AI for Science Book Series — 23 本英文书

书目

共 23 本英文书，全部已排版完成。每本以可复现的端到端工作流为核心，配真实数据集与可运行代码，遵循双证据方法：机器学习筛选经 DFT 验证，并以实验数据锚定。

Machine-Learning Interatomic Potentials in Practice: From DeePMD to DPA

10 章 + 4 附录 · 348 pp

Chapter 1 MLIP Fundamentals and Ecosystem: The World of Machine-Learning Interatomic Potentials
Chapter 2 Training-Data Preparation: From DFT to the DeePMD Format
Chapter 3 The Complete DeePMD Training Workflow: From Configuration to Convergence
Chapter 4 DPA-2 and DPA-4 in Focus: Large-Scale Pretraining and Transfer Learning
Chapter 5 Model Validation and Blind Testing: How to Judge Whether a Model Is Good
Chapter 6 Active Learning and Data Iteration: Getting the Best Model with the Least DFT
Chapter 7 Model Deployment and Large-Scale MD Simulation: From "Well Trained" to "In Use"
Chapter 8 Practical Case Studies: From Data to Scientific Discovery
Chapter 9 Advanced Topics: QM/ML Hybrids, Long-Range Corrections, and Uncertainty Quantification
Chapter 10 Outlook: The Next Decade of MLIPs

Corrosion Inhibitor Computing in Practice

10 章 · 135 pp

Chapter 1 Why Corrosion Inhibitors Are So Hard to Select: A Cost Race against Rust
Chapter 2 The Computational Toolbox for Corrosion Inhibitors: DFT, MLIP, Frontier Orbitals, and MD — Each Tool for Its Own Stage
Chapter 3 Building the Curated Inhibitor Database: From Raw Sources to Audited Records — Database Construction, Grading, and an Audit That Nearly Contaminated the Whole Library
Chapter 4 A Worked Case: Whose Film Survives the Heat — Real Multi-Temperature MD Data for a Family of Corrosion Inhibitors
Chapter 5 From Single Molecule to Formulation — Synergy, Formulation Screening, and "Advice That Does Not Overstep"
Chapter 6 A Surface Is Not a Solid Slab of Iron — How First-Principles Calculation Answers "Which Piece of Iron Does the Molecule Stick To"
Chapter 7 Let AI Screen the Iron Surfaces for You — Machine-Learning Interatomic Potentials in Corrosion Systems: High-Throughput and Dual Evidence
Chapter 8 Corrosion Inhibitors Are Not the Study of "One Molecule at a Time" — The Curated Inhibitor Database Family Map and Cross-Family Computational Signatures
Chapter 9 The 50 K Life-or-Death Line — Temperature and Kinetics: Turning "Is the Film Stable" into a Computable Question
Chapter 10 What the Client Wants Is Not a Number, but a "Verifiable" Number — The Delivery Methodology of Corrosion-Inhibitor Calculation Reports

Corrosion Science Computing: DFT + Machine-Learning Workflows

9 章 · 120 pp

Chapter 1 A Panorama of Corrosion Computing: From Weight-Loss Experiments to MLIP
Chapter 2 Atomistic Foundations of Metal Surfaces and Adsorption
Chapter 3 DFT Computation of Adsorption Energies on Fe(110)/Cu(111) Surfaces
Chapter 4 Inhibitor Molecular Structure and Descriptors
Chapter 5 DPA-2.4 Zero-Training Inference: The hundreds of systems
Chapter 6 MD of Inhibitor Thermal Stability: Thermally Activated / Thermally Desorbing / Stable
Chapter 7 The Curated Inhibitor Database: Building and Using an Inhibitor Database
Chapter 8 From Computation to Decision: The Corrosion-Protection R&D Workflow
Appendix B Curated Inhibitor Database Structure and Query Paradigms

