials science. J Phys D: Appl Phys. https://doi.org/10.1088/1361-6463/ac3a4810.1088/1361-6463/ac3a48 ↗
61. Ozdemir HC, Bedir E, Yilmaz R, Yagci MB, Canadinc D (2022) Machine learning-assisted design of biomedical high entropy alloys with low elastic modulus for orthopedic implants. J Mater Sci 57(24):11151–11169. https://doi.org/10.1007/s10853-022-07363-w10.1007/s10853-022-07363-w ↗
62. Charitidis C, Sebastiani M, Goldbeck G (2022) Fostering research and innovation in materials manufacturing for Industry 5.0: the key role of domain intertwining between materials characterization, modelling and data science. Mater Des 223:555. https://doi.org/10.1016/j.matdes.2022.11122910.1016/j.matdes.2022.111229 ↗
63. Matsuda S, Lambard G, Sodeyama K (2022) Data-driven automated robotic experiments accelerate discovery of multi-component electrolyte for rechargeable Li–O2 batteries. Cell Rep Phys Sci. https://doi.org/10.1016/j.xcrp.2022.10083210.1016/j.xcrp.2022.100832 ↗
64. Takahashi K, Takahashi L (2023) Toward the golden age of materials informatics: perspective and opportunities. J Phys Chem Lett 14(20):4726–4733. https://doi.org/10.1021/acs.jpclett.3c0064810.1021/acs.jpclett.3c00648 ↗
65. Pilania G, Liu XY (2018) Machine learning properties of binary wurtzite superlattices. J Mater Sci 53(9):6652–6664. https://doi.org/10.1007/s10853-018-1987-z10.1007/s10853-018-1987-z ↗
66. Mannodi-Kanakkithodi A, Chan MKY (2022) Accelerated screening of functional atomic impurities in halide perovskites using high-throughput computations and machine learning. J Mater Sci 57(23):10736–10754. https://doi.org/10.1007/s10853-022-06998-z10.1007/s10853-022-06998-z ↗
67. Meier S, Albuquerque RQ, Demleitner M, Ruckdäschel H (2022) Modeling glass transition temperatures of epoxy systems: a machine learning study. J Mater Sci 57(29):13991–14002. https://doi.org/10.1007/s10853-022-07372-910.1007/s10853-022-07372-9 ↗
68. Kirchdoerfer T, Ortiz M (2016) Data-driven computational mechanics. Comput Methods Appl Mech Eng 304:81–101. https://doi.org/10.1016/j.cma.2016.02.00110.1016/j.cma.2016.02.001 ↗
69. Chen C, Ong SP (2021) AtomSets as a hierarchical transfer learning framework for small and large materials datasets. npj Comput Mater. https://doi.org/10.1038/s41524-021-00639-w10.1038/s41524-021-00639-w ↗
70. Gupta V et al (2021) Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data. Nat Commun 12(1):6595. https://doi.org/10.1038/s41467-021-26921-510.1038/s41467-021-26921-5 ↗
71. Zhang Y, Ling C (2018) A strategy to apply machine learning to small datasets in materials science. npj Comput Mater. https://doi.org/10.1038/s41524-018-0081-z10.1038/s41524-018-0081-z ↗
72. Yosipof A, Khalemsky A, Gelbard R, Senderowitz H (2022) Dynamic classification for materials-informatics: mining the solar cell space. Mol Inform 41(1):e2000173. https://doi.org/10.1002/minf.20200017310.1002/minf.202000173 ↗
73. Ohno H (2021) Training data augmentation using generative models with statistical guarantees for materials informatics. Soft Comput 26(3):1181–1196. https://doi.org/10.1007/s00500-021-06533-310.1007/s00500-021-06533-3 ↗
74. Bergerhoff G, Hundt R, Sievers R, Brown ID (1983) The inorganic crystal structure data base. J Chem Inf Comput Sci. https://doi.org/10.1021/ci00038a00310.1021/ci00038a003 ↗
75. Curtarolo S et al (2012) AFLOWLIB.ORG: a distributed materials properties repository from high-throughput ab initio calculations. Comput Mater Sci. https://doi.org/10.1016/j.commatsci.2012.02.00210.1016/j.commatsci.2012.02.002 ↗
76. Jain A et al (2013) Commentary: the materials project: a materials genome approach to accelerating materials innovation. In: APL Materials vol. 1, ed: American Institute of Physics Inc10.1063/1.4812323 ↗
77. Saal JE, Kirklin S, Aykol M, Meredig B, Wolverton C (2013) Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD). JOM. https://doi.org/10.1007/s11837-013-0755-410.1007/s11837-013-0755-4 ↗
78. Draxl C, Scheffler M (2018) NOMAD: the FAIR concept for big data-driven materials science. MRS Bull. https://doi.org/10.1557/mrs.2018.20810.1557/mrs.2018.208 ↗
79. Hu J et al (2022) MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art. npj Comput Mater 8(1):65–65. https://doi.org/10.1038/s41524-022-00750-610.1038/s41524-022-00750-6 ↗
80. Mooser E, Pearson WB (1959) On the crystal chemistry of normal valence compounds. Acta Crystallogr A. https://doi.org/10.1107/s0365110x5900285710.1107/s0365110x59002857 ↗
