resources.md

July 11, 2023 · View on GitHub

Learning resources for fundamental AI or Science fields (open-source denotes that the code is publicly available and further development is permitted).

Note that this table is by no means complete and only consists of a small set of available resources.

	Fundamental AI/Science	Type	Description
Symposiums/Conferences	APS	Physics	American Physical Society
	ACS	Chemistry	American Chemical Society
	MRS	Materials	Materials Research Society
	AIChE	Chemistry	American Institute of Chemical Engineers
	NeurIPS	AI	Neural Information Processing System
	ICLR	AI	Intl. Conf. on Learning Representations
	ICML	AI	Intl. Conf. on Machine Learning
	AAAI	AI	AAAI Conference on Artificial Intelligence
Courses	Computational Biology	Biology	-
	Quantum Physics	Physics	-
	Machine Learning	AI	-
	Deep Learning	AI	-
	Theoretical Chemistry	Chemistry	-
	Mechanical Engineering Analysis	Engineering	-
Software & Library	PySCF	Quantum Chemistry	Open-Source Quantum Chemistry Code
	PSI4	Quantum Chemistry	Open-Source Quantum Chemistry Code
	NWChem	Quantum Chemistry	Open-Source Quantum Chemistry Code
	CP2K	Quantum Chemistry	Open-Source Quantum Chemistry Code
	ORCA	Quantum Chemistry	Quantum Chemistry Code
	GAUSSIAN	Quantum Chemistry	Quantum Chemistry Code
	Q-Chem	Quantum Chemistry	Quantum Chemistry Code
	Quantum-ESPRESSO	First-Principles	Open-Source Electronic Structure Code
	ABINIT	First-Principles	Open-Source Electronic Structure Code
	GPAW	First-Principles	Open-Source Electronic Structure Code
	BerkeleyGW	First-Principles	Open-Source Electronic Structure Code
	WEST	First-Principles	Open-Source Electronic Structure Code
	Octopus	First-Principles	Open-Source Electronic Structure Code
	exciting	First-Principles	Open-Source Electronic Structure Code
	SIESTA	First-Principles	Open-Source Electronic Structure Code
	OpenMX	First-Principles	Open-Source Electronic Structure Code
	ABACUS	First-Principles	Open-Source Electronic Structure Code
	Wannier90	First-Principles	Open-Source Electronic Structure Code
	EPW	First-Principles	Open-Source Electronic Structure Code
	WIEN2k	First-Principles	Electronic Structure Code
	VASP	First-Principles	Electronic Structure Code
	FHI-aims	First-Principles	Electronic Structure Code
	pymatgen	Materials	Open-Source Python Library for Materials Analysis
	ASE	Materials	Open-Source Python Library for Atomistic Simulations
	JARVIS-Tools	Materials	Software Package for Atomistic Data-Driven Materials Design
	PAOFLOW	Materials	Open-Source Code for Post-Processing First-Principles Calculations
	XtalOpt	Materials	Open-Source Crystal Structure Search Code
	CALYPSO	Materials	Crystal Structure Search Code
	USPEX	Materials	Crystal Structure Search Code
	AIRSS	Materials	Crystal Structure Search Code
	PyMOL	Atomistic	Molecular Visualization Software
	RDKit	Cheminformatics	Open-Source Cheminformatics Software
	OpenBabel	Cheminformatics	Open-Source Cheminformatics Software
	AutoDock Vina	Cheminformatics	Open-Source Molecular Docking
	OpenMM	Molecular Dynamics	Open-Source Molecular Simulation Package
	GROMACS	Molecular Dynamics	Open-Source Molecular Simulation Package
	Amber	Molecular Dynamics	Molecular Simulation Package
	LAMMPS	Molecular Dynamics	Open-Source Molecular Simulation Package
	MDAnalysis	Molecular Dynamics	Open-Source Python Library for Molecular Dynamics Trajectory Analysis
	Rosetta	Biology	Protein Structure Analysis
	Biotite	Biology	Open-Source Python Library for Computational Molecular Biology
	Biopython	Biology	Open-Source Python Library for Biological Computation
	ScanPy	Biology	Open-Source Python Library for Single-Cell Analysis
	PyClaw	Partial Differential Equations	Open-Source Finite Volume Numerical Solvers for PDE in Python

Learning resources for AI for Science.

