Machine learning guided design of single-molecule magnets for magnetocaloric applications

Holleis, Ludwig; Shivaram, B. S.; Balachandran, Prasanna V.

doi:10.1063/1.5094553

Cited by 29Magnetism in coordination complexesMachine Learning in Materials ScienceMagnetic and transport properties of perovskites and related materials

Machine learning guided design of single-molecule magnets for magnetocaloric applications

Holleis, Ludwig, Shivaram, B. S., Balachandran, Prasanna V.·2019·Applied Physics Letters

DOI 10.1063/1.5094553

Open this paper in CoChat — free account

Abstract

We present a data-driven approach to predict entropy changes (ΔS) in small magnetic fields in single-molecule magnets (SMMs) relevant to their application as magnetocaloric refrigerants. We construct a database of SMMs with a representation scheme incorporating aspects related to dimensionality, structure, local coordination environment, ideal total spin of magnetic ions, ligand type, and linking chemistry. We train machine learning models for predicting the entropy change as a function of structure and chemistry and use the models to arrive at ΔS for hypothetical molecules. We also identify key descriptors that affect the entropy change, thus providing insights into designing tailored SMMs with improved magnetocaloric properties.

Integrity signals

Registry

Crossref · 10.1063/1.5094553

Cites

58 of 58 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

The likelihood of a TMC to exhibit spin-crossover behavior has been predicted based on the ligand nitrogen-nitrogen separation, and similar features have been invoked for predicting single-molecule magnet behavior.
“The likelihood of a TMC to exhibit spin-crossover behavior has been predicted based on the ligand nitrogen-nitrogen separation (83), and similar features have been invoked for predicting single-molecule magnet behavior (84).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [84]

