Books and Courses

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2016-11-28 09:07 (updated 2025-10-17 21:34)

Books

Remember to also check our group Zotero library for folders labeled classics.

Computational modeling

Cramer, C. J. Essentials of Computational Chemistry: Theories and Models, 2nd ed.; John Wiley & Sons, 2013.

A comprehensive introduction to many topics in computational modeling: molecular mechanics force field, statistical sampling, molecular orbital and density-functional theory methods, solvation models, charge models, and more. Cramer is a fantastic teacher and he has made video recordings of his course on YouTube.

Molecular simulation and statistical mechanics

Frenkel, D.; Smit, B. Understanding Molecular Simulation: From Algorithms to Applications, 3rd ed.; Academic Press: San Diego, 2023. (available online)

(Or Allen, M. P.; Tildesley, D. J. Computer Simulation of Liquids, 2nd ed.; Oxford University Press: Oxford, United Kingdom, 2017.)

An in-depth treatment of various molecular simulation techniques.

Kärger, J.; Ruthven, D. M.; Theodorou, D. N. Diffusion in Nanoporous Materials; Wiley, 2012.

Has a very good chapter on molecular simulation and rare events sampling. Also a comprehensive treatise of adsorption equilibrium and kinetics, separation processes, and collection of data on many specific classes of porous materials, including zeolites and metal-organic frameworks.

Rapaport, D. C. The Art of Molecular Dynamics Simulation, 2nd ed.; Cambridge University Press, 2004. (available online)

Have implementation details for many common analyses.

Maitland, G. C.; Rigby, M.; Smith, E. B.; Wakeham, W. A. Intermolecular Forces: Their Origin and Determination; International series of monographs on chemistry; Clarendon Press, 1987.
Rowlinson, J. S. Liquids and Liquid Mixtures, 3rd ed.; Butterworths monographs in chemistry; Butterworth Scientific, 1982. (available online)
Hansen, J.-P.; McDonald, I. R. Theory of Simple Liquids: With Applications of Soft Matter, 4th edition.; Elsevier/AP, 2013. (available online)

Electronic-structure methods

Parr, R. G.; Yang, W. Density-Functional Theory of Atoms and Molecules; Oxford University Press, 1994. (available online)

Applied mathematics

Numerical methods

Numerical Recipes: The Art of Scientific Computing, 3rd ed.; Press, W. H., Teukolsky, S. A., Vetterling, W. T., Flannery, B. P., Eds.; Cambridge University Press, 2007. (available online)

Statistics

Vapnik, V. N. Statistical Learning Theory; Adaptive and learning systems for signal processing, communications, and control; Wiley: New York, 1998.
Efron, B.; Hastie, T. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science, 1st ed.; Cambridge University Press, 2016.
Hastie, T.; Friedman, J.; Tibshirani, R. The Elements of Statistical Learning; Springer Series in Statistics; Springer New York, 2001. (Available online)
Deisenroth, M. P.; Faisal, A. A.; Ong, C. S. Mathematics for Machine Learning; Cambridge University Press, 2020. (Available online)
Santner, T. J.; Williams, B. J.; Notz, W. I. The Design and Analysis of Computer Experiments; Springer, 2018. (Available online)
Montgomery, D. C. Design and Analysis of Experiments, 10th ed.; Wiley, 2019.
Box, G. E. P.; Draper, N. R. Response Surfaces, Mixtures, and Ridge Analyses, 2nd ed.; John Wiley, 2007.
Box, G. E. P.; Hunter, J. S.; Hunter, W. G. Statistics for Experimenters: Design, Innovation, and Discovery, 2nd ed.; Wiley series in probability and statistics; Wiley, 2005.

Networks & graphs

Easley, D.; Kleinberg, J. Networks, Crowds, and Markets: Reasoning about a Highly Connected World; Cambridge University Press, 2010. (available online)
Barabási, A.-L.; Pósfai, M. Network Science; Cambridge University Press, 2016. (available online)

Machine learning

Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; Adaptive computation and machine learning; The MIT Press: Cambridge, Massachusetts, 2016. (available online)
Murphy, K. P. Probabilistic Machine Learning: An Introduction; MIT Press, 2022 & Advanced Topics; MIT Press, 2023. (available online)
Zhang, A.; Lipton, Z. C.; Li, M.; Smola, A. J. Dive into Deep Learning; online, 2020.
Bishop, C. M. Pattern Recognition and Machine Learning; Information science and statistics; Springer: New York, 2006.
Russell, S. J.; Norvig, P. Artificial Intelligence: A Modern Approach, 4th edition.; Pearson series in artificial intelligence; Pearson, 2021.
Shalev-Shwartz, S.; Ben-David, S. Understanding Machine Learning: From Theory to Algorithms; Cambridge University Press, 2014. (available online)
C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X (Available online)
Koller, D.; Friedman, N. Probabilistic Graphical Models: Principles and Techniques; Adaptive computation and machine learning; MIT Press: Cambridge, MA, 2009.

