Table of Contents

From vibe coding as a rigorous engineering discipline to autonomous AI scientists.

Alexander Apartsin & Yehudit Aperstein, 2026

7 parts · 58 chapters, plus front matter, 7 appendices, and six capstone tracks. The Discovery Workbench platform grows through every chapter.

Front Matter

7 entries
  1. F1
    ForewordDiscovery as the common thread across software engineering, scientific research, and autonomous AI.
  2. F2
    What This Book CoversThe seven-part arc: foundations, vibe coding, data and models, knowledge, simulation, domains, and autonomous systems.
  3. F3
    Who Should Read This BookThree personas: the software engineer building research tools, the scientist who wants rigorous theory and working code, and the domain expert adding AI to a scientific workflow.
  4. F4
    How to Use This BookReading paths for graduate research, software practitioner, and domain scientist audiences; how the parts depend on each other.
  5. F5
    About the AuthorsAlexander (Sasha) Apartsin, Ph.D. and Yehudit Aperstein, Ph.D.
  6. F6
    Copyright & LegalEdition, license, and attribution.
  7. F7
    About the Hands-On AI Science SeriesThe nine-book Hands-On AI Science series and where this volume fits.

Part I · Foundations of Discovery AI

6 chapters

Discovery as search, inference, optimization, and experimentation. The scientific, mathematical, and architectural foundations all later chapters build on.

  1. 1
    Discovery as Search Search spaces, objective functions, utility, regret, information gain, exploration versus exploitation, and the role of AI in accelerating search.
    part-1-foundations/module-01-discovery-as-search/
  2. 2
    Scientific Discovery and Knowledge Creation Scientific method, falsification, reproducibility, Bayesian science, measurement, uncertainty, and construct validity.
    part-1-foundations/module-02-scientific-discovery/
  3. 3
    Knowledge Representation Logic, ontologies, knowledge graphs, embeddings, vector databases, and hybrid retrieval.
    part-1-foundations/module-03-knowledge-representation/
  4. 4
    Reasoning for Discovery Deductive, inductive, abductive, analogical, and causal reasoning; chain-of-thought in language models; AlphaGeometry and formal mathematical reasoning.
    part-1-foundations/module-04-reasoning-for-discovery/
  5. 5
    Discovery Through Data, Models, and Simulation Data-driven, theory-driven, and simulation-driven discovery; surrogate models, digital twins, and active learning.
    part-1-foundations/module-05-data-models-simulation/
  6. 6
    Discovery System Architecture The Discovery Workbench: artifact graphs, provenance, human approvals, sandboxes, cost, and observability.
    part-1-foundations/module-06-system-architecture/

Part II · Software Engineering & Vibe Coding

18 chapters

Vibe coding as a rigorous discipline: specification, steering, verification, and repair. From a first coding agent to fully autonomous software organizations.

  1. 7
    Software Development as a Discovery Process SDLC as iterative hypothesis testing; agile, lean, and design thinking as discovery methodologies.
    part-2-software-engineering/module-07-software-discovery/
  2. 8
    Foundations of AI Assisted Software Engineering Code foundation models, program synthesis, repository-level reasoning, and human-AI collaboration patterns.
    part-2-software-engineering/module-08-ai-software-foundations/
  3. 9
    Vibe Coding as Specification, Steering, Verification, and Repair Natural language specification, intent elicitation, steering by tests and artifacts, verification, and the formalized vibe coding loop.
    part-2-software-engineering/module-09-vibe-coding/
  4. 10
    Prompting to Programming Structured outputs, tool definitions, typed agent interfaces, DSPy optimization, and reusable agent components.
    part-2-software-engineering/module-10-prompting-to-programming/
  5. 11
    Context Engineering at Repository Scale Repository graphs, chunking strategies, retrieval-augmented code generation, context compression, and agent memory.
    part-2-software-engineering/module-11-context-engineering/
  6. 12
    Building MCP Servers for Scientific Workflows Model Context Protocol architecture, tool and resource definitions, scientific tool integration, testing, and publishing.
    part-2-software-engineering/module-12-mcp-servers/
  7. 13
    Discovery of Requirements Stakeholder goals, AI-assisted requirement extraction, validation, traceability, and conflict detection.
    part-2-software-engineering/module-13-requirements-discovery/
  8. 14
    Discovery of Architectures Architectural styles, design patterns, AI-assisted architecture generation, tradeoff analysis, and fitness functions.
    part-2-software-engineering/module-14-architecture-discovery/
  9. 15
    Discovery of Algorithms Algorithm search, complexity analysis, program synthesis, and benchmarking methodology.
    part-2-software-engineering/module-15-algorithm-discovery/
  10. 16
    AI Assisted Implementation at Repository Scale Multi-file code generation, impact analysis, change propagation, patch review, and failure patterns.
    part-2-software-engineering/module-16-ai-implementation/
  11. 17
    Multi-Agent Software Teams Agent roles, workflow graphs, shared state, debate and critique patterns, human approval gates, and failure recovery.
    part-2-software-engineering/module-17-multi-agent-teams/
  12. 18
    AI Assisted Testing and QA Test generation, property-based testing, invariant discovery, mutation testing, and evaluation of AI-generated tests.
    part-2-software-engineering/module-18-ai-testing/
  13. 19
    AI Assisted Debugging Hypothesis-driven debugging, root cause analysis, trace-driven reproduction, self-debugging agents, and error budgets.
    part-2-software-engineering/module-19-ai-debugging/
  14. 20
    AI for Software Security Threat modeling, secure SDLC, vulnerability discovery, AI-assisted security review, and sandboxing coding agents.
    part-2-software-engineering/module-20-software-security/
  15. 21
    AI for DevOps and Platform Engineering CI/CD pipelines, observability, infrastructure as code, incident analysis, and deployment automation.
    part-2-software-engineering/module-21-devops/
  16. 22
    MLOps, LLMOps, and AgentOps Data pipelines, model lifecycle, prompt versioning, agent trace logging, governance, and rollback.
    part-2-software-engineering/module-22-mlops-llmops/
  17. 23
    Evaluating AI Coding Agents SWE-bench tasks, repository task suites, human review, benchmark validity, cost, latency, and reliability.
    part-2-software-engineering/module-23-evaluating-agents/
  18. 24
    Autonomous Software Organizations SWE agents, autonomous software factories, issue triage, review and release pipelines, and self-improving systems.
    part-2-software-engineering/module-24-autonomous-software/