R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works
Bootstrap methods: another look at the jackknife
Efron, B. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344552 · Cited by 17,602 · see citing works
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works
Confidence Limits for the Indirect Effect: Distribution of the Product and Resampling Methods
MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004) · Multivariate Behavioral Research · doi:10.1207/s15327906mbr3901_4 · Cited by 7,806 · see citing works
Lanthanide Single-Molecule Magnets
Woodruff, D. N., Winpenny, R. E. P., & Layfield, R. A. (2013) · Chemical Reviews · doi:10.1021/cr400018q · Cited by 2,791 · see citing works
THE NATURE OF THE CHEMICAL BOND. IV. THE ENERGY OF SINGLE BONDS AND THE RELATIVE ELECTRONEGATIVITY OF ATOMS
Pauling, L. C. (1932) · Journal of the American Chemical Society · doi:10.1021/ja01348a011 · Cited by 1,881 · see citing works
Lanthanides in molecular magnetism: old tools in a new field
Sorace, L., Benelli, C., & Gatteschi, D. (2011) · Chemical Society Reviews · doi:10.1039/c0cs00185f · Cited by 1,038 · see citing works
Estimation of Electronegativity Values of Elements in Different Valence States
Li, K., & Xue, D. (2006) · The Journal of Physical Chemistry A · doi:10.1021/jp062886k · Cited by 874 · see citing works
Radical ligand-containing single-molecule magnets
Demir, S., Jeon, I., Long, J. R., & Harris, T. D. (2014) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2014.10.012 · Cited by 631 · see citing works
Molecule-based magnetic coolers
Zheng, Y., Zhou, G., Zheng, Z., & Winpenny, R. E. P. (2013) · Chemical Society Reviews · doi:10.1039/c3cs60337g · Cited by 579 · see citing works
High-Nuclearity 3d–4f Clusters as Enhanced Magnetic Coolers and Molecular Magnets
Peng, J.-B., Zhang, Q.-C., Kong, X., Zheng, Y., Ren, Y.-P., Long, L., Huang, R., Zheng, L., & Zheng, Z. (2012) · Journal of the American Chemical Society · doi:10.1021/ja209752z · Cited by 469 · see citing works
Recipes for enhanced molecular cooling
Evangelisti, M., & Brechin, E. K. (2010) · Dalton Transactions · doi:10.1039/b926030g · Cited by 465 · see citing works
Understanding the Magnetic Anisotropy toward Single-Ion Magnets
Meng, Y., Jiang, S., Wang, B., & Gao, S. W. (2016) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6b00222 · Cited by 454 · see citing works
Orbital Radii of Atoms and Ions
Waber, J. T., & CROMER, D. T. (1965) · The Journal of Chemical Physics · doi:10.1063/1.1695904 · Cited by 412 · see citing works
Co–Ln Mixed-Metal Phosphonate Grids and Cages as Molecular Magnetic Refrigerants
Zheng, Y., Evangelisti, M., Tuna, F., & Winpenny, R. E. P. (2011) · Journal of the American Chemical Society · doi:10.1021/ja208367k · Cited by 381 · see citing works
Cryogenic Magnetocaloric Effect in a Ferromagnetic Molecular Dimer
Evangelisti, M., Roubeau, O., Palacios, E., Camón, A., Hooper, T. N., Brechin, E. K., & Alonso, J. J. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201102640 · Cited by 315 · see citing works
Lanthanide single molecule magnets: progress and perspective
Zhang, P., Zhang, L., & Tang, J. (2015) · Dalton Transactions · doi:10.1039/c4dt03329a · Cited by 299 · see citing works
Large Magnetocaloric Effect in a Wells–Dawson Type {Ni 6 Gd 6 P 6 } Cage
Zheng, Y., Evangelisti, M., & Winpenny, R. E. P. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201008074 · Cited by 299 · see citing works
A 24-Gd nanocapsule with a large magnetocaloric effect
Chang, L., Xiong, G., Wang, L., Cheng, P., & Zhao, B. (2012) · Chemical Communications · doi:10.1039/c2cc35800j · Cited by 284 · see citing works
Chilling with Magnetic Molecules
Sessoli, R. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201104448 · Cited by 276 · see citing works
Co–Gd phosphonate complexes as magnetic refrigerants
Zheng, Y., Evangelisti, M., & Winpenny, R. E. P. (2010) · Chemical Science · doi:10.1039/c0sc00371a · Cited by 248 · see citing works
Mixed-Valent Mn Supertetrahedra and Planar Discs as Enhanced Magnetic Coolers
Manoli, M., Collins, A., Parsons, S., Candini, A., Evangelisti, M., & Brechin, E. K. (2008) · Journal of the American Chemical Society · doi:10.1021/ja802829d · Cited by 234 · see citing works
Study of a magnetic-cooling material Gd(OH)CO 3
Chen, Y., Qin, L., Meng, Z., Yang, D., Wu, C. J., Fu, Z., Zheng, Y., Liu, J., Tarasenko, R., Orendáč, M., et al. (2014) · Journal of Materials Chemistry A · doi:10.1039/c4ta01646g · Cited by 223 · see citing works
Magnetic cluster excitations