Note that this table is by no means complete and only consists of resources commonly used by the authors.

	AI for Science	Type	Description
Workshops	AI4Science	General	AI for Science
	ML4PS	General	Machine Learning for Physical Sciences
	NSF AI4Science	General	AI-Enabled Scientific Revolution
	MLSB	Atomistic	Machine Learning for Structural Biology
	ML4Molecules	Atomistic	Machine Learning for Molecules
	AI4Mat	Atomistic	AI for Acc. Materials Design
	AIMS	Atomistic	Artificial Intelligence for Materials Science
	SimDL	Continuum	Deep Learning for Simulation
Symposiums/Conferences	AAAI Spring Symposium	General	Comp. Approaches to Scientific Discovery
	MoML	Atomistic	Molecular ML Conference
Research Institutes and Labs	IPAM	General	Institute for Pure & Applied Math. at UCLA
	CUAISci	General	Cornell University AI for Science Institute
	AI4Science	General	AI for Science Initiative at Caltech
	AI4ScienceLab	General	AI for Science Lab at UvA
	A3D3	General	Acc. AI Algo. for Data-Driven Discovery
	IAIFI	General	Institute for AI and Fundam. Interactions
	AI & Science	General	AI & Science Initiative at UChicago
	Molecule Maker Lab Institute	Atomistic	AI Institute for Molecule Discovery and Synthesis
	AI Institute in Dynamic Systems	Continuum	-
Tutorials and Blogs	AI4Science101 Blog Series	General	-
	AI4Science Tutorial Series	General	-
	Deep Learning and Quantum Many-Body Computation	Quantum	-
	Tutorial on Quantum Many-body problem	Quantum	-
	Neural Operator	Continuum	-
	Physics-Informed Neural Networks	Continuum	-
Reading Groups and Seminars	Scientific ML Webinar	General	Scientific Machine Learning Webinar Series
	AI4Science Seminar	General	AI for Science Seminar at Chalmers
	M2D2 Reading Group	Atomistic	Molecular Modeling& Drug Discovery
Courses	Data-driven Science and Engineering	General	-
	Group Equivariant Deep Learning	General	-
	Symmetry and its application to ML	General	-
	AI for Science Summer School	General	AI for Science Summer School at UChicago
	Crash Course on Neural Operators	Continuum	-
Software and Libraries	E3NN	General	Machine Learning and Symmetry Library
	DIG	General	Geometric Deep Learning Library
	NetKet	Quantum	Machine Learning for Quantum Physics
	DeepChem	Atomistic	Machine Learning for Molecules
	TDC	Atomistic	Machine Learning for Therapeutic Molecules
	DeePMD	Atomistic	Deep Learning Interatomic Potential and Force Field
	M2Hub	Atomistic	Machine Learning for Materials Discovery
	Jax CFD	Continuum	Machine Learning for Computational Fluid Dynamics
	$\Phi_{\text{Flow}}$	Continuum	Open-source Python PDE solver compatible with popular deep learning frameworks
Competitions and Benchmarks	Open Catalyst Project	Atomistic	Discover New Catalyst
	Open Graph Benchmark	Atomistic	Molecular Property Prediction
	PDEArena	Continuum	Operator Learning
	PDEBench	Continuum	Operator Learning
Review Papers	Machine Learning and Physical Sciences	General	-
	Quantum Chemistry in the Age of Machine Learning	Quantum	-
	Roadmap on Machine learning in electronic structure	Quantum	-
	Physics-Guided Deep Learning for Dynamical System	Continuum	-

Recommended books for fundamental AI, Science and AI for Science fields.