End-to-End Computation of Energy-Storage Batteries: From Materials to Cells

10 章 · 151 pp

Chapter 1 The Computational Panorama of a Battery: From the Energy of a Single Li to the Numbers on a Cell
Chapter 2 Cathode Computation: Voltage, Phase Transformation, and Doping — NMC111's Long Chase
Chapter 3 The Anode and SEI: Behind the Electrolyte's "Inevitable Decomposition", the Film Decides Lifetime
Chapter 4 Ion Transport in Electrolytes: Liquid "Palanquin-Changing" versus Solid "Site-Hopping" — How to Compute Who Is Faster, and How to Compare without Getting Tricked
Chapter 5 Full-Workflow Practice: From a Candidate Material to a Cell Design Parameter Sheet
Chapter 6 High-Ni and Si-C: How to Compute Next-Generation Systems — Nickel Buys Capacity, Not Voltage; Silicon Wins Capacity and Loses the Interface
Chapter 7 The All-Solid-State Cell: Interface Engineering and the Impedance Budget — Highest Conductivity ≠ Best C-rate
Chapter 8 Fast Charge and Heat: The Polarization Budget and C-rate — "Can It Fast-Charge?" Must Pass Four Ledgers, and Temperature Is Everyone's Enemy
Chapter 9 Long Cycling and Calendar Aging: SEI Evolution and the Boundary of Lifetime Prediction — Only Experiment Can Issue End-Point Values; Computation Hands Over the Degradation-Cause Shortlist
Chapter 10 Book-Wide Closing: From Material to Cell to System — One Master Table, One Page of Decision Tree, One Relay

AI for Science: Earth Science — From Data to the Digital Twin

13 章 · 123 pp

Chapter 1 Methodological Panorama: Four Spheres of the Earth System and Observability
Chapter 2 Climate Modeling: Parameterization Surrogates and Emulators
Chapter 3 Remote Sensing and Land-Surface Monitoring
Chapter 4 Geology and Geophysics: Earthquakes, Faults, Inversion
Chapter 5 Ocean and Extreme Events: Typhoons, El Niño, Marine Heatwaves
Chapter 6 Hydrology and Hazards: Floods, Droughts, Landslides
Chapter 7 Ecology and Biodiversity
Chapter 8 Carbon and Energy: Storage Monitoring and Resource Exploration
Chapter 9 Data Assimilation and Predictability
Chapter 10 Data Foundation and Benchmarks
Chapter 11 Cases and Engineering Practice
Chapter 12 Outlook: Earth Digital Twin
Appendix A. Engineering Discipline Checklist

AI for Science: A Practical Case Collection

7 章 · 113 pp

Chapter 0 Introduction: AI Coarse Screening + DFT Refinement + Experimental Anchoring — A Real-World Playbook for Materials Computation
Chapter 1 Adsorption Energy of CO on an Iron Surface — How AI Computes Whether a Molecule "Sticks" to a Metal
Chapter 2 Voltages of Six Li-Ion Cathode Materials — How AI Computes a Battery's "Personality"
Chapter 3 Fishing Out the Best Hydrogen Sorbents from Ten Thousand MOFs — A 12-MOF High-Throughput Screening Case (DPA Inference)
Chapter 4 Is a Perovskite Stable at Room Temperature by Itself? — Molecular Dynamics of CsPbI₃ (DPA-3.2 MD)
Chapter 5 Are Corrosion Inhibitors Afraid of Heat? Will They Still Hold at 350 K?
Chapter 6 Why Does the Computer Say SiC's Band Gap Is Only 2.4?

Electrocatalysis by Computation

9 章 · 194 pp

Chapter 1 The Electrocatalysis Panorama: A Reaction Map of HER / ORR / CO₂RR / NRR
Chapter 2 Surface Models and Active Sites (Slab / Step / Doping)
Chapter 3 Adsorption-Energy Calculations (*H/*OH/*O Intermediates)
Chapter 4 Free-Energy Diagrams and Volcano Curves (Computing ΔG)
Chapter 5 MLIP-Accelerated Electrocatalysis (DPA Long-Timescale MD / Transition States)
Chapter 6 Solvation and Field Effects (Implicit Solvation / External Electric Fields)
Chapter 7 High-Throughput Screening (Composition–Activity)
Chapter 8 The Experimental Loop (Tafel / TOF Linkage)
Chapter 9 Frontier Outlook: Interfacial MLIP and Active Learning