Fürrer, A., & Waldmann, O. (2013) · Reviews of Modern Physics · doi:10.1103/revmodphys.85.367 · Cited by 188 · see citing works
Anion‐Templated Assembly and Magnetocaloric Properties of a Nanoscale {Gd38} Cage versus a {Gd48} Barrel
Guo, F., Chen, Y., Mao, L., Lin, W., Leng, J., Tarasenko, R., Orendáč, M., Prokleška, J., Sechovský, V., & Tong, M. (2013) · Chemistry - A European Journal · doi:10.1002/chem.201302093 · Cited by 182 · see citing works
1,2,3-Triazolate-Bridged Tetradecametallic Transition Metal Clusters [M14(L)6O6(OMe)18X6] (M = FeIII, CrIII and VIII/IV) and Related Compounds: Ground-State Spins Ranging from S = 0 to S = 25 and Spin-Enhanced Magnetocaloric Effect
Shaw, R., Laye, R. H., Jones, L. F., Low, D. M., Talbot-Eeckelaers, C. E., Wei, Q., Milios, C. J., Teat, S. J., Helliwell, M., Raftery, J., et al. (2007) · Inorganic Chemistry · doi:10.1021/ic070320k · Cited by 150 · see citing works
Pentametallic lanthanide-alkoxide square-based pyramids: high energy barrier for thermal relaxation in a holmium single molecule magnet
Blagg, R. J., Tuna, F., McInnes, E. J. L., & Winpenny, R. E. P. (2011) · Chemical Communications · doi:10.1039/c1cc14186d · Cited by 144 · see citing works
MnII–GdIII Phosphonate Cages with a Large Magnetocaloric Effect
Zheng, Y., Pineda, E. M., Helliwell, M., & Winpenny, R. E. P. (2012) · Chemistry - A European Journal · doi:10.1002/chem.201200152 · Cited by 140 · see citing works
Coordination compounds and the magnetocaloric effect
Sharples, J. W., & Collison, D. J. (2013) · Polyhedron · doi:10.1016/j.poly.2013.02.034 · Cited by 120 · see citing works
High spin cycles: topping the spin record for a single molecule verging on quantum criticality
Baniodeh, A., Magnani, N., Lan, Y., Buth, G., Anson, C. E., Richter, J., Affronte, M., Schnack, J., & Powell, A. K. (2018) · npj Quantum Materials · doi:10.1038/s41535-018-0082-7 · Cited by 116 · see citing works
Coordination‐Cluster‐Based Molecular Magnetic Refrigerants
Zhang, S., & Cheng, P. (2016) · The Chemical Record · doi:10.1002/tcr.201600038 · Cited by 108 · see citing works
Mixed-anion templated cage-like lanthanide clusters: Gd 27 and Dy 27
Zheng, X., Peng, J., Kong, X., Long, L., & Zheng, L. (2015) · Inorganic Chemistry Frontiers · doi:10.1039/c5qi00249d · Cited by 102 · see citing works
Fluoride‐Bridged {GdIII3MIII2} (M=Cr, Fe, Ga) Molecular Magnetic Refrigerants
Pedersen, K. S., Lorusso, G., Morales, J. J., Weyhermüller, T., Piligkos, S., Singh, S. K., Larsen, D., Schau‐Magnussen, M., Rajaraman, G., Evangelisti, M., et al. (2014) · Angewandte Chemie International Edition · doi:10.1002/anie.201308240 · Cited by 95 · see citing works
Fluoride Bridges as Structure-Directing Motifs in 3d-4f Cluster Chemistry
Birk, T., Pedersen, K. S., Thuesen, C. A., Weyhermüller, T., Schau‐Magnussen, M., Piligkos, S., Weihe, H., Mossin, S., Evangelisti, M., & Bendix, J. (2012) · Inorganic Chemistry · doi:10.1021/ic300421x · Cited by 93 · see citing works
Magnetic molecular clusters as promising materials for refrigeration in low-temperature regions
Spichkin, Y. I., Zvezdin, A. K., Gubin, S. P., Mischenko, A. S., & Tishin, A. M. (2001) · Journal of Physics D Applied Physics · doi:10.1088/0022-3727/34/8/306 · Cited by 91 · see citing works
Role of Single-Ion Anisotropy and Magnetic Exchange Interactions in Suppressing Zero-Field Tunnelling in {3d-4f} Single Molecule Magnets
Gupta, T., Beg, M. F., & Rajaraman, G. (2016) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.6b01831 · Cited by 75 · see citing works
Molybdate templated assembly of Ln 12 Mo 4 -type clusters (Ln = Sm, Eu, Gd) containing a truncated tetrahedron core
Zheng, Y. J., Zhang, Q.-C., Long, L., Huang, R., Müller, A., Schnack, J., Zheng, L., & Zheng, Z. (2012) · Chemical Communications · doi:10.1039/c2cc36530h · Cited by 72 · see citing works
Synthesis, crystal structure and study of magnetocaloric effect and single molecular magnetic behaviour in discrete lanthanide complexes
Adhikary, A., Sheikh, J. A., Biswas, S., & Konar, S. (2014) · Dalton Transactions · doi:10.1039/c4dt00540f · Cited by 62 · see citing works
Iron Lanthanide Phosphonate Clusters: {Fe6Ln6P6} Wells—Dawson-like Structures withD3dSymmetry
Pineda, E. M., Tuna, F., Zheng, Y., Teat, S. J., Winpenny, R. E. P., Schnack, J., & McInnes, E. J. L. (2014) · Inorganic Chemistry · doi:10.1021/ic402839q · Cited by 58 · see citing works
Structure–Curie temperature relationships in BaTiO3 -based ferroelectric perovskites: Anomalous behavior of (Ba,Cd)TiO3 from DFT, statistical inference, and experiments