81. Pettifor DG (1984) A chemical scale for crystal-structure maps. Solid State Commun. https://doi.org/10.1016/0038-1098(84)90765-810.1016/0038-1098(84)90765-8 ↗
82. Saad Y, Gao D, Ngo T, Bobbitt S, Chelikowsky JR, Andreoni W (2012) Data mining for materials: computational experiments with AB compounds. Phys Rev B - Condens Matter Mater Phys. https://doi.org/10.1103/PhysRevB.85.10410410.1103/PhysRevB.85.104104 ↗
83. Kojima T, Washio T, Hara S, Koishi M (2022) Search strategy for rare microstructure to optimize material properties of filled rubber using machine learning based simulation. Comput Mater Sci. https://doi.org/10.1016/j.commatsci.2022.11120710.1016/j.commatsci.2022.111207 ↗
84. Pilania G, Liu X-Y, Wang Z (2019) Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators. J Mater Sci 54(11):8361–8380. https://doi.org/10.1007/s10853-019-03434-710.1007/s10853-019-03434-7 ↗
85. Deringer VL, Caro MA, Csanyi G (2019) Machine learning interatomic potentials as emerging tools for materials science. Adv Mater 31(46):e1902765. https://doi.org/10.1002/adma.20190276510.1002/adma.201902765 ↗
86. Goryaeva AM, Maillet J-B, Marinica M-C (2019) Towards better efficiency of interatomic linear machine learning potentials. Comput Mater Sci 166:200–209. https://doi.org/10.1016/j.commatsci.2019.04.04310.1016/j.commatsci.2019.04.043 ↗
87. Kokiopoulou E, Saad Y (2007) Orthogonal neighborhood preserving projections: a projection-based dimensionality reduction technique. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2007.113110.1109/TPAMI.2007.1131 ↗
88. Griffiths PR, Harris TAL (2021) Machine learning workflow for microparticle composite thin-film process–structure linkages. J Coat Technol Res 19(1):83–96. https://doi.org/10.1007/s11998-021-00512-x10.1007/s11998-021-00512-x ↗
89. Mäkinen T et al (2022) Detection of the onset of yielding and creep failure from digital image correlation. Phys Rev Mater. https://doi.org/10.1103/PhysRevMaterials.6.10360110.1103/PhysRevMaterials.6.103601 ↗
90. Ghiringhelli LM, Vybiral J, Levchenko SV, Draxl C, Scheffler M (2015) Big data of materials science: critical role of the descriptor. Phys Rev Lett. https://doi.org/10.1103/PhysRevLett.114.10550310.1103/PhysRevLett.114.105503 ↗
91. Goldsmith BR, Boley M, Vreeken J, Scheffler M, Ghiringhelli LM (2017) Uncovering structure-property relationships of materials by subgroup discovery. N J Phys 19(1):555. https://doi.org/10.1088/1367-2630/aa57c210.1088/1367-2630/aa57c2 ↗
92. Meredig B, Wolverton C (2014) Dissolving the periodic table in cubic zirconia: Data mining to discover chemical trends. Chem Mater. https://doi.org/10.1021/cm403727z10.1021/cm403727z ↗
93. Ouyang R, Curtarolo S, Ahmetcik E, Scheffler M, Ghiringhelli LM (2018) SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys Rev Mater. https://doi.org/10.1103/PhysRevMaterials.2.08380210.1103/PhysRevMaterials.2.083802 ↗
94. Ouyang R, Ahmetcik E, Carbogno C, Scheffler M, Ghiringhelli LM (2019) Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO. J Phys Materials. https://doi.org/10.1088/2515-7639/ab077b10.1088/2515-7639/ab077b ↗
95. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc: Ser B (Methodological). https://doi.org/10.1111/j.2517-6161.1996.tb02080.x10.1111/j.2517-6161.1996.tb02080.x ↗
96. Xu Y, Qian Q (2022) i-SISSO: mutual information-based improved sure independent screening and sparsifying operator algorithm. Eng Appl Artif Intell 116:105442–105442. https://doi.org/10.1016/j.engappai.2022.10544210.1016/j.engappai.2022.105442 ↗
97. Deml AM, O’Hayre R, Wolverton C, Stevanović V (2016) Predicting density functional theory total energies and enthalpies of formation of metal-nonmetal compounds by linear regression. Phys Rev B. https://doi.org/10.1103/PhysRevB.93.08514210.1103/PhysRevB.93.085142 ↗
98. Meredig B et al (2014) Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys Rev B – Condens Matter Mater Phys. https://doi.org/10.1103/PhysRevB.89.09410410.1103/PhysRevB.89.094104 ↗
99. Pilania G, Gubernatis JE, Lookman T (2015) Structure classification and melting temperature prediction in octet AB solids via machine learning. Phys Rev B - Condens Matter Mater Phys. https://doi.org/10.1103/PhysRevB.91.21430210.1103/PhysRevB.91.214302 ↗
100. Ward L, Agrawal A, Choudhary A, Wolverton C (2016) A general-purpose machine learning framework for predicting properties of inorganic materials. npj Comput Mater. https://doi.org/10.1038/npjcompumats.2016.2810.1038/npjcompumats.2016.28 ↗