Molecular Networking - Delhommelle, Jerome

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Résumé :

Section I Molecular networking analytics

Chapter 1 Probabilities, distributions and statistics
1.1 MECHANICS
1.1.1 Newton, Lagrange, and Hamilton
1.1.2 Wave function and uncertainty
1.1.3 Quantum Energy and Density of States
1.2 THERMODYNAMICS
1.2.1 Processes, Work, and Heat
1.2.2 First, Second and Third Laws
1.2.3 Changing Conditions: Legendre Transformations
1.3 STATISTICS AND DISTRIBUTIONS
1.3.1 Maxwell-Boltzmann distribution
1.3.2 Phase space and probability distribution
1.3.3 Micro-Macro Connection

Chapter 2 Communication Rules in Molecular Systems
2.1 COMMUNICATION AND INTERACTIONS
2.1.1 Interactions in a Quantum World
2.1.2 Coarse-graining: Tight-Binding
2.1.3 Further coarse-graining: a classical world
2.2 INTERACTIONS BETWEEN MOLECULES
2.2.1 Molecular Properties and Interactions
2.2.2 2-Body vs. Many-Body Potentials
2.2.3 Towards Macro- and Bio-molecules
2.3 BEYOND INTERACTIONS
2.3.1 Signaling
2.3.2 Phoresis and Active Matter
2.3.3 Chemotaxis

Chapter 3 An Ensemble Approach: Finding descriptors and reducing dimensions
3.1 COLLECTIONS AND ENSEMBLES
3.1.1 Making Sense of the Microscopic Big Data
3.1.2 Defining Ensembles
3.1.3 The Concept of the Most Probable Distribution
3.2 INDIVIDUALS IN AN ISOTHERMAL WORLD: THE CANONICAL ENSEMBLE
3.2.1 Key Parameters and Multipliers
3.2.2 The Central Partition Function
3.2.3 Partition Function and Thermodynamics
3.3 INDIVIDUALS IN ISOLATION: THE MICROCANONICAL ENSEMBLE
3.3.1 Number and Density of States
3.3.2 Boltzmann's Entropy
3.3.3 Thermodynamic Functions

Chapter 4 Accounting for Individual Features and Changes
4.1 MOLECULES IN A CANONICAL WORLD
4.1.1 Features and Consequences
4.1.2 The Case of Diatomic Molecules
4.1.3 Molecular Symmetry and Polyatomic Molecules
4.2 CONNECTING WITH THE MACROSCOPIC WORLD
4.2.1 Are all Features Essential?
4.2.2 Model-Partition Function Interplay
4.2.3 Thermodynamic properties and Ideality
4.3 CHANGING IDENTITIES: CHEMICAL REACTIONS
4.3.1 Reaction properties and Parameters
4.3.2 Partition Functions and Equilibrium Constants
4.3.3 The Activated Complex

Chapter 5 Machine Learning and Molecular Systems
5.1 DISTINGUISHING FROM THE MOLECULAR CROWD
5.1.1 Labels and Classes
5.1.2 Identifying and Handling Patterns
5.1.3 Learning under supervision
5.2 QUANTITATIVE MODELS FOR MOLECULAR GROUPS
5.2.1 Training regression models
5.2.2 Mapping numbers: Artificial Neural Networks
5.2.3 Optimization through back-propagation
5.3 BEYOND ARTIFICIAL NEURAL NETWORKS
5.3.1 Learning by watching: Convolutional Neural Networks
5.3.2 Time sequences and Recurrent Neural Networks
5.3.3 Understanding policies: the Advent of Reinforcement Learning

Section II Static trends: equilibrium statistics

Chapter 6 Polling a molecular population: Monte Carlo and Wang Landau simulations
6.1 THE BIRTH OF THE MONTE CARLO METHOD
6.1.1 Randomness and Integration
6.1.2 Sample Mean Approach
6.1.3 The Concept of Importance Sampling
6.2 THE METROPOLIS METHOD
6.2.1 Markov Chain and Stochastic Matrix
6.2.2 Randomness and Acceptance
6.2.3 Implementation and Testing
6.3 WANG-LANDAU SAMPLING
6.3.1 A Paradigm Shift: Evaluating the Density of States
6.3.2 The Biased Distribution
6.3.3 A Twist in the Monte Carlo plot

Chapter 7 Molecular networking in insulation: adiabatic ensembles
7.1 ADIABATIC PROCESSES AND ENSEMBLES
7.1.1 Adiabatic vs. Isothermal
7.1.2 The Concept of Heat Function
7.1.3 Eight S...

Sommaire:
The book builds on an analogy between social groups and assemblies of molecules to introduce the concepts of statistical mechanics, machine learning and data science. Applications to systems at the cutting-edge of research e.g. environmental and energy applications, will be used....