Battery Computing: From Electrode Materials to Cell Design

9 章 · 205 pp

Chapter 1 Battery Computing Panorama: Material–Cell–System, Three Layers
Chapter 2 Electrode-Material Thermodynamics: Voltage and Intercalation Energy (NMC Case Study)
Chapter 3 Lithium-Ion Diffusion and C-Rate (NEB/Molecular Dynamics)
Chapter 4 Interfaces and SEI Formation: The First Handshake with the Electrolyte
Chapter 5 Phase Transitions and Structural Stability: The Hidden Killer of Charge Cycling
Chapter 6 The MLIP Voltage Engine: CHGNet/MACE/DPA and Experimental-Anchor Correction
Chapter 7 Doping and Defect Engineering: Offense and Defense Against Mn Dissolution and Ni Mixing
Chapter 8 Solid-State vs. Liquid: Choosing a Route for One Cell
Chapter 9 Frontier Outlook: All-Solid-State, Sodium-Ion, and Data-Driven Battery Design

AIMD in Practice

10 章 · 105 pp

Chapter 1 Don't Just "Set a Temperature and Run": What AIMD Really Is and Why It Is Worth the Wait
Chapter 2 An AIMD That Can Actually Run: The Four-Piece Set of Initial Structure, Ensemble, Time Step, and Thermostat
Chapter 3 From Trajectory to Diffusion: The Complete MSD, Diffusion-Coefficient, and Arrhenius Activation-Energy Conversion Chain
Chapter 4 Who Is Moving? Reading the Real 10 ps Trajectory of CsPbI₃ at 300 K
Chapter 5 Industrializing AIMD: MLIP-MD Acceleration, DFT Anchors, and "Two-Evidence" Validation
Chapter 6 Do Not Mistake "5000 Frames Run" for "5000 Samples Collected": When Does a Single Trajectory Count
Chapter 7 What Can You Read by Turning the Temperature from 350 K to 400 K: Directional Reading of Two-Temperature Trajectories and the Activation Energy That Two Points Cannot Yield
Chapter 8 How Long Does It Take to Wait for One Hop: Barriers, Rare Events, and When NEB Enters
Chapter 9 Can This Trajectory Become an Experimental Observable: The MSD→D→σ Comparison Discipline
Chapter 10 Which Knife Should You Use: A Selection Decision Tree for DFT-MD, MLIP-MD, and Classical MD (Closing the Book)

Polymer Computation: From Chain Structure to Property Design

10 章 · 94 pp

Chapter 1 The Landscape of Polymer Computation: Structure–Property Relationships and Computational Entry Points
Chapter 2 Molecular Modeling: From Monomer to Chain to Crosslinked Network
Chapter 3 Force Fields and Molecular Dynamics Simulation (Gromacs/LAMMPS)
Chapter 4 Glass Transition Temperature Tg: Temperature-Ramped MD and Free Volume
Chapter 5 Mechanical Properties: Moduli, Stress–Strain and Chain Entanglements
Chapter 6 Transport and Permeation: Ion Transport, Gas Barrier and Diffusion
Chapter 7 The Challenge of MLIP in Polymers: Force-Field Accuracy for Long Chains
Chapter 8 Polymer–Filler Interfaces and Nanocomposites
Chapter 9 High-Throughput and Inverse Design: Polymer Informatics
Chapter 10 Frontiers: AI for Soft Matter, Self-Healing and Recyclability