Balachandran, P. V., Xue, D., & Lookman, T. (2016) · Physical review. B./Physical review. B · doi:10.1103/physrevb.93.144111 · Cited by 51 · see citing works
Self-citation
Magneto-Structural Properties and Theoretical Studies of a Family of Simple Heterodinuclear Phenoxide/Alkoxide Bridged MnIIILnIIIComplexes: On the Nature of the Magnetic Exchange and Magnetic Anisotropy
Hänninen, M. M., Mota, A. J., Sillanpää, R., Dey, S., Velmurugan, G., Rajaraman, G., & Colacio, E. (2018) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.7b02917 · Cited by 47 · see citing works
Magnetocaloric effect and slow magnetic relaxation in two only azido bridged ferromagnetic tetranuclear metal clusters
Zhao, J., Zhao, R., Yang, Q., Hu, B., Liu, F., & Bu, X. (2013) · Dalton Transactions · doi:10.1039/c3dt51655e · Cited by 45 · see citing works
Octanuclear [Ni II 4 Ln III 4 ] complexes. Synthesis, crystal structures and magnetocaloric properties
Pasatoiu, T. D., Ghirri, A., Mădălan, A. M., Affronte, M., & Andruh, M. (2014) · Dalton Transactions · doi:10.1039/c4dt00515e · Cited by 42 · see citing works
CO 2 as a reaction ingredient for the construction of metal cages: a carbonate-panelled [Gd 6 Cu 3 ] tridiminished icosahedron
Hooper, T. N., Inglis, R., Palacios, M. A., Nichol, G. S., Pitak, M. B., Coles, S. J., Lorusso, G., Evangelisti, M., & Brechin, E. K. (2014) · Chemical Communications · doi:10.1039/c4cc00141a · Cited by 42 · see citing works
Dodecanuclear 3d/4f-metal clusters with a ‘Star of David’ topology: single-molecule magnetism and magnetocaloric properties
Alexandropoulos, D. I., Cunha-Silva, L., Lorusso, G., Evangelisti, M., Tang, J., & Stamatatos, T. C. (2015) · Chemical Communications · doi:10.1039/c5cc09385f · Cited by 42 · see citing works
Remarkable Ln III 3 Fe III 2 clusters with magnetocaloric effect and slow magnetic relaxation
Li, H., Shi, W., Niu, Z., Zhou, J.-M., Xiong, G., Li, L.-L., & Cheng, P. (2014) · Dalton Transactions · doi:10.1039/c4dt02871f · Cited by 37 · see citing works
Gadolinium Sulfate Modified by Formate To Obtain Optimized Magneto-Caloric Effect
Xu, L., Zhao, J., Liu, T., & Liu, F. (2015) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.5b00214 · Cited by 35 · see citing works
Wells–Dawson Cages as Molecular Refrigerants
Pineda, E. M., Tuna, F., Zheng, Y., Winpenny, R. E. P., & McInnes, E. J. L. (2013) · Inorganic Chemistry · doi:10.1021/ic402296t · Cited by 32 · see citing works
Predicting displacements of octahedral cations in ferroelectric perovskites using machine learning
Balachandran, P. V., Shearman, T. L., Theiler, J. P., & Lookman, T. (2017) · Acta Crystallographica Section B Structural Science Crystal Engineering and Materials · doi:10.1107/s2052520617011945 · Cited by 32 · see citing works
Self-citation
‘All three-in-one’: ferromagnetic interactions, single-molecule magnetism and magnetocaloric properties in a new family of [Cu 4 Ln] (Ln III = Gd, Tb, Dy) clusters
Richardson, P., Alexandropoulos, D. I., Cunha-Silva, L., Lorusso, G., Evangelisti, M., Tang, J., & Stamatatos, T. C. (2015) · Inorganic Chemistry Frontiers · doi:10.1039/c5qi00146c · Cited by 28 · see citing works
Cryogenic magnetocaloric effect in the Fe17 molecular nanomagnet
Gass, I. A., Brechin, E. K., & Evangelisti, M. (2012) · Polyhedron · doi:10.1016/j.poly.2012.06.049 · Cited by 24 · see citing works
General One-Step Self-Assembly of Isostructural Intermetallic CoII3LnIII Cubane Aggregates
Wang, P. J., Shannigrahi, S. R., Yakovlev, N. L., & Hor, T. S. A. (2012) · Inorganic Chemistry · doi:10.1021/ic301527b · Cited by 24 · see citing works
A triangular [Mn 3 ] cluster-based ferrimagnet with significant magnetic entropy change
Hu, H., Cao, C., Yang, Y., Cheng, P., & Zhao, B. (2015) · Journal of Materials Chemistry C · doi:10.1039/c4tc02958e · Cited by 23 · see citing works
Facile Self‐Assembly of Intermetallic [Ni2Gd2] Cubane Aggregate for Magnetic Refrigeration
Wang, P., Shannigrahi, S. R., Yakovlev, N. L., & Hor, T. S. A. (2013) · Chemistry - An Asian Journal · doi:10.1002/asia.201300838 · Cited by 23 · see citing works
Data mining for materials design: A computational study of single molecule magnet
Dam, H., Pham, T.-L., Ho, T. B., Nguyen, A. T., & Nguyen, V.-C. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4862156 · Cited by 17 · see citing works
Redox Switches for Single‐Molecule Magnet Activity: An Ab Initio Insight
Vieru, V., & Chibotaru, L. F. (2016) · Chemistry - A European Journal · doi:10.1002/chem.201504490 · Cited by 6 · see citing works

