Appendix E — Bibliography¶
A consolidated bibliography of the references cited throughout the book, organised by the chapter in which they first appear. Entries are given in a BibTeX-style markdown format that is easy to copy into a citation manager.
Foundational textbooks (referenced across the book)¶
Martin — Martin, R. M. Electronic Structure: Basic Theory and Practical Methods, 2nd ed. Cambridge University Press, 2020. ISBN 978-1-108-42990-0.
Martin–Reining–Ceperley — Martin, R. M.; Reining, L.; Ceperley, D. M. Interacting Electrons: Theory and Computational Approaches. Cambridge University Press, 2016. ISBN 978-0-521-87150-1.
Ashcroft–Mermin — Ashcroft, N. W.; Mermin, N. D. Solid State Physics. Holt, Rinehart and Winston, 1976. ISBN 978-0-03-083993-1.
Marder — Marder, M. P. Condensed Matter Physics, 2nd ed. Wiley, 2010. ISBN 978-0-470-61798-4.
Kittel — Kittel, C. Introduction to Solid State Physics, 8th ed. Wiley, 2005. ISBN 978-0-471-41526-8.
Frenkel–Smit — Frenkel, D.; Smit, B. Understanding Molecular Simulation: From Algorithms to Applications, 3rd ed. Academic Press, 2023. ISBN 978-0-323-90292-2.
Allen–Tildesley — Allen, M. P.; Tildesley, D. J. Computer Simulation of Liquids, 2nd ed. Oxford University Press, 2017. ISBN 978-0-19-880319-5.
Tuckerman — Tuckerman, M. E. Statistical Mechanics: Theory and Molecular Simulation, 2nd ed. Oxford University Press, 2023. ISBN 978-0-19-882526-5.
Sholl–Steckel — Sholl, D. S.; Steckel, J. A. Density Functional Theory: A Practical Introduction. Wiley, 2009. ISBN 978-0-470-37317-0.
Parr–Yang — Parr, R. G.; Yang, W. Density-Functional Theory of Atoms and Molecules. Oxford University Press, 1989. ISBN 978-0-19-509276-9.
Griffiths — Griffiths, D. J.; Schroeter, D. F. Introduction to Quantum Mechanics, 3rd ed. Cambridge University Press, 2018. ISBN 978-1-107-18963-8.
Shankar — Shankar, R. Principles of Quantum Mechanics, 2nd ed. Plenum Press, 1994. ISBN 978-0-306-44790-7.
Cohen-Tannoudji — Cohen-Tannoudji, C.; Diu, B.; Laloë, F. Quantum Mechanics (Vols. I–III), 2nd English ed. Wiley-VCH, 2019. ISBN 978-3-527-34553-3.
Goodfellow–Bengio–Courville — Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning. MIT Press, 2016. ISBN 978-0-262-03561-3.
Bishop — Bishop, C. M. Pattern Recognition and Machine Learning. Springer, 2006. ISBN 978-0-387-31073-2.
Murphy 2022 — Murphy, K. P. Probabilistic Machine Learning: An Introduction. MIT Press, 2022. ISBN 978-0-262-04682-4.
Chapter 4 — Quantum many-body theory¶
Hohenberg–Kohn 1964 — Hohenberg, P.; Kohn, W. "Inhomogeneous electron gas." Physical Review 136, B864–B871 (1964). [The founding paper of density functional theory.]
Kohn–Sham 1965 — Kohn, W.; Sham, L. J. "Self-consistent equations including exchange and correlation effects." Physical Review 140, A1133–A1138 (1965). [The Kohn–Sham auxiliary system.]
Hartree 1928 — Hartree, D. R. "The wave mechanics of an atom with a non-Coulomb central field. Part II: Some results and discussion." Proceedings of the Cambridge Philosophical Society 24, 111–132 (1928).