Perovskite Solar Cell Computation

10 章 · 94 pp

Chapter 1 Why Perovskites Caught Up with Silicon in a Decade: From 3.8% to 27%, Decoded by Computation
Chapter 2 Crystal Structure ABC: Octahedra, the Tolerance Factor, and AX₃
Chapter 3 Stability from First Principles: Decomposition Energies, Defects, and Moisture
Chapter 4 MD Views How Atoms Move: The CsPbI₃ Real Case—Pb Framework Stable, I Most Mobile
Chapter 5 From Material to Cell: Interfaces, Energy Levels, and Efficiency Losses
Chapter 6 Quantifying Defect Tolerance: Why Perovskites Stay Efficient Even When "Dirty"
Chapter 7 Who Moves: A Device-Level Deep Read of the Ten-Picosecond Trajectories of CsPbI₃ and CsSn₀.₅Pb₀.₅I₃
Chapter 8 Degradation Dynamics: How Ions "Run" under Moisture, Heat, and Light
Chapter 9 Stabilization and Lead-Free Routes: The Ledger of A-, B-, and X-Site Engineering
Chapter 10 From Single Junction to Tandem: Band-Gap Matching, Defect Budgets, and the Stability Map

Semiconductor Device Computing: SiC/GaN from Band Structure to Devices

10 章 · 81 pp

Chapter 1 Why SiC and GaN Rule Power Devices: Three Parameters That Overturned Silicon's Half Century
Chapter 2 Heterojunction Band Engineering: The 2DEG "Conjured from Nothing" at the AlGaN/GaN Interface
Chapter 3 The Device-Simulation Method Chain: Coupling Discipline from Band Structure and Transport to TCAD
Chapter 4 HEMT Breakdown and Reliability Computation: The Trap Ledger behind Current Collapse
Chapter 5 From Device to System: How to Keep the Books for an 800 V Inverter and a GaN Fast Charger
Chapter 6 Breakdown and Reliability Computation for Power Devices: The Triangular Ledger of Avalanche, Critical Field, and Heat
Chapter 7 Computation for GaN HEMT RF Applications: Keeping the Books on High Frequency, Noise, and Large Signals
Chapter 8 Device-Level TCAD and Multiscale Coupling: The Seams between Band Structure, Transport, and the Device Equations
Chapter 9 MLIP and Machine-Learning-Accelerated Design for Wide-Bandgap Devices: Fast Screening That Does Not Overstep, Landing on Dual Evidence
Chapter 10 From Device to System: Closing the Whole Book on Three Touchstones

MLIP Training in Practice

10 章 · 142 pp

Chapter 1 Why MLIP: What You Lack Is Not Compute Power, but the Potential Energy Surface
Chapter 2 Where Training Data Comes From: To Build a Potential Energy Surface, First Build the Structures Right
Chapter 3 Hands-on Training: Getting DPA / CHGNet Running — Learn to Read the Loss Curve First
Chapter 4 Validation and Benchmarking: Errors Lie; Only Dual Evidence Can Be Trusted
Chapter 5 Deployment and Scaling: From "One Trained Potential" to "Six Systems per Second"
Chapter 6 Data Iteration and Post-Mortems: From 6.12 V to 3.82 V — Turning Rollovers into a Usable Data Strategy
Chapter 7 Pretrained Potentials and Transfer Learning: Zero-Shot Is Not a Master Key, but It Has a Precisely Defined Applicability Range
Chapter 8 Uncertainty, Errors, and the Trust Boundary: Giving MLIP a "How Much Can I Trust It" Dashboard
Chapter 9 MLIP-Driven Dynamics and Screening Production: From 56 Systems in 15 Seconds to "One Ruler for Static and Dynamic"
Chapter 10 The Dual-Evidence Anchor Method: The Final Closure from "Potential Energy Surface" to "Paper-Grade Conclusion"

