RESOURCES

1. P. Hohenberg, W. Kohn, "Inhomogeneous Electron Gas," Physical Review, 136, B864–B871 (1964). Read

2. W. Kohn, L. J. Sham, "Self-Consistent Equations Including Exchange and Correlation Effects,"  Physical Review, 140, A1133–A1138 (1965). Read

3. W. Kohn, "Nobel Lecture: Electronic Structure of Matter — Wave Functions and Density Functionals," Reviews of Modern Physics, 71, 1253 (1999). Read

4. J. P. Perdew, A. Zunger, "Self-Interaction Correction to Density-Functional Approximations for Many-Electron Systems (LDA)," Physical Review B, 23, 5048 (1981). Read

5. J. P. Perdew, K. Burke, M. Ernzerhof, "Generalized Gradient Approximation Made Simple (PBE)," Physical Review Letters, 77, 3865 (1996). Read

6. A. D. Becke, "Density-Functional Exchange-Energy Approximation with Correct Asymptotic Behavior (B88)," Physical Review A, 38, 3098 (1988). Read

7. C. Lee, W. Yang, R. G. Parr, "Development of the Colle-Salvetti Correlation-Energy Formula into a Functional of the Electron Density (LYP)," Physical Review B, 37, 785 (1988). Read

8. A. D. Becke, "A New Mixing of Hartree–Fock and Local Density-Functional Theories (B3LYP)," The Journal of Chemical Physics, 98, 1372 (1993). Read

9. J. Tao, J. P. Perdew, V. N. Staroverov, G. E. Scuseria, "Climbing the Density Functional Ladder: Nonempirical Meta-GGA (TPSS)," Physical Review Letters, 91, 146401 (2003). Read

10. J. Sun, A. Ruzsinszky, J. P. Perdew, "Strongly Constrained and Appropriately Normed Semilocal Density Functional (SCAN)," Physical Review Letters, 115, 036402 (2015). Read

11. S. Grimme, "Semiempirical GGA-type Density Functional with Long-Range Dispersion Correction (DFT-D)," Journal of Computational Chemistry, 27, 1787 (2006). Read

12. S. Grimme, J. Antony, S. Ehrlich, H. Krieg, "A Consistent and Accurate Ab Initio Parametrization of Density Functional Dispersion Correction (DFT-D3)," The Journal of Chemical Physics, 132, 154104 (2010). Read

13. A. Tkatchenko, M. Scheffler, "Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data," Physical Review Letters, 102, 073005 (2009). Read

14. P. E. Blöchl, "Projector Augmented-Wave Method (PAW)," Physical Review B, 50, 17953 (1994). Read

15. H. J. Monkhorst, J. D. Pack, "Special Points for Brillouin-Zone Integrations," Physical Review B, 13, 5188 (1976). Read

16. N. Troullier, J. L. Martins, "Efficient Pseudopotentials for Plane-Wave Calculations," Physical Review B, 43, 1993 (1991). Read

17. R. Ditchfield, W. J. Hehre, J. A. Pople, "Self-Consistent Molecular-Orbital Methods. IX. An Extended Gaussian-Type Basis for Molecular-Orbital Studies of Organic Molecules (6-31G)," The Journal of Chemical Physics, 54, 724 (1971). Read

18. T. H. Dunning Jr., "Gaussian Basis Sets for Use in Correlated Molecular Calculations (cc-pVXZ)," The Journal of Chemical Physics, 90, 1007 (1989). Read

19. F. Weigend, R. Ahlrichs, "Balanced Basis Sets of Split Valence, Triple Zeta Valence and Quadruple Zeta Valence Quality for H to Rn (def2-TZVP)," Physical Chemistry Chemical Physics, 7, 3297 (2005). Read

20. V. I. Anisimov, J. Zaanen, O. K. Andersen, "Band Theory and Mott Insulators: Hubbard U Instead of Stoner I (LDA+U)," Physical Review B, 44, 943 (1991). Read

21. S. L. Dudarev, G. A. Botton, S. Y. Savrasov, C. J. Humphreys, A. P. Sutton, "Electron-Energy-Loss Spectra and the Structural Stability of Nickel Oxide: An LSDA+U Study," Physical Review B, 57, 1505 (1998). Read

22. J. C. Slater, "The Self-Consistent Field and the Structure of Atoms," Physical Review, 32, 339 (1928). Read

23. V. Fock, "Näherungsmethode zur Lösung des quantenmechanischen Mehrkörperproblems (Hartree-Fock)," Zeitschrift für Physik, 61, 126 (1930). Read