Flagged first, then by citations

Sorted by citations

Artificial intelligence and machine learning in design of mechanical materials
Guo, K., Yang, Z., Yu, C., & Buehler, M. J. (2020) · Materials Horizons · doi:10.1039/d0mh01451f · Cited by 702 · see citing works
Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works
Machine-learning and high-throughput studies for high-entropy materials
Huang, E., Lee, W., Singh, S. S., Kumar, P., Lee, C., Lam, T., Chin, H.-H., Lin, B., & Liaw, P. K. (2022) · Materials Science and Engineering R Reports · doi:10.1016/j.mser.2021.100645 · Cited by 170 · see citing works
Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells
Zhao, Z.-W., del Cueto, M., Geng, Y., & Troisi, A. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.0c02325 · Cited by 102 · see citing works
XGBoost model for electrocaloric temperature change prediction in ceramics
Gong, J., Chu, S., Mehta, R. K., & McGaughey, A. J. H. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00826-3 · Cited by 71 · see citing works
Machine Learning for the Discovery, Design, and Engineering of Materials.
Duan, C., Nandy, A., & Kulik, H. J. (2022) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092320-120230 · Cited by 69 · see citing works
Machine learning assisted development of Fe2P-type magnetocaloric compounds for cryogenic applications
Lai, J., Bolyachkin, A. S., Terada, N., Dieb, S., Tang, X., Ohkubo, T., Sepehri‐Amin, H., & Hono, K. (2022) · Acta Materialia · doi:10.1016/j.actamat.2022.117942 · Cited by 50 · see citing works
Data-driven assessment of chemical vapor deposition grown MoS2 monolayer thin films
Costine, A., Delsa, P., Li, T., Reinke, P., & Balachandran, P. V. (2020) · Journal of Applied Physics · doi:10.1063/5.0017507 · Cited by 38 · see citing works
Self-citation
Density functional theory and machine learning guided search for RE 2 Si 2 O 7 with targeted coefficient of thermal expansion
Ayyasamy, M. V., Deijkers, J. A., Wadley, H. N. G., & Balachandran, P. V. (2020) · Journal of the American Ceramic Society · doi:10.1111/jace.17121 · Cited by 31 · see citing works
Self-citation
Exchange Spin Coupling from Gaussian Process Regression
Bahlke, M. P., Mogos, N., Proppe, J., & Herrmann, C. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c05983 · Cited by 27 · see citing works
Machine learning predicted magnetic entropy change using chemical descriptors across a large compositional landscape
Ucar, H., Paudyal, D., & Choudhary, K. K. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111414 · Cited by 26 · see citing works
Accurate prediction of magnetocaloric effect in NiMn‐based Heusler alloys by prioritizing phase transitions through explainable machine learning
Tang, Y., Cao, K., Ma, R., Wang, J., Zhang, Y., Zhang, D., Zhou, C., Tian, F., Fang, M., & Yang, S. (2024) · Rare Metals · doi:10.1007/s12598-024-02953-w · Cited by 26 · see citing works
Magnetocaloric Effect in Cu5-NIPA Molecular Magnet: A Theoretical Study
Szałowski, K., & Kowalewska, P. (2020) · Materials · doi:10.3390/ma13020485 · Cited by 21 · see citing works
Accelerated Design Of MTX Alloys with Targeted Magnetostructural Properties Through Interpretable Machine Learning
Hartnett, T. Q., Sharma, V., Garg, S., Barua, R., & Balachandran, P. V. (2021) · Acta Materialia · doi:10.1016/j.actamat.2022.117891 · Cited by 18 · see citing works
Self-citation
AI for dielectric capacitors
Liu, R., Wang, J., Shen, Z.-H., & Shen, Y. (2024) · Energy storage materials · doi:10.1016/j.ensm.2024.103612 · Cited by 16 · see citing works
Data-driven design of B20 alloys with targeted magnetic properties guided by machine learning and density functional theory