High-Throughput Screening in Practice

10 章 · 132 pp

Chapter 1 Why High-Throughput: The "Sea" of Materials Space and the "Boat" of Experimental Trial and Error — Only After Admitting You Cannot Compute It All Do You Earn the Right to Talk Method
Chapter 2 Where Data Come From: The Curated Inhibitor Database from Zero to many thousands of Rows in Practice — Public Databases, Paper Supplementary Tables, and Three Quality Gates
Chapter 3 How to Assemble the Pipeline: Slot Management for AI Coarse Screening → DFT Refinement → Experimental Anchors — Do Not Let a Million Core-Hours Queue Up Waiting for You to Clock Out
Chapter 4 Case Study 1: Hydrogen-Storage Screening of 12 MOFs — One "Partial Failure" Is Worth More Than "All Hits"
Chapter 5 Case Study 2: Screening hundreds of Inhibitor Systems — Turning "Corrosion Protection" into Engineering with the Dual-Method Consensus on CO@Fe(110)
Chapter 6 Data-Quality Engineering: From Crash Sites to Pipeline Validation Gates — Three Things the 190 Misaligned Fields Taught Us
Chapter 7 Representation, Descriptors, and End-to-End: The Three "Spellings" of a Molecule Decide the Three Ceilings of AI — Why Inhibitor Systems Cannot Rely on Descriptor Models Alone
Chapter 8 Ranking, Grading, and Statistical Pitfalls: How Many "First Places" Hide inside hundreds of Adsorption Energies — Aggregation Levels, Small Samples, and Multiple Comparisons
Chapter 9 The Validation Loop and Failure Postmortems: Between Prediction and Trustworthiness There Are Several Gates — Turning "The AI Says" into "The Evidence Says"
Chapter 10 From Pipeline to Industrial Decision: One Page for the Decision Maker — How a Screening Report Is Closed Out So That It Counts as a Real Delivery

CO2 Capture Materials Computing

10 章 · 136 pp

Chapter 1 Why CO₂ Capture Also Has to Be "Computed" First: Three Routes, Four Families of Materials, and a Computational Coordinate System
Chapter 2 An Assessment Framework: Adsorption Energy, Selectivity, Capacity, and Regeneration — Which Part Does DFT Handle and Which Does an MLIP Handle
Chapter 3 Transplanting a Pipeline: Turning the 12-MOF Hydrogen-Storage Screening into CO₂ Screening
Chapter 4 It Only Counts If It Separates: Separation Selectivity, Membranes, and PSA/TSA Process Calculations
Chapter 5 From Material to Plant: Isotherms, Thermodynamics, and Regeneration Energy — What Scale-Up Is Computed With
Chapter 6 Sites Decide Life and Death: CO₂ Adsorption Sites and Mechanisms in MOFs — A DFT Perspective on Open Metal Sites, Amine Functionalization, and Ligand Design
Chapter 7 MOFs Are Not the Only Answer: Zeolites, Activated Carbons, Amine-Functionalized Silicas, and Perovskite-Class Materials — Four Different Computational Ledgers
Chapter 8 Teaching a Machine to Pick CO₂ Materials: MLIP Binding-Energy Prediction, Learning Curves, and Active Learning — How the Dual-Evidence Workflow Puts a Lock on AI
Chapter 9 Fishing CO₂ out of the Air: The Computational Support for Direct Air Capture (DAC) — The Thermodynamics of 420 ppm, Cyclic Energy Consumption, and a First-Pass Judgment of Material Economics
Chapter 10 From "Computing One Number" to "Computing a Production Line": Case Practice, Database Methodology, and Outlook

Defects and Doping in Solids

10 章 · 133 pp

Chapter 1 Perfect Crystals Do Not Live Long: Why Defects Decide Whether a Device Lives or Dies
Chapter 2 Computing Defect Formation Energies: The Trio of Supercell, Chemical Potential, and Charge State
Chapter 3 Doping: Donors, Acceptors, and Compensation — The Energetics of "What to Add and How Much"
Chapter 4 Deep Levels and Carrier Traps: How to Compute and How to Judge a Recombination Center
Chapter 5 Defect Engineering in Practice: The Three Cases of SiC, GaN, and Perovskite
Chapter 6 How Defects Affect Transport: Scattering, Lifetime, and Mobility
Chapter 7 Characterizing Deep-Level Defects: DLTS, PL, and Computation in Comparison
Chapter 8 Grain Boundaries and Dislocations: The Device Ledger of 1D/2D Defects
Chapter 9 Defect Computation in the MLIP Era: Big Supercells, AIMD, and the Dual-Evidence Workflow
Chapter 10 Defect-Engineering Casebook: Semi-Insulating Substrates, Buffer Layers, and Passivation