24. C. Møller, M. S. Plesset, "Note on an Approximation Treatment for Many-Electron Systems (MP2)," Physical Review, 46, 618 (1934). Read

25. J. Čížek, "On the Correlation Problem in Atomic and Molecular Systems. Calculation of Wavefunction Components in Ursell-Type Expansion Using Quantum-Field Theoretical Methods (Coupled Cluster)," The Journal of Chemical Physics, 45, 4256 (1966). Read

26. G. D. Purvis III, R. J. Bartlett, "A Full Coupled-Cluster Singles and Doubles Model: The Inclusion of Disconnected Triples (CCSD(T))," The Journal of Chemical Physics, 76, 1910 (1982). Read

27. M. J. Frisch et al., Gaussian 16, Revision C.01, Gaussian, Inc., Wallingford, CT (2016). Read

28. R. Ahlrichs, M. Bär, M. Häser, H. Horn, C. Kölmel, "Electronic Structure Calculations on Workstation Computers: The Program System Turbomole," Chemical Physics Letters, 162, 165 (1989). Read

29. G. Kresse, J. Furthmüller, "Efficient Iterative Schemes for Ab Initio Total-Energy Calculations Using a Plane-Wave Basis Set (VASP)," Physical Review B, 54, 11169 (1996). Read

30. P. Giannozzi et al., "QUANTUM ESPRESSO: A Modular and Open-Source Software Project for Quantum Simulations of Materials," Journal of Physics: Condensed Matter, 21, 395502 (2009). Read

31. F. Neese, "The ORCA Program System," WIREs Computational Molecular Science, 2, 73 (2012). Read

32. J. M. Soler et al., "The SIESTA Method for Ab Initio Order-N Materials Simulation," Journal of Physics: Condensed Matter, 14, 2745 (2002). Read

33. E. Runge, E. K. U. Gross, "Density-Functional Theory for Time-Dependent Systems," Physical Review Letters, 52, 997 (1984). Read

34. M. E. Casida, "Time-Dependent Density Functional Response Theory for Molecules," in Recent Advances in Density Functional Methods, World Scientific, Singapore, pp. 155–192 (1995). Read

I. Classical Machine Learning

1. C. E. Shannon, "A Mathematical Theory of Communication," Bell System Technical Journal, 27, 379–423 (1948). Read https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

2. F. Rosenblatt, "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain," Psychological Review, 65, 386–408 (1958). Read  https://doi.org/10.1037/h0042519

3. V. N. Vapnik, A. Y. Chervonenkis, "On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities (VC Theory)," Theory of Probability & Its Applications, 16, 264–280 (1971). Read https://doi.org/10.1137/1116025

4. J. R. Quinlan, "Induction of Decision Trees," Machine Learning, 1, 81–106 (1986). https://doi.org/10.1007/BF00116251

5. C. Cortes, V. Vapnik, "Support-Vector Networks (SVM)," Machine Learning, 20, 273–297 (1995). https://doi.org/10.1007/BF00994018

6. L. Breiman, "Random Forests," Machine Learning, 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324

7. T. Chen, C. Guestrin, "XGBoost: A Scalable Tree Boosting System," Proceedings of the 22nd ACM SIGKDD, pp. 785–794 (2016). https://doi.org/10.1145/2939672.2939785

II. Neural Networks and Backpropagation

1. D. E. Rumelhart, G. E. Hinton, R. J. Williams, "Learning Representations by Back-Propagating Errors," Nature, 323, 533–536 (1986). https://doi.org/10.1038/323533a0

2. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, "Backpropagation Applied to Handwritten Zip Code Recognition (CNN)," Neural Computation, 1, 541–551 (1989). https://doi.org/10.1162/neco.1989.1.4.541

3. G. Cybenko, "Approximation by Superpositions of a Sigmoidal Function (Universal Approximation Theorem)," Mathematics of Control, Signals and Systems, 2, 303–314 (1989). https://doi.org/10.1007/BF02551274

4. S. Hochreiter, J. Schmidhuber, "Long Short-Term Memory (LSTM)," Neural Computation, 9, 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

III. Deep Learning Revolution

1. G. E. Hinton, S. Osindero, Y.-W. Teh, "A Fast Learning Algorithm for Deep Belief Nets," Neural Computation, 18, 1527–1554 (2006). https://doi.org/10.1162/neco.2006.18.7.1527

2. Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, "Greedy Layer-Wise Training of Deep Networks," Advances in Neural Information Processing Systems (NIPS), 19 (2007). https://proceedings.neurips.cc/paper/2006/hash/5da713a690c067105aeb2fae32403405-Abstract.html

3. A. Krizhevsky, I. Sutskever, G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)," Advances in Neural Information Processing Systems (NIPS), 25 (2012). https://doi.org/10.1145/3065386

4. G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R. R. Salakhutdinov, "Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors (Dropout)," arXiv preprint, arXiv:1207.0580 (2012). https://arxiv.org/abs/1207.0580

5. I. J. Goodfellow et al., "Generative Adversarial Nets (GANs)," Advances in Neural Information Processing Systems (NIPS), 27 (2014). https://doi.org/10.48550/arXiv.1406.2661

6. D. P. Kingma, J. Ba, "Adam: A Method for Stochastic Optimization," International Conference on Learning Representations (ICLR) (2015). https://arxiv.org/abs/1412.6980

7. K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition (ResNet)," CVPR (2016). https://doi.org/10.1109/CVPR.2016.90

8. S. Ioffe, C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," ICML (2015). https://arxiv.org/abs/1502.03167

IV. Transformers and Attention

1. A. Vaswani et al., "Attention Is All You Need (Transformer)," Advances in Neural Information Processing Systems (NIPS), 30 (2017). https://doi.org/10.48550/arXiv.1706.03762

2. J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," NAACL (2019). https://doi.org/10.48550/arXiv.1810.04805

3. T. Brown et al., "Language Models are Few-Shot Learners (GPT-3)," Advances in Neural Information Processing Systems (NeurIPS), 33 (2020). https://doi.org/10.48550/arXiv.2005.14165

V. Graph Neural Networks (from Materials Science Perspective)

1. T. N. Kipf, M. Welling, "Semi-Supervised Classification with Graph Convolutional Networks (GCN)," ICLR (2017). https://arxiv.org/abs/1609.02907

2. T. Xie, J. C. Grossman, "Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties (CGCNN)," Physical Review Letters, 120, 145301 (2018). https://doi.org/10.1103/PhysRevLett.120.145301

3. C. Chen, W. Ye, Y. Zuo, C. Zheng, S. P. Ong, "Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals (MEGNet)," Chemistry of Materials, 31, 3564–3572 (2019). https://doi.org/10.1021/acs.chemmater.9b01294

4. C. Chen, S. P. Ong, "A Universal Graph Deep Learning Interatomic Potential for the Periodic Table (M3GNet)," Nature Computational Science, 2, 718–728 (2022). https://doi.org/10.1038/s43588-022-00349-3

VI. Review Articles on AI/ML for Materials Science 

1. K. T. Butler, D. W. Davies, H. Cartwright, O. Ceriotti, A. Walsh, "Machine Learning for Molecular and Materials Science," Nature, 559, 547–555 (2018). https://doi.org/10.1038/s41586-018-0337-2

2. J. Behler, M. Parrinello, "Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces (HDNNP)," Physical Review Letters, 98, 146401 (2007). https://doi.org/10.1103/PhysRevLett.98.146401

3. A. P. Bartók, M. C. Payne, R. Kondor, G. Csányi, "Gaussian Approximation Potentials (GAP)," Physical Review Letters, 104, 136403 (2010). https://doi.org/10.1103/PhysRevLett.104.136403

4. J. Schmidt, M. R. G. Marques, S. Botti, M. A. L. Marques, "Recent Advances and Applications of Machine Learning in Solid-State Materials Science," npj Computational Materials, 5, 83 (2019). https://doi.org/10.1038/s41524-019-0221-0

5. B. Sanchez-Lengeling, A. Aspuru-Guzik, "Inverse Molecular Design Using Machine Learning: Generative Models for Matter Engineering," Science, 361, 360–365 (2018). https://doi.org/10.1126/science.aat2663

VII. Gaussian Process and Bayesian Methods

1. C. E. Rasmussen, C. K. I. Williams, Gaussian Processes for Machine Learning, MIT Press (2006). http://www.gaussianprocess.org/gpml/

2. B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, N. de Freitas, "Taking the Human Out of the Loop: A Review of Bayesian Optimization," Proceedings of the IEEE, 104, 148–175 (2016). https://doi.org/10.1109/JPROC.2015.2494218