Fock 1930 — Fock, V. "Näherungsmethode zur Lösung des quantenmechanischen Mehrkörperproblems." Zeitschrift für Physik 61, 126–148 (1930). [The original Hartree–Fock method.]
Slater 1929 — Slater, J. C. "The theory of complex spectra." Physical Review 34, 1293–1322 (1929). [The Slater determinant.]
Møller–Plesset 1934 — Møller, C.; Plesset, M. S. "Note on an approximation treatment for many-electron systems." Physical Review 46, 618–622 (1934).
Chapter 5 — Density functional theory¶
Levy 1979 — Levy, M. "Universal variational functionals of electron densities, first-order density matrices, and natural spin-orbitals and solution of the v-representability problem." Proceedings of the National Academy of Sciences of the United States of America 76, 6062–6065 (1979). [The constrained-search formulation of density functional theory.]
Lieb 1983 — Lieb, E. H. "Density functionals for Coulomb systems." International Journal of Quantum Chemistry 24, 243–277 (1983). [The rigorous mathematical foundation of the constrained search.]
Perdew–Burke–Ernzerhof 1996 (PBE) — Perdew, J. P.; Burke, K.; Ernzerhof, M. "Generalized gradient approximation made simple." Physical Review Letters 77, 3865–3868 (1996). [The PBE functional, the most-used GGA in materials science.]
Becke 1993 (B3LYP) — Becke, A. D. "Density-functional thermochemistry. III. The role of exact exchange." Journal of Chemical Physics 98, 5648–5652 (1993).
Heyd–Scuseria–Ernzerhof 2003 (HSE) — Heyd, J.; Scuseria, G. E.; Ernzerhof, M. "Hybrid functionals based on a screened Coulomb potential." Journal of Chemical Physics 118, 8207–8215 (2003).
Sun–Ruzsinszky–Perdew 2015 (SCAN) — Sun, J.; Ruzsinszky, A.; Perdew, J. P. "Strongly constrained and appropriately normed semilocal density functional." Physical Review Letters 115, 036402 (2015).
Grimme 2010 (D3) — Grimme, S.; Antony, J.; Ehrlich, S.; Krieg, H. "A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H–Pu." Journal of Chemical Physics 132, 154104 (2010).
Burke 2012 — Burke, K. "Perspective on density functional theory." Journal of Chemical Physics 136, 150901 (2012).
Burke–Wagner 2013 — Burke, K.; Wagner, L. O. "DFT in a nutshell." International Journal of Quantum Chemistry 113, 96–101 (2013).
Chapter 6 — Running DFT¶
Car–Parrinello 1985 — Car, R.; Parrinello, M. "Unified approach for molecular dynamics and density-functional theory." Physical Review Letters 55, 2471–2474 (1985).
Kresse–Hafner 1993 (VASP) — Kresse, G.; Hafner, J. "Ab initio molecular dynamics for liquid metals." Physical Review B 47, 558–561 (1993).
Giannozzi 2009 (Quantum ESPRESSO) — Giannozzi, P. et al. "Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials." Journal of Physics: Condensed Matter 21, 395502 (2009).
Blöchl 1994 (PAW) — Blöchl, P. E. "Projector augmented-wave method." Physical Review B 50, 17953–17979 (1994).
Vanderbilt 1990 (ultrasoft) — Vanderbilt, D. "Soft self-consistent pseudopotentials in a generalized eigenvalue formalism." Physical Review B 41, 7892–7895 (1990).
Hamann 2013 (ONCV) — Hamann, D. R. "Optimized norm-conserving Vanderbilt pseudopotentials." Physical Review B 88, 085117 (2013).
Monkhorst–Pack 1976 — Monkhorst, H. J.; Pack, J. D. "Special points for Brillouin-zone integrations." Physical Review B 13, 5188–5192 (1976).
Pulay 1980 — Pulay, P. "Convergence acceleration of iterative sequences. The case of SCF iteration." Chemical Physics Letters 73, 393–398 (1980).
