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1. Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods

G. Pilania, P.V. Balachandran, J.E. Gubernatis and T. Lookman

Publisher: Morgan & Claypool Publishers

Electronic ISBN: 9781681737386

Print ISBN: 9781681737393

Print ISBN: 9781681737379


4. Importance of Feature Selection in Machine Learning and Adaptive Design for Materials

P.V. Balachandran et al.

In T. Lookman, S. Eidenbenz, F. Alexander and C. Barnes (Eds.), Materials Discovery and Design, Springer Ser. Materials, Vol. 280 (2018).

3. Symmetry-adapted distortion modes as descriptors for materials informatics

P.V. Balachandran, N.A. Benedek and J.M. Rondinelli

In T. Lookman, F. J. Alexander and K. Rajan (Eds.), Information Science for Materials Discovery and Design, Springer Ser. Materials, Vol. 225 (2016).

2. A perspective on materials informatics: state-of-the-art and future directions

T. Lookman, P.V. Balachandran et al.

In T. Lookman, F. J. Alexander and K. Rajan (Eds.), Information Science for Materials Discovery and Design, Springer Ser. Materials, Vol. 225 (2016).

1. Informatics-based approaches for accelerated discovery of functional materials

P.V. Balachandran and J.M. Rondinelli

In S. Datta and J. Paulo Davim (Eds.), Computational Approaches to Materials Design: Theoretical and Practical Aspects, IGI Global (2016).




49. Density functional theory study of chemical pressure in muticaloric MTX compounds

T.Q. Hartnett, V. Sharma, R. Barua, and P.V. Balachandran

Applied Physics Letters 118, 212408 (2021).

48. Tuning Dzyaloshinskii-Moriya interaction in ferrimagnetic GdCo: A first-principles approach

Md.G. Morshed, K.H. Khoo, Y. Quessab, J-W. Xu, R. Laskowski, P.V. Balachandran, A.D. Kent, and A. Ghosh

Physical Review B 103, 174414 (2021).

47. Machine learning for materials design and discovery

R. Vasudevan, G. Pilania, and P. V. Balachandran

Journal of Applied Physics 129, 070401 (2021).

46. Rare-earth-free ferrimagnetic Mn4N sub-20 nm thin films as potential high-temperature spintronic material

W. Zhou, C.T. Ma, T.Q. Hartnett, P. V. Balachandran, and S.J. Poon

AIP Advances 11, 015334 (2021).



45. Data-driven assessment of chemical vapor deposition grown MoS2 monolayer thin films

A. Costine, P. Delsa, P. Reinke, and P. V. Balachandran

Journal of Applied Physics 128, 235303 (2020).

44. Magnetic Damping in Epitaxial Iron Alloyed with Vanadium and Aluminum

D. A. Smith, A. Rai, Y. Lim, T. Q. Hartnett, A. Sapkota, A. Srivastava, C. Mewes, Z. Jiang, M. Clavel, M. K. Hudait, D. D. Viehland, J. J. Heremans, P. V. Balachandran, T. Mewes, and S. Emori

Physical Review Applied 14, 034042 (2020).

43. Adaptive machine learning for efficient materials design

P.V. Balachandran

MRS Bulletin 45, 579-586 (2020).

42. Compositional dependence of linear and nonlinear optical response in crystalline hafnium zirconium oxide thin films

J.F. Ihlefeld, T.S Luk, S.W. Smith, S.S. Fields, S.T. Jaszewski, D.M. Hirt, W.T. Riffe, S.T. Bender, C. Constantin, M.V. Ayyasamy, P.V. Balachandran, P. Lu, M.D. Henry and P.S. Davids

Journal of Applied Physics 128, 034101 (2020).

41. Chemical gradients in human enamel crystallites

K.A. DeRocher, P.J.M. Smeets, B.H. Goodge, M.J. Zachman, P.V. Balachandran, L. Stegbauer, M.J. Cohen, L.M. Gordon, J.M. Rondinelli, L.F. Kourkoutis and D. Joester

Nature 583, 66-71 (2020).

40. Density functional theory and machine learning guided search for RE2Si2O7 with targeted coefficient of thermal expansion

M.V. Ayyasamy, J.A. Deijkers, H.N.G. Wadley and P.V. Balachandran

Journal of the American Ceramic Society 103, 4489-4497 (2020).

39. Conductivity-like Gilbert Damping due to Intraband Scattering in Epitaxial Iron 

B. Khodadadi, A. Rai, A. Sapkota, A. Srivastava, B. Nepal, Y. Lim, D.A. Smith, C. Mewes, S. Budhathoki, A.J. Hauser, M. Gao, J-F Li, D.D. Viehland, Z. Jiang, J.J. Heremans, P.V. Balachandran, T. Mewes, and S. Emori

Physical Review Letters 124, 157201 (2020).