MOF Computation: From Structure to Hydrogen Storage and Adsorption

10 章 · 169 pp

Chapter 1 — Introduction to MOF Computation: From Structure to Function
Chapter 2 — MOF Structural Foundations and Computational Modeling
Chapter 3 — Framework Property Computation: Geometry Optimization, Pore Size, and Stability
Chapter 4 — Electronic Structure and Binding Mechanism
Chapter 5 — Thermodynamic Computation of Hydrogen Storage and Gas Adsorption
Chapter 6 — Adsorption Sites and Diffusion Kinetics
Chapter 7 — Machine-Learning-Potential-Accelerated MOF Simulation
Chapter 8 — High-Throughput Screening and MOF Databases
Chapter 9 — Flexible Frameworks and Ab Initio Molecular Dynamics
Chapter 10 — End-to-End Practical Workflow and Common Pitfalls

Scientific Visualization: Publication-Grade Figures and Atomic Animation

9 章 · 77 pp

Chapter 1 — The "Publication-Grade" Standard for Scientific Visualization
Chapter 2 — Data-Figure Fundamentals: Standardized matplotlib Templates
Chapter 3 — Structural Visualization: Fast Publication Figures with ASE / VESTA / OVITO
Chapter 4 — Advanced Blender Atomic Rendering [Hardcore Chapter 1]
Chapter 5 — AI Video Generation (MiniMax H3) [Hardcore Chapter 2 · Differentiator]
Chapter 6 — Reaction and MD Trajectory Animation: From Data to "Telling a Story"
Chapter 7 — Journal Cover Design: Composition and Storytelling
Chapter 8 — The Figure Automation Pipeline: Script Batching and Trio Integration
Chapter 9 — Client-Delivery Standards: How to Hand Over Figures So the Client Buys

Solid-State Electrolyte Computing: From Crystal Structure to Lithium-Ion Transport

9 章 · 146 pp

Chapter 1 — The Solid-State Electrolyte Landscape: Why Replace the Liquid?
Chapter 2 — Crystal Structure and Transport Channels: The Li Sublattice and Hopping Mechanisms
Chapter 3 — Diffusion Coefficients and Conductivity: From MSD to Arrhenius
Chapter 4 — MLIP-Accelerated Transport Calculations: The Three-Temperature LATP Case
Chapter 5 — Interfaces and Space-Charge Layers: Impedance at Solid–Solid Boundaries and How to Counter It
Chapter 6 — Stability and Decomposition Pathways
Chapter 7 — All-Solid-State Battery Assembly and Device Simulation
Chapter 8 — High-Throughput Screening: Composition–Conductivity Maps
Chapter 9 — Frontier Outlook: Halides, Sulfides, and Hybrid Routes

High-Entropy Alloys by Computation: From Composition Design to Mechanical Properties

9 章 · 152 pp

Chapter 1 — The High-Entropy Alloy Panorama: Four Core Effects and the Computational Entry Point
Chapter 2 — Composition Space and Structure Modeling: SQS and the Treatment of Disorder
Chapter 3 — DFT First-Principles Calculations: Relaxation, Energy, and Forces
Chapter 4 — MLIP-Accelerated High-Entropy Alloys: Multi-Element Prediction
Chapter 5 — The First of the Three Gates: Structural Stability (Convex Hull and Mixing Enthalpy)
Chapter 6 — The Second of the Three Gates: Mechanical-Property Calculation (Elastic Constants and Strength)
Chapter 7 — The Third of the Three Gates: Thermodynamics and Phase Stability (Entropy–Enthalpy Trade-Off)
Chapter 8 — High-Throughput Composition Screening: The DFT/MLIP Two-Engine Comparison Table
Chapter 9 — Frontier Outlook: MLIP Training, Active Learning, and the Experimental Closed Loop