Chapter 7 — Molecular dynamics¶
Verlet 1967 — Verlet, L. "Computer 'experiments' on classical fluids. I. Thermodynamical properties of Lennard-Jones molecules." Physical Review 159, 98–103 (1967).
Nosé 1984 — Nosé, S. "A unified formulation of the constant temperature molecular dynamics methods." Journal of Chemical Physics 81, 511–519 (1984).
Hoover 1985 — Hoover, W. G. "Canonical dynamics: Equilibrium phase-space distributions." Physical Review A 31, 1695–1697 (1985).
Bussi–Donadio–Parrinello 2007 (CSVR) — Bussi, G.; Donadio, D.; Parrinello, M. "Canonical sampling through velocity rescaling." Journal of Chemical Physics 126, 014101 (2007). [The stochastic velocity-rescaling (CSVR) thermostat.]
Parrinello–Rahman 1981 — Parrinello, M.; Rahman, A. "Polymorphic transitions in single crystals: A new molecular dynamics method." Journal of Applied Physics 52, 7182–7190 (1981).
Ewald 1921 — Ewald, P. P. "Die Berechnung optischer und elektrostatischer Gitterpotentiale." Annalen der Physik 369, 253–287 (1921).
Essmann 1995 (SPME) — Essmann, U. et al. "A smooth particle mesh Ewald method." Journal of Chemical Physics 103, 8577–8593 (1995).
Plimpton 1995 (LAMMPS) — Plimpton, S. "Fast parallel algorithms for short-range molecular dynamics." Journal of Computational Physics 117, 1–19 (1995).
Chapter 8 — Statistical mechanics¶
Metropolis 1953 — Metropolis, N.; Rosenbluth, A. W.; Rosenbluth, M. N.; Teller, A. H.; Teller, E. "Equation of state calculations by fast computing machines." Journal of Chemical Physics 21, 1087–1092 (1953).
Hastings 1970 — Hastings, W. K. "Monte Carlo sampling methods using Markov chains and their applications." Biometrika 57, 97–109 (1970).
Henkelman–Jónsson 2000 (NEB) — Henkelman, G.; Uberuaga, B. P.; Jónsson, H. "A climbing image nudged elastic band method for finding saddle points and minimum energy paths." Journal of Chemical Physics 113, 9901–9904 (2000).
Togo–Tanaka 2015 (phonopy) — Togo, A.; Tanaka, I. "First principles phonon calculations in materials science." Scripta Materialia 108, 1–5 (2015).
Chapter 9 — Machine-learning interatomic potentials¶
Behler–Parrinello 2007 — Behler, J.; Parrinello, M. "Generalized neural-network representation of high-dimensional potential-energy surfaces." Physical Review Letters 98, 146401 (2007).
Bartók 2010 (GAP) — Bartók, A. P.; Payne, M. C.; Kondor, R.; Csányi, G. "Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons." Physical Review Letters 104, 136403 (2010).
Bartók 2013 (SOAP) — Bartók, A. P.; Kondor, R.; Csányi, G. "On representing chemical environments." Physical Review B 87, 184115 (2013).
Schütt 2017 (SchNet) — Schütt, K. T.; Kindermans, P.-J.; Sauceda, H. E.; Chmiela, S.; Tkatchenko, A.; Müller, K.-R. "SchNet: A continuous-filter convolutional neural network for modeling quantum interactions." Advances in Neural Information Processing Systems (NeurIPS), 2017.
Batzner 2022 (NequIP) — Batzner, S. et al. "E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials." Nature Communications 13, 2453 (2022).
Batatia 2022 (MACE) — Batatia, I.; Kovács, D. P.; Simm, G. N. C.; Ortner, C.; Csányi, G. "MACE: Higher order equivariant message passing neural networks for fast and accurate force fields." Advances in Neural Information Processing Systems (NeurIPS), 2022.
Drautz 2019 (ACE) — Drautz, R. "Atomic cluster expansion for accurate and transferable interatomic potentials." Physical Review B 99, 014104 (2019).
Pozdnyakov 2020 — Pozdnyakov, S. N.; Willatt, M. J.; Bartók, A. P.; Ortner, C.; Csányi, G.; Ceriotti, M. "Incompleteness of atomic structure representations." Physical Review Letters 125, 166001 (2020). [Counterexamples showing that two- and three-body descriptors do not uniquely determine an atomic environment.]
Geiger–Smidt 2022 (e3nn) — Geiger, M.; Smidt, T. "e3nn: Euclidean neural networks." arXiv:2207.09453 (2022). [The reference library for E(3)-equivariant neural networks; underlies NequIP and MACE.]
Behler 2021 (review) — Behler, J. "Four generations of high- dimensional neural network potentials." Chemical Reviews 121, 10037–10072 (2021).
Chapter 10 — Graph neural networks¶
Xie–Grossman 2018 (CGCNN) — Xie, T.; Grossman, J. C. "Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties." Physical Review Letters 120, 145301 (2018).
Chen 2019 (MEGNet) — Chen, C.; Ye, W.; Zuo, Y.; Zheng, C.; Ong, S. P. "Graph networks as a universal machine learning framework for molecules and crystals." Chemistry of Materials 31, 3564–3572 (2019).
Choudhary–DeCost 2021 (ALIGNN) — Choudhary, K.; DeCost, B. "Atomistic line graph neural network for improved materials property predictions." npj Computational Materials 7, 185 (2021).
Chen–Ong 2022 (M3GNet) — Chen, C.; Ong, S. P. "A universal graph deep learning interatomic potential for the periodic table." Nature Computational Science 2, 718–728 (2022).
Gilmer 2017 (MPNN) — Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; Dahl, G. E. "Neural message passing for quantum chemistry." International Conference on Machine Learning (ICML), 2017.
Jain 2013 (Materials Project) — Jain, A. et al. "Commentary: The Materials Project: A materials genome approach to accelerating materials innovation." APL Materials 1, 011002 (2013).
Chapter 11 — Active learning and Bayesian optimisation¶
Mockus 1975 — Mockus, J. "On Bayesian methods for seeking the extremum." Optimization Techniques IFIP Technical Conference, Springer, 1975, pp. 400–404.
Jones 1998 (EGO) — Jones, D. R.; Schonlau, M.; Welch, W. J. "Efficient global optimization of expensive black-box functions." Journal of Global Optimization 13, 455–492 (1998).
Snoek 2012 — Snoek, J.; Larochelle, H.; Adams, R. P. "Practical Bayesian optimization of machine learning algorithms." Advances in Neural Information Processing Systems (NeurIPS), 2012.
Settles 2009 (active-learning survey) — Settles, B. "Active learning literature survey." University of Wisconsin–Madison Computer Sciences Technical Report 1648, 2009.
Lookman 2019 — Lookman, T.; Balachandran, P. V.; Xue, D.; Yuan, R. "Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design." npj Computational Materials 5, 21 (2019).
Chapter 12 — Foundation models¶
Batatia 2024 (MACE-MP-0) — Batatia, I. et al. "A foundation model for atomistic materials chemistry." arXiv:2401.00096 (2024).
Deng 2023 (CHGNet) — Deng, B. et al. "CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling." Nature Machine Intelligence 5, 1031–1041 (2023).
Park 2024 (SevenNet) — Park, S.; Kim, J.; Han, S. "Scalable parallel algorithm for graph neural network interatomic potentials in molecular dynamics simulations." Journal of Chemical Theory and Computation 20, 4857–4868 (2024).
Neumann 2024 (Orb) — Neumann, M. et al. "Orb: A fast, scalable neural network potential." arXiv:2410.22570 (2024).