Editor's Suggestion

38. Data-driven design of B20 alloys with targeted magnetic properties guided by machine learning and density functional theory

P.V. Balachandran

Journal of Materials Research 35, 890-897 (2020).

Special Issue: JMR Early Career Scholars in Materials Science Annual Issue



37. EDITORIAL: Future Frontiers in Corrosion Science and Engineering, Part III: The Next “Leap Ahead” in Corrosion Control May Be Enabled by Data Analytics and Artificial Intelligence

J.R. Scully and P.V. Balachandran

Corrosion 75, 1395-1397 (2019).

36. Prediction of new iodine-containing apatites using machine learning and density functional theory

T.Q. Hartnett, M.V. Ayyasamy and P.V. Balachandran

MRS Communications 9, 882-890 (2019).

35. Machine learning guided design of single-molecule magnets for magnetocaloric applications (Feature article)

L. Holleis, B.S. Shivaram and P.V. Balachandran

Applied Physics Letters 114, 222404 (2019).

AIP Scilight: Machine learning accelerates design of single-molecule magnetis for magnetocaloric applications

34. Machine learning guided design of functional materials with targeted properties

P.V. Balachandran

Computational Materials Science 164, 82-90 (2019).

Special Issue: Rising Stars in Computational Materials Science

33. Active learning in materials science with an emphasis on adaptive sampling using uncertainties for targeted design

T. Lookman, P.V. Balachandran, D. Xue and R. Yuan

npj Computational Materials 5, 21 (2019).



32. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning

P.V. Balachandran, B. Kowalski, A. Sehirlioglu and T. Lookman

Nature Communications 9, 1668 (2018).

31. Predictions of new ABO3 perovskite compounds by combining machine learning and density functional theory

P.V. Balachandran, A.A. Emery, J.E. Gubernatis, T. Lookman, C. Wolverton and A. Zunger

Physical Review Materials 2, 043802 (2018).

30. Multi-objective Optimization for Materials Discovery via Adaptive Design

A.M. Gopakumar, P.V. Balachandran, D. Xue, J.E. Gubernatis and T. Lookman

Scientific Reports 8, 3738 (2018).

29. Accelerated Discovery of Large Electrostrains in BaTiO3-Based Piezoelectrics Using Active Learning

R. Yuan, Z. Liu, P.V. Balachandran, D. Xue, Y. Zhou, X. Ding, J. Sun, D. Xue and T. Lookman

Advanced Materials 30, 1702884 (2018).



28. Predicting Displacements of Octahedral Cations in Ferroelectric Perovskite using Machine Learning

P.V. Balachandran, T. Shearman, J. Theiler and T. Lookman

Acta Crystallographica Section B 73, 962-967 (2017).

27. Three-dimensional imaging of vortex structure in a ferroelectric nanoparticle driven by an electric field

D. Karpov, Z. Liu, T. dos Santos Rolo, R. Harder, P.V. Balachandran, D. Xue, T. Lookman and E. Fohtung

Nature Communications 8, 280 (2017).

26. Materials descriptors for morphotropic phase boundary curvature in lead-free piezoelectrics

D. Xue, P.V. Balachandran, H. Wu, R. Yuan, Y. Zhou, X-D. Ding, J. Sun and T. Lookman

Applied Physics Letters 111, 032907 (2017).

25. Learning from Data to Design Functional Materials without Inversion Symmetry

P.V. Balachandran, J. Young, T. Lookman and J.M. Rondinelli

Nature Communications 8, 14282 (2017).

24. Optimal Experimental Design for Materials Discovery

R. Dehghannasiri, D. Xue, P.V. Balachandran, M.R. Yousefi, L.A. Dalton, T. Lookman and E.R. Dougherty

Computational Materials Science 129, 311-312 (2017).

23. An informatics approach to transformation temperatures of NiTi-based shape memory alloys

D. Xue, D. Xue, R. Yuan, Y. Zhou, P.V. Balachandran, X. Ding, J. Sun and T. Lookman

Acta Materialia 125, 532-541 (2017).

22. Role of Cadmium on the Phase Transitions and Electrical Properties of BaTiO3 Ceramics

R. Yuan, P.V. Balachandran, D. Xue, Y. Zhou, X. Ding, J. Sun, T. Lookman and D. Xue

Ceramics International 43, 1114-11120 (2017).

21. Statistical Inference and Adaptive Design for Materials Discovery

T. Lookman, P.V. Balachandran, D. Xue, J. Hogden and J. Theiler

(invited) Current Opinion in Solid State & Materials Science 21, 121-128 (2017).



20. Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

D. Xue, P.V. Balachandran, R. Yuan, T. Hu, X. Qian, E.R. Dougherty and T. Lookman

Proceedings of the National Academy of Sciences of the USA 113, 13301-13306 (2016).

19. Finding new perovskite halides via machine learning

G. Pilania, P.V. Balachandran, C. Kim and T. Lookman

Frontiers in Materials 3, 19 (2016).