AI for Science: A Learning Roadmap

7 章 · 99 pp

Chapter 1 — What Is AI for Science: From "AI-Assisted Research" to "AI-Driven Discovery"
Chapter 2 — The Core Toolchain: DFT, MD, MLIP, and High-Throughput
Chapter 3 — The Learning Path: Zero to Independent Research
Chapter 4 — Your First Cases: Water, Methane, and Silicon
Chapter 5 — Pitfalls and How to Avoid Them: From Non-Convergence to Data Labeling
Chapter 6 — Resource Index: Courses, Software, Databases, and Communities
Chapter 7 — Outlook: The Boundaries and Future of AI4S

Wide-Band-Gap Semiconductor Computing: SiC/GaN

9 章 · 99 pp

Chapter 1 Wide Band Gap Semiconductor Panorama: SiC/GaN Markets and Computational Entry Points
Chapter 2 Band Structure Calculations: PBE vs HSE06, and a Brief Note on GW
Chapter 3 Defect and Doping Chemistry: Formation Energy and Migration Barriers
Chapter 4 Surface and Epitaxial Interfaces: CVD/MOCVD Surface Reactions
Chapter 5 Process Optimization: Surrogate Models + NSGA-II (4H-SiC CVD Case)
Chapter 6 Carrier Transport and Device Properties: Mobility/Breakdown
Chapter 7 Heterojunctions and Power Devices: GaN-on-SiC
Chapter 8 Data-Driven Wide Band Gap: Bandgap Databases and ML Prediction
Chapter 9 Experiment Closed Loop and Frontier Outlook

Computational Materials Science in Practice

29 章 + 1 附录 · 1025 pp

Chapter 1 A Panoramic View of Computational Materials Science: Scales, Methods, and Workflows
Chapter 2 Foundations of Quantum Mechanics: Wave Functions, Operators, and Approximation Methods
Chapter 3 Density Functional Theory (DFT): From the Kohn–Sham Equations to Runnable Input Files
Chapter 4 Statistical Mechanics and Ensembles: The Bridge from the Microscopic to the Macroscopic
Chapter 5 Advanced DFT Theory: Kohn–Sham Equations, the Functional Hierarchy, DFT+U, and vdW
Chapter 6 VASP in Practice: From INCAR to Band-Structure Plots (with a QE Cross-Reference Migration)
Chapter 7 CP2K in Practice: Fast DFT and Ab Initio Molecular Dynamics (AIMD)
Chapter 8 Convergence Tests and Reproducibility Standards: From "It Runs" to "It Is Trustworthy"
Chapter 9 Structural Relaxation and Mechanical Properties: From the Potential-Energy Surface to Elastic Constants
Chapter 10 Phonon Calculations and Thermodynamic Properties
Chapter 11 Surface and Interface Calculations
Chapter 12 Free-Energy Calculation Methods: From Thermodynamic Integration to Umbrella Sampling
Chapter 13 Getting Started with Materials Studio and Its Open-Source Alternatives
Chapter 14 Gaussian and ORCA: Quantum Chemistry in Practice for Molecular Systems
Chapter 15 LAMMPS Modeling and Potential Functions
Chapter 16 MD Production Simulations and Trajectory Analysis
Chapter 17 Enhanced Sampling and Rare Events: Escaping the Energy Wells
Chapter 18 Monte Carlo Methods and the Theory of Statistical Sampling
Chapter 19 GROMACS in Practice: Biomolecular and Polymer Systems
Chapter 20 Foundations of Machine-Learned Interatomic Potentials: From Linear Potentials to Neural-Network Potentials
Chapter 21 MLIP Training in Practice and Deployment
Chapter 22 Materials Informatics and Data-Driven Research
Chapter 23 High-Throughput Computational Workflows
Chapter 24 Cross-Scale Methods and Phase-Field Simulation
Chapter 25 CALPHAD Phase Diagrams and Thermodynamic Databases
Chapter 26 An Introduction to Finite Elements and Micromechanics Simulation
Chapter 27 Project Practice: The Complete Workflow from Problem to Paper
Chapter 28 Publication-Grade Figures and Review-Proofing
Appendix A Quick Reference of Unit Systems, Physical Constants, and Engineering Parameters

实用计算材料科学 · AI for Science 书系

书目