Barroso-Luque 2024 (OMat24) — Barroso-Luque, L. et al. "Open Materials 2024 (OMat24) inorganic materials dataset and models." arXiv:2410.12771 (2024).
Zeni 2025 (MatterGen) — Zeni, C. et al. "MatterGen: A generative model for inorganic materials design." Nature 639, 624–632 (2025).
Xie 2022 (CDVAE) — Xie, T.; Fu, X.; Ganea, O.-E.; Barzilay, R.; Jaakkola, T. "Crystal diffusion variational autoencoder for periodic material generation." International Conference on Learning Representations (ICLR), 2022.
Jiao 2023 (DiffCSP) — Jiao, R. et al. "Crystal structure prediction by joint equivariant diffusion." Advances in Neural Information Processing Systems (NeurIPS), 2023.
Antunes 2024 (Crystal-LLM) — Antunes, L. M.; Butler, K. T.; Grau-Crespo, R. "Crystal structure generation with autoregressive large language modeling." Nature Communications 15, 10570 (2024).
Merchant 2023 (GNoME) — Merchant, A.; Batzner, S.; Schoenholz, S. S.; Aykol, M.; Cheon, G.; Cubuk, E. D. "Scaling deep learning for materials discovery." Nature 624, 80–85 (2023). [The GNoME large-scale discovery effort.]
Szymanski 2023 (A-Lab) — Szymanski, N. J. et al. "An autonomous laboratory for the accelerated synthesis of novel materials." Nature 624, 86–91 (2023).
Ko 2021 (4G-HDNNP) — Ko, T. W.; Finkler, J. A.; Goedecker, S.; Behler, J. "A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer." Nature Communications 12, 398 (2021).
Riebesell 2024 (Matbench Discovery) — Riebesell, J. et al. "Matbench Discovery — A framework to evaluate machine learning crystal stability predictions." arXiv:2308.14920 (updated 2024).
Schmidt 2019 (review) — Schmidt, J.; Marques, M. R. G.; Botti, S.; Marques, M. A. L. "Recent advances and applications of machine learning in solid-state materials science." npj Computational Materials 5, 83 (2019).
Choudhary 2022 (review) — Choudhary, K. et al. "Recent advances and applications of deep learning methods in materials science." npj Computational Materials 8, 59 (2022).
Friederich 2021 (review) — Friederich, P.; Häse, F.; Proppe, J.; Aspuru-Guzik, A. "Machine-learned potentials for next-generation matter simulations." Nature Materials 20, 750–761 (2021).
Generative-modelling background (cited in Chapter 12)¶
Sohl-Dickstein 2015 — Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; Ganguli, S. "Deep unsupervised learning using nonequilibrium thermodynamics." International Conference on Machine Learning (ICML), 2015.
Ho 2020 (DDPM) — Ho, J.; Jain, A.; Abbeel, P. "Denoising diffusion probabilistic models." Advances in Neural Information Processing Systems (NeurIPS), 2020.
Song 2021 (score-based) — Song, Y. et al. "Score-based generative modeling through stochastic differential equations." International Conference on Learning Representations (ICLR), 2021.
Austin 2021 (categorical diffusion) — Austin, J. et al. "Structured denoising diffusion models in discrete state-spaces." Advances in Neural Information Processing Systems (NeurIPS), 2021.
Radford 2021 (CLIP) — Radford, A. et al. "Learning transferable visual models from natural language supervision." International Conference on Machine Learning (ICML), 2021.
Brown 2020 (GPT-3) — Brown, T. et al. "Language models are few-shot learners." Advances in Neural Information Processing Systems (NeurIPS), 2020.
Bommasani 2021 (foundation-model report) — Bommasani, R. et al. "On the opportunities and risks of foundation models." arXiv:2108.07258 (2021).
This concludes the bibliography. The list is consolidated for ease of citation; the editors recommend that any reader pursuing research in this field maintain a personal bibliography that extends these entries with their own commentary.