18. Structure-Curie temperature relationships in BaTiO3-based ferroelectric perovskites: Anomalous behavior of (Ba,Cd)TiO3 from DFT, statistical inference, and experiments

P.V. Balachandran, D. Xue and T. Lookman

Physical Review B 93, 144111 (2016).

17. Accelerated search for materials with targeted properties by adaptive design

D. Xue, P.V. Balachandran, J. Hogden, J. Theiler, D. Xue and T. Lookman

Nature Communications 7, 11241 (2016).

16. Adaptive strategies for materials design using uncertainties

P.V. Balachandran, D. Xue, J. Theiler, J. Hogden, T. Lookman

Scientific Reports 6, 19660 (2016).



15. Classification of ABO3 perovskite solids: a machine learning study

G. Pilania, P.V. Balachandran, J. Gubernatis and T. Lookman

Acta Crystallographica Section B 71, 507-513 (2015).

14. Materials prediction via classification learning

P.V. Balachandran, J. Theiler, J.M. Rondinelli and T. Lookman

Scientific Reports 5, 13285 (2015).

13. Polarization screening induced magnetic phase gradients at complex oxide interfaces

S.R. Spurgeon, P.V. Balachandran et al.

Nature Communications 6, 6735 (2015).

12. Massive band gap variation in layered oxides through cation ordering

P.V. Balachandran and J.M. Rondinelli

Nature Communications 6, 6191 (2015).



11. Polar cation ordering: A route to introducing >10% elastic strain into layered oxide films

B.B. Nelson-Cheeseman, H. Zhou, P.V. Balachandran, G. Fabris, J. Hoffman, D. Haskel, J.M. Rondinelli, and A. Bhattacharya

Advanced Functional Materials 24, 6884-6891 (2014).

10. Inductive crystal field control in layered metal oxides with correlated electrons

P.V. Balachandran, A. Cammarata, B.B. Nelson-Cheeseman, A. Bhattacharya, and J.M. Rondinelli

APL Materials 2, 076110 (2014).

9. A software framework for data dimensionality reduction: application to chemical crystallography

S.K. Samudrala, P.V. Balachandran, J. Zola, K. Rajan, and B. Ganapathysubramanian

Integrating Materials and Manufacturing Innovation 3, 17 (2014).

8. Electronically driven structural transitions in A10(PO4)6F2 apatites (A=Ca, Sr, Pb, Cd, and Hg)

P.V. Balachandran, K. Rajan, and J.M. Rondinelli

Acta Crystallographica Section B 70, 612-615 (2014).

7. Effect of interfacial octahedral behavior in ultrathin manganite films

E.J. Moon, P.V. Balachandran, B.J. Kirby, D.J. Keavney, R.J. Sichel-Tissot, C.M. Schlepütz, E. Karapetrova, X.M. Cheng, J.M. Rondinelli, and S.J. May

Nano Letters 14, 250-2514 (2014).

6. Thickness-dependent crossover from charge- to strain-mediated magnetoelectric coupling in ferromagnetic/piezoelectric oxide heterostructures

S.R. Spurgeon, J.D. Sloppy, D. Kepaptsoglou, P.V. Balachandran et al. 

ACS Nano 8, 894-903 (2014).

5. Crystal-chemistry guidelines for noncentrosymmetric A2BO4 Ruddlesden-Popper oxides

P.V. Balachandran, D. Puggioni, and J.M. Rondinelli

Inorganic Chemistry 53, 336-348 (2014).



4. Connecting bulk symmetry and orbital polarization in strained RNiO3 ultra-thin films

I.C. Tung, P.V. Balachandran, J. Liu, B.A. Gray, E.A. Karapetrova, J.H. Lee, J. Chakhalian, M.J. Bedzyk, J.M. Rondinelli, and J.W. Freeland

Physical Review B 88, 205112 (2013).

3. Interplay of octahedral rotations and breathing distortions in charge ordering perovskite oxides

P.V. Balachandran and J.M. Rondinelli

Physical Review B 88, 054101 (2013).



2. Structure maps for AI4AII6(BO4)6X2 apatites via data mining

P.V. Balachandran and K. RajanActa Crystallographica B 68, 24-33 (2012).



1. Identifying the 'inorganic gene' for high-temperature piezoelectric perovskites through statistical learning

P.V. Balachandran, S.R. Broderick and K. Rajan

Proceedings of the Royal Society A 467, 2271-2290 (2011).



1. Materials informatics as computational infrastructure for materials discovery

P.V. Balachandran and K. Rajan

In Proceedings of the SciDAC 2011, Denver, CO, July 2011 (

2. Data mining for new materials chemistries for immobilizing environmentally toxic elements: a systems engineering approach

P.V. Balachandran and K. Rajan

In NSF Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM) 2009, Columbia, MD, October 2009.