Molecular Networking

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.

The book builds on the analogy between social groups and assemblies of molecules to introduce the concepts of statistical mechanics, machine learning and data science. Applying a data analytics approach to molecular systems, we show how individual (molecular) features and interactions between molecules, or "communication" processes, allow for the prediction of properties and collective behavior of molecular systems - just as polling and social networking shed light on the behavior of social groups. Applications to systems at the cutting-edge of research for biological, environmental, and energy applications are also presented.

Key features:

• Draws on a data analytics approach of molecular systems
• Covers hot topics such as artificial intelligence and machine learning of molecular trends
• Contains applications to systems at the cutting-edge of research for biological, environmental and energy applications
• Discusses molecular simulation and links with other important, emerging techniques and trends in computational sciences and society

• Authors have a well-established track record and reputation in the field

  Autorentext

  Dr. Caroline Desgranges received a DEA in Physics in 2005 from the University Paul Sabatier-Toulouse III (France) and a PhD in Chemical Engineering from the University of South Carolina (USA) in 2008. She is currently a Research Assistant Professor in Physics & Applied Physics at the University of Massachusetts Lowell.

Dr. Jerome Delhommelle did his undergraduate studies at the Ecole Normale Superieure Paris-Saclay and received his PhD in Chemistry from the University of Paris-Saclay (France) in 2000. He is currently an Associate Professor in Chemistry at the University of Massachusetts Lowell.

Inhalt

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 Statistical Ensembles
7.2 MECHANICS OF ADIABATIC ENSEMBLES
7.2.1 Microcanonical distribution and thermodynamic equations
7.2.2 The ( , P,R) Ensemble
7.2.3 A Full Picture for the Four Adiabatic Ensembles
7.3 MONTE CARLO EXPLORATION OF ADIABATIC ENSEMBLES
7.3.1 Exploring the Microcanonical Ensemble
7.3.2 Musing in the (N, P,H) Ensemble
7.3.3 Direct Entropy Evaluations in the ( , P,R) Ensemble

Chapter 8 Networking under one (or more) cues: isothermal ensembles
8.1 THERMAL AND CHEMICAL CUES
8.1.1 The Grand-Canonical Ensemble
8.1.2 Monte Carlo Exploration
8.1.3 Grand Partition Function Determination
8.2 THERMAL AND MECHANICAL CUES
8.2.1 The Isothermal-Isobaric Ensemble
8.2.2 Properties Calculations
8.2.3 Partition Function Computation
8.3 VARIATIONS AND APPLICATIONS
8.3.1 Multi-Component Systems and Semi-Grand Approach
8.3.2 A First Step towards Coexistence: Gibbs Ensemble Monte Carlo
method
8.3.3 Recycling and Reweighting

Chapter 9 Collective properties from partition functions
9.1 GENERATING DATA ON PARTITION FUNCTIONS
9.1.1 Starting from A
9.1.2 From dilute to condensed phases
9.1.3 Direct determination of partition functions
9.2 THE CASE OF PHASE TRANSITIONS
9.2.1 Matching Probabilities
9.2.2 Features of coexistence
9.2.3 Extension to Multi-Component Systems
9.3 GAS STORAGE AND SEPARATION APPLICATIONS
9.3.1 Partition Functions for Adsorbed Fluids
9.3.2 Thermodynamic Properties of Adsorption
9.3.3 Environmental and Energy Applications

Chapter 10 Machine Learning Molecular Trends
10.1 LEARNING INTERMOLECULAR INTERACTIONS
10.1.1 Starting from empirical datasets
10.1.2 Training on tight-binding data
10.1.3 Neural network potentials
10.2 LEARNING PARTITION FUNCTIONS
10.2.1 Single-component systems
10.2.2 Multicomponent mixtures
10.2.3 Adsorbed Phases
10.3 LEARNING TRANSITIONS
10.3.1 Spanning Pathways
10.3.2 From Partition Functions to Reaction Coordinates
10.3.3 On-The-Fly Learning of Collective Variables

Section III Dynamic trends: motion statistics

Chapter 11 Molecular evolution and fluctuations: time-resolved statistics
11.1 COMPUTING MOLECULAR TRAJECTORIES
11.1.1 Ensemble and Time Averages Equivalency
11.1.2 Molecular Equation…