Balachandran, P. V. (2020) · Journal of materials research/Pratt's guide to venture capital sources · doi:10.1557/jmr.2020.38 · Cited by 13 · see citing works
Self-citation
Low-Temperature Magnetocaloric Properties of V12 Polyoxovanadate Molecular Magnet: A Theoretical Study
Szałowski, K. (2020) · Materials · doi:10.3390/ma13194399 · Cited by 11 · see citing works
Magnetic relaxation dependences on the central ions for Ln (Ln = Tb, Dy, Er) phthalocyanines
Ruan, L., Tong, J., Li, L., Luo, F., Zhang, R., Qin, G. W., & Zhang, X. (2020) · Applied Physics Letters · doi:10.1063/5.0019909 · Cited by 8 · see citing works
Machine learning assisted electrocaloric and pyroelectric performance of Ba0.85Ca0.15Ti0.9Zr0.1O3 ceramic for solid state refrigeration
Nayak, M., Haldar, B., Joardar, H., Badapanda, T., Sahoo, S., Alrasheedi, N. H., & Abdala, A. A. (2025) · Ceramics International · doi:10.1016/j.ceramint.2025.08.392 · Cited by 4 · see citing works
Thermodynamics of Fullerene C60 in a Magnetic Field
Borodin, V. I., Bubenchikov, M. A., Nosyrev, O. D., & Mamontov, D. V. (2023) · Russian Physics Journal · doi:10.1007/s11182-023-02916-1 · Cited by 3 · see citing works
Octahedral Lattice Distortion-Driven Ferroelectricity and Canted Antiferromagnetism in Oxalate and Phenanthrolin Ligand-Based Chain Molecular Magnets [{Fe(Δ)Fe(Λ)}1–x{Cr(Δ)Cr(Λ)}x(ox)2(phen)2]n (x = 0, 0.1, and 0.5)
Bhatt, P., Kumar, A., Ritter, C., & Yusuf, S. M. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c05079 · Cited by 3 · see citing works
Machine learning in magnetism: Progress, challenges, and perspectives
Jiang, C., Xie, W., Wang, M. Z., Zhou, T., Song, W., Yue, Y., & Hu, X. (2026) · Computational Materials Science · doi:10.1016/j.commatsci.2026.114652 · Cited by 2 · see citing works
Magnetic modification of Dy-phthalocyanine single-molecule magnet by Co-crystallizing diamagnetic Y-phthalocyanine
Li, L., Tong, J., Ruan, L., Luo, F., Qin, G. W., & Zhang, X. (2021) · Journal of Magnetism and Magnetic Materials · doi:10.1016/j.jmmm.2021.168162 · Cited by 2 · see citing works
Low-temperature magnetocaloric effect of the polyoxovanadate molecular magnet {VIV/V12As8}: An experimental study
Pełka, R., Szałowski, K., Rajňák, M., Sas, W., Czernia, D., Konieczny, P., Kobylarczyk, J., Mihalik, M., & Kögerler, P. (2024) · Journal of Magnetism and Magnetic Materials · doi:10.1016/j.jmmm.2024.171722 · Cited by 2 · see citing works
Dynamics of C60 Molecule and Surrounding Gas Molecules
Borodin, V. I., Bubenchikov, M. A., Nosyrev, O. D., & Mamontov, D. V. (2023) · Russian Physics Journal · doi:10.1007/s11182-023-02915-2 · Cited by 1 · see citing works
Accelerated Design Of MTX Alloys with Targeted Magnetostructural Properties Through Interpretable Machine LearningPreprint
Hartnett, T. Q., Sharma, V., Garg, S., Barua, R., & Balachandran, P. V. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3994647
Self-citation
Low-Temperature Magnetocaloric Effect of the Polyoxovanadate Molecular Magnet {Viv/V12as8}: An Experimental StudyPreprint
Pełka, R., Szałowski, K., Rajňák, M., Sas, W., Czernia, D., Konieczny, P., Kobylarczyk, J., Mihalik, M., & Kögerler, P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4607778
Perspectives of Machine Learning for Ligand-Field Analyses in Lanthanide-Based Single Molecule Magnets
Ali, Z. A., Tewatia, P., & Waldmann, O. (2026) · Magnetochemistry · doi:10.3390/magnetochemistry12020019 · Cited by 1 · see citing works
Predictive Computational Analysis of Exchange Interactions in Gd3+ Complexes with Biradical Ligands
Dmitriev, A. A., Kozlov, R. V., & Vostrikova, K. E. (2026) · Magnetochemistry · doi:10.3390/magnetochemistry12100119