Note that this table is by no means complete and only consists of resources commonly used by the authors.

Title	Author	Domain
Deep Learning	Ian Goodfellow, Yoshua Bengio, and Aaron Courville	AI
Pattern Recognition and Machine Learning	Christopher M. Bishop and Nasser M. Nasrabadi	AI
Machine Learning: A Probabilistic Perspective	Kevin P. Murphy	AI
Advanced Engineering Mathematics	Erwin Kreyszig	Mathematics
The Feynman Lectures on Physics: The New Millennium Edition	Richard Feynman, Robert Leighton, and Matthew Sands	Physics
Group Theory in a Nutshell for Physicists	Anthony Zee	Group Theory
Group Theory: Application to the Physics of Condensed Matter	Mildred S. Dresselhaus, Gene Dresselhaus, and Ado Jorio	Group Theory
Group Theory in Quantum Mechanics: An Introduction to Its Present Usage	Volker Heine	Group Theory
An Introduction to Tensors and Group Theory for Physicists	Nadir Jeevanjee	Group Theory
Symmetry Principles in Solid State and Molecular Physics	Melvin Lax	Group Theory
Introduction to Quantum Mechanics	David J. Griffiths and Darrell F. Schroeter	Quantum Mechanics
Modern Quantum Mechanics	J. J. Sakurai and J. Napolitano	Quantum Mechanics
Quantum Theory of Angular Momentum	D. A. Varshalovich, A. N. Moskalev, and V. K. Khersonskii	Quantum Mechanics
Fundamentals of Condensed Matter Physics	Marvin L. Cohen and Steven G. Louie	Quantum Theory
Quantum Theory of Materials	Efthimios Kaxiras and John D. Joannopoulos	Quantum Theory
Electronic Structure: Basic Theory and Practical Methods	Richard M. Martin	DFT and Quantum Chemistry
Density-Functional Theory of Atoms and Molecules	Robert G. Parr and Weitao Yang	DFT and Quantum Chemistry
Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory	Attila Szabo and Neil S. Ostlund	DFT and Quantum Chemistry
A Primer in Density Functional Theory	Carlos Fiolhais, Fernando Nogueira, and Miguel A. L. Marques	DFT and Quantum Chemistry
Density Functional Theory: An Advanced Course	Eberhard Engel and Reiner M. Dreizler	DFT and Quantum Chemistry
Density Functional Theory: An Approach to the Quantum Many-Body Problem	Reiner M. Dreizler and Eberhard K. U. Gross	DFT and Quantum Chemistry
Interacting Electrons: Theory and Computational Approaches	Richard M. Martin, Lucia Reining, and David M. Ceperley	DFT and Quantum Chemistry
A Chemist's Guide to Density Functional Theory	Wolfram Koch and Max C. Holthausen	DFT and Quantum Chemistry
Materials Modelling using Density Functional Theory	Feliciano Giustino	DFT and Materials Modeling
Handbook of Materials Modeling	Sidney Yip	Materials Modeling
A Physical Introduction to Fluid Mechanics	Alexander J. Smits	Fluid Mechanics
Lectures in Fluid Mechanic	Alexander J. Smits	Fluid Mechanics
Turbulent Flows	Stephen B. Pope	Fluid Mechanics
Turbulence, Coherent Structures, Dynamical Systems and Symmetry	Philip Holmes, John L. Lumley, Gahl Berkooz, and Clarence W Rowley	Fluid Mechanics
Introduction to Partial Differential Equations	Peter J. Olver	PDE
Partial Differential Equations	Lawrence C. Evans	PDE
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges	Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković	AI & Geometry
Data-driven Science & Engineering: Machine learning, dynamical systems, and control	Steven L. Brunton and J. Nathan Kutz	AI & Engineering
Deep Learning for Molecules & Materials	Andrew D. White	AI & Atomistic