Convolutional neural networks

Chatper 9 of Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; Adaptive computation and machine learning; The MIT Press: Cambridge, Massachusetts, 2016. (available online)
Chapter 14 of Murphy, K. P. Probabilistic Machine Learning: An Introduction; MIT Press, 2022. (available online)
Section 15.3.2 of Murphy, K. P. Probabilistic Machine Learning: Advanced Topics; MIT Press, 2023. (available online)
Chapters 6 & 7 of Zhang, A.; Lipton, Z. C.; Li, M.; Smola, A. J. Dive into Deep Learning; online, 2020.
Sections 3.2 — 6.1 of Yann LeCun’s Deep Learning (YouTube playlist).

Self-supervised learning

Chatper 15 of Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; Adaptive computation and machine learning; The MIT Press: Cambridge, Massachusetts, 2016. (available online)
Sections 19.1 — 19.3 of Murphy, K. P. Probabilistic Machine Learning: An Introduction; MIT Press, 2022. (available online)
Sections 14.1 — 14.2 of Zhang, A.; Lipton, Z. C.; Li, M.; Smola, A. J. Dive into Deep Learning; online, 2020.
Chapter 10 of Yann LeCun’s Deep Learning (YouTube playlist).

Attention mechanisms & transformers

Sections 15.4 — 15.6 of Murphy, K. P. Probabilistic Machine Learning: An Introduction; MIT Press, 2022. (available online)
Section 15.3.4 of Murphy, K. P. Probabilistic Machine Learning: Advanced Topics; MIT Press, 2023. (available online)
Chapter 10 of Zhang, A.; Lipton, Z. C.; Li, M.; Smola, A. J. Dive into Deep Learning; online, 2020.

Auto-encoders

Chatper 14 of Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; Adaptive computation and machine learning; The MIT Press: Cambridge, Massachusetts, 2016. (available online)
Section 20.3 of Murphy, K. P. Probabilistic Machine Learning: An Introduction; MIT Press, 2022. (available online)
Chapter 20 of Murphy, K. P. Probabilistic Machine Learning: Advanced Topics; MIT Press, 2023. (available online)
Sections 7.3 and 8.3 of Yann LeCun’s Deep Learning (YouTube playlist).
UFLDL’s Deep Learning Tutorial.
Jeremy Jordon’s blog articles: Introduction to autoencoders and Variational autoencoders.
Joseph Rocca’s blog article: Understanding Variational Autoencoders (VAEs).

Generative adversarial networks

Chatper 20 of Goodfellow, I.; Bengio, Y.; Courville, A. Deep Learning; Adaptive computation and machine learning; The MIT Press: Cambridge, Massachusetts, 2016. (available online)
Chapter 25 of Murphy, K. P. Probabilistic Machine Learning: Advanced Topics; MIT Press, 2023. (available online)
Chapter 17 of Zhang, A.; Lipton, Z. C.; Li, M.; Smola, A. J. Dive into Deep Learning; online, 2020.
Sections 9.2 and 9.3 of Yann LeCun’s Deep Learning (YouTube playlist).
Joseph Rocca’s blog article: Understanding Generative Adversarial Networks (GANs).
Jonathan Hui’s blog series on GANs.

Graph networks

Chapter 23 of Murphy, K. P. Probabilistic Machine Learning: An Introduction; MIT Press, 2022. (available online)
Section 15.3.5 of Murphy, K. P. Probabilistic Machine Learning: Advanced Topics; MIT Press, 2023. (available online)
Hamilton, W. L. Graph Representation Learning; Morgan & Claypool Publishers, 2020. (available online)
Chapter 13 of Yann LeCun’s Deep Learning (YouTube playlist).
Chapters 8 and 9 of White, Andrew Deep Learning for Molecules and Materials; 2022.
Sanchez-Lengeling, B.; Reif, E.; Pearce, A.; Wiltschko, A. B. A Gentle Introduction to Graph Neural Networks. Distill 2021, 6, e33.
Jonathan Hui’s blog articles: Graph Convolutional Networks (GCN) & Pooling and Graph Neural Networks (GNN, GAE, STGNN).

Reinforcement & active learning

Sections 19.4 — 19.7 of Murphy, K. P. Probabilistic Machine Learning: An Introduction; MIT Press, 2022. (available online)
Section 6.6, Supplementary Section 6.4, and Chapters 34 — 35 of Murphy, K. P. Probabilistic Machine Learning: Advanced Topics; MIT Press, 2023. (available online)
Chapter 22 of Russell, S. J.; Norvig, P. Artificial Intelligence: A Modern Approach, 4th edition.; Pearson series in artificial intelligence; Pearson, 2021.
Chapter 21 of Shalev-Shwartz, S.; Ben-David, S. Understanding Machine Learning: From Theory to Algorithms; Cambridge University Press, 2014. (available online)
Santner, T. J.; Williams, B. J.; Notz, W. I. The Design and Analysis of Computer Experiments; Springer, 2018. (Available online)

Interpretable & explainable AI

Molnar, C. Interpretable Machine Learning, 2nd ed.; Leanpub, 2018. (available online)
Sections 14.6.3 & 14.6.4 of Murphy, K. P. Probabilistic Machine Learning: An Introduction; MIT Press, 2022. (available online)
Chapter 33 of Murphy, K. P. Probabilistic Machine Learning: Advanced Topics; MIT Press, 2023. (available online)
Kaggle’s Machine Learning Explainability tutorials and a three-part blog article series (Explainability: Cracking open the black box, Part 1, Interpretability: Cracking open the black box, Part 2, Interpretability part 3: opening the black box with LIME and SHAP).

Separation

Ruthven, D. M. Principles of Adsorption and Adsorption Processes; John Wiley & Sons: New York, 1984.
Yang, R. T. Gas Separation by Adsorption Processes; Butterworths: Boston, 1987. (available online)
Yang, R. T. Adsorbents: Fundamentals and Applications; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2003. (available online)

Catalysis

Boudart, M. Kinetics of Chemical Processes; Butterworth-Heinemann, 1968.
Thomas, J. M.; Thomas, W. J. Principles and Practice of Heterogeneous Catalysis, 2nd ed.; John Wiley & Sons, 2015. (available online)

A comprehensive treatise of heterogeneous catalysis, covering fundamentals in thermodynamics and kinetics, experimental techniques, selected topics on important catalytic processes, and a chapter surveying computational methods.

Hartwig, J. F. Organotransition Metal Chemistry: From Bonding to Catalysis; University Science Books: Sausalito, Calif, 2010.

A bible in organometallic chemistry and very good for mechanistic understanding of heterogeneous catalysts.

Cotton, F. A.; Wilkinson, G.; Murillo, C.; Bochmann, M. Advanced Inorganic Chemistry, 6th ed.; Wiley, 1999.

Materials chemistry and physics

Tilley, R. J. D. Crystals and Crystal Structures; John Wiley & Sons, 2006.
Ashcroft, N. W.; Mermin, N. D.; Wei, D. Solid State Physics, Revised ed.; Cengage Learning, 2016.
Kittel, C. Introduction to Solid State Physics, 9th ed.; Wiley, 2018.

Courses

Online courses

Andrew Ng’s Machine Learning (YouTube playlist).
Geoffrey Hinton’s Neural Networks.
Yann LeCun’s Deep Learning (YouTube playlist).
Google’s Machine Learning Crash Course (with TensorFlow APIs).
Lex Fridman’s MIT Deep Learning and Artificial Intelligence Lectures (YouTube playlist)
Deeplearning.ai’s YouTube playlists
fast.ai’s Deep Learning courses
Harvard’s CS50 - Introduction to Artificial Intelligence with Python (and Machine Learning)
Kaggle’s courses

UMass courses

Chemical engineering

CHEM-ENG 690C - Catalysis
CHEM-ENG 690A - Fundamentals of Intelligent Theses (FIT), helpful for career development and submitting qualifying exam/manuscript

Mechanical and industrial engineering

M&I ENG 597E - Computational Material Science

Computer science

COMPSCI 514 - Algorithms for Data Science (w/Cameron Musco), mostly theoretical but incredibly relevant, interesting, and well-paced
COMPSCI 589 - Machine Learning

Mathematics & Statistics

MATH 552 - Applications of Scientific Computing

Blogs

Collection of blogs of researchers in Programming, Statistics, etc.