Machine learning accelerates design of single-molecule magnets for magnetocaloric applications
Patrick, C. (2019) · Scilight · doi:10.1063/1.5113601
Machine learning predicted magnetic entropy change using chemical descriptors across a large compositional landscape
Ucar, H., Paudyal, D., & Choudhary, K. K. (2022) · Computational Materials Science · doi:10.1016/j.commatsci.2022.111414 · Cited by 26 · see citing works
The prediction of single-molecule magnet properties via deep learning
Takiguchi, Y., Nakane, D., & Akitsu, T. (2024) · IUCrJ · doi:10.1107/s2052252524000770 · Cited by 10 · see citing works
Comparative study of machine learning models for predicting maximum magnetic entropy change in manganite magnetocaloric materials
Arfaoui, N., Thaljaoui, R., Aljaafari, A., Mohammadi, M. S., Asem, M., & Yousif, B. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.110838 · Cited by 1 · see citing works
Exploring novel Ni-Mn-X based magnetocaloric materials via machine learning with physical descriptors
Li, H., Tian, X., Zhao, W., Yang, J., Wang, X., Zhang, K., Li, J., & Tan, C. (2025) · Applied Materials Today · doi:10.1016/j.apmt.2025.102923

Authors: Holleis, L., Shivaram, B. S., & Balachandran, P. V.
Affiliations: University of Virginia (US)
ORCID: Holleis, Ludwig
Shivaram, B. S.
Balachandran, Prasanna V.
Venue: Applied Physics Letters
Publisher: AIP Publishing
Published: 2019-06-03
Volume: 114
Issue: 22
ISSN: 0003-6951 (ISSN-L)
1077-3118
DOI: 10.1063/1.5094553
OpenAlex: W2952673586
Cited by: 29
References: 58
Citations by year: 2026: 3 · 2025: 1 · 2024: 3 · 2023: 3 · 2022: 6 · 2021: 3 · 2020: 10
FWCI: 1.47
Type: journal article
Language: en
Open access: closed
Locations: Applied Physics Letters (publishedVersion)
Topics: Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Magnetism in coordination complexes
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Magnetic and transport properties of perovskites and related materials
Keywords: single-molecule magnets, magnetocaloric effect, machine learning, molecular design, magnetic refrigeration, structural descriptors, coordination environment, ligand chemistry

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Machine learning guided design of single-molecule magnets for magnetocaloric applications

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks