RESOURCES

Classical ML and Deep Learning: scikit-learn | scikit-opt | PyTorch | PyTorch Geometric | JAX | Tensorflow | Deep Learning

Awesome Lists: Trending Papers | Awesome Materials Informatics | Awesome Self Driving Labs | Awesome Generative AI 

Tools for Materials: matminer | ASE | automatminer | matgl | dscribe | elementembeddings | Atomistic ML

Tools for Molecules: RDKit | chemiscope | DeepChem | e3nn | stk 

Important Resourses related to Course:Topics in Computational Chemistry | Stanford ML Cheat Sheets | ML-for-Chemistry | ML-for-Materials | Mathematics in Materials | Practical-Deeplearning | NumericsOfML | Train-DL | machine-learning-refined | Materials-Informatics | LLM Visualisation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• A Mathematical Theory of Communication. C. E. Shannon, Bell System Technical Journal, 27, 379–423 (1948). 

• The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. F. Rosenblatt, Psychological Review, 65, 386–408 (1958).

• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities (VC Theory), V. N. Vapnik, A. Y. Chervonenkis, Theory of Probability & Its Applications, 16, 264–280 (1971).

• Induction of Decision Trees, J. R. Quinlan, Machine Learning, 1, 81–106 (1986).

• Support-Vector Networks (SVM), C. Cortes, V. Vapnik, Machine Learning, 20, 273–297 (1995). 

• Random Forests. L. Breiman, Machine Learning, 45, 5–32 (2001). 

• XGBoost: A Scalable Tree Boosting System. T. Chen, C. Guestrin, Proceedings of the 22nd ACM SIGKDD, pp. 785–794 (2016).

• Learning Representations by Back-Propagating Errors. D. E. Rumelhart, G. E. Hinton, R. J. Williams, Nature, 323, 533–536 (1986). 

• Backpropagation Applied to Handwritten Zip Code Recognition (CNN), Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, Neural Computation, 1, 541–551 (1989). 

• Approximation by Superpositions of a Sigmoidal Function (Universal Approximation Theorem). G. Cybenko, Mathematics of Control, Signals and Systems, 2, 303–314 (1989). 

• Long Short-Term Memory (LSTM). S. Hochreiter, J. Schmidhuber, Neural Computation, 9, 1735–1780 (1997). 

• A Fast Learning Algorithm for Deep Belief Nets. G. E. Hinton, S. Osindero, Y.-W. Teh, Neural Computation, 18, 1527–1554 (2006).

• Greedy Layer-Wise Training of Deep Networks. Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, " Advances in Neural Information Processing Systems (NIPS), 19 (2007). 

• ImageNet Classification with Deep Convolutional Neural Networks (AlexNet). A. Krizhevsky, I. Sutskever, G. E. Hinton, Advances in Neural Information Processing Systems (NIPS), 25 (2012). 

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

• Generative Adversarial Nets (GANs). I. J. Goodfellow et al., Advances in Neural Information Processing Systems (NIPS), 27 (2014). 

• Adam: A Method for Stochastic Optimization. D. P. Kingma, J. Ba, International Conference on Learning Representations (ICLR) (2015). 

• Deep Residual Learning for Image Recognition (ResNet), K. He, X. Zhang, S. Ren, J. Sun, CVPR (2016).

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, S. Ioffe, C. Szegedy, ICML (2015).

• Attention Is All You Need (Transformer), A. Vaswani et al., Advances in Neural Information Processing Systems (NIPS), 30 (2017).

• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, NAACL (2019).

• Language Models are Few-Shot Learners (GPT-3), T. Brown et al., Advances in Neural Information Processing Systems (NeurIPS), 33 (2020). 

• Semi-Supervised Classification with Graph Convolutional Networks (GCN). T. N. Kipf, M. Welling, ICLR (2017). 

• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties (CGCNN). T. Xie, J. C. Grossman, Physical Review Letters, 120, 145301 (2018). 

• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals (MEGNet). C. Chen, W. Ye, Y. Zuo, C. Zheng, S. P. Ong, Chemistry of Materials, 31, 3564–3572 (2019). 

• A Universal Graph Deep Learning Interatomic Potential for the Periodic Table (M3GNet). C. Chen, S. P. Ong, Nature Computational Science, 2, 718–728 (2022). 

• Machine Learning for Molecular and Materials Science, K. T. Butler, D. W. Davies, H. Cartwright, O. Ceriotti, A. Walsh, " Nature, 559, 547–555 (2018).

• Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces (HDNNP). J. Behler, M. Parrinello, Physical Review Letters, 98, 146401 (2007). 

• Gaussian Approximation Potentials (GAP). A. P. Bartók, M. C. Payne, R. Kondor, G. Csányi, Physical Review Letters, 104, 136403 (2010). 

• Recent Advances and Applications of Machine Learning in Solid-State Materials Science. J. Schmidt, M. R. G. Marques, S. Botti, M. A. L. Marques, npj Computational Materials, 5, 83 (2019). 

• Inverse Molecular Design Using Machine Learning: Generative Models for Matter Engineering. B. Sanchez-Lengeling, A. Aspuru-Guzik, Science, 361, 360–365 (2018). 

• Gaussian Processes for Machine Learning. C. E. Rasmussen, C. K. I. Williams,  MIT Press (2006). 

• Taking the Human Out of the Loop: A Review of Bayesian Optimization. B. Shahriari, K. Swersky, Z. Wang, R. P. Adams, N. de Freitas, Proceedings of the IEEE, 104, 148–175 (2016).