Part III · Data and Models

11 chapters

Scientific foundation models, multimodal AI, reasoning models, and generative models for molecular design. The machine learning toolkit of the AI scientist.

  1. 25
    Exploratory Discovery Exploratory data analysis, clustering, dimensionality reduction, pattern discovery, and hypothesis generation from data.
    part-3-data-models/module-25-exploratory-discovery/
  2. 26
    Representation Learning Self-supervised and contrastive learning, latent geometry, foundation model embeddings, and probing evaluation.
    part-3-data-models/module-26-representation-learning/
  3. 27
    Scientific Foundation Models Protein, molecular, materials, and genomics foundation models; ESM-3, AlphaFold3, GNoME, Uni-Mol2; fine-tuning with LoRA; scaling laws for scientific domains.
    part-3-data-models/module-27-foundation-models/
  4. 28
    Multimodal Scientific AI Molecule-text, protein-text, and vision-language models; cross-modal retrieval; PaperQA2; multimodal hypothesis generation.
    part-3-data-models/module-28-multimodal-scientific-ai/
  5. 29
    Reasoning Models for Discovery Chain-of-thought, test-time compute, process reward models, MCTS over reasoning, AlphaProof, FunSearch, and formal theorem proving with Lean4.
    part-3-data-models/module-29-reasoning-models/
  6. 30
    Anomaly and Novelty Discovery Density-based, isolation-based, and deep anomaly detection; open-world learning; drift detection; separating errors from discoveries.
    part-3-data-models/module-30-anomaly-novelty/
  7. 31
    Causal Discovery and Causal Inference Structural causal models, causal discovery algorithms, treatment effects, do-calculus, counterfactuals, and double ML.
    part-3-data-models/module-31-causal-discovery/
  8. 32
    Bayesian Discovery and Uncertainty Bayesian workflow, MCMC, variational inference, posterior predictive checking, model comparison, and experimental design.
    part-3-data-models/module-32-bayesian-discovery/
  9. 33
    Scientific Machine Learning PINNs, neural operators, equivariant networks, universal force fields, hybrid models, and differentiable simulation.
    part-3-data-models/module-33-scientific-ml/
  10. 34
    Generative Models for Discovery VAEs, diffusion, flow matching, equivariant architectures, graph generation, protein-ligand cofolding, and property-guided sampling.
    part-3-data-models/module-34-generative-models/
  11. 35
    Symbolic Regression and Equation Discovery Genetic programming, neural-guided symbolic regression, dimensional constraints, interpretability, and parsimony.
    part-3-data-models/module-35-symbolic-regression/

Part IV · Knowledge

6 chapters

Literature at scale, retrieval-augmented discovery, knowledge graphs, hypothesis generation, and research agents. The full knowledge infrastructure for AI-assisted science.

  1. 36
    Literature Mining Citation analysis, semantic search, knowledge extraction, claim and hypothesis extraction, and domain mapping.
    part-4-knowledge/module-36-literature-mining/
  2. 37
    Retrieval Augmented Discovery Systems RAG architecture, research copilots, evidence grounding, PaperQA2, and citation verification.
    part-4-knowledge/module-37-rag-discovery/
  3. 38
    Knowledge Graph Discovery Link prediction, graph embeddings, GNNs for knowledge graph completion, ontology enrichment, and relation extraction.
    part-4-knowledge/module-38-knowledge-graphs/
  4. 39
    Hypothesis Generation Gap analysis, analogical transfer, AI-generated hypotheses, scoring by plausibility, novelty, and testability.
    part-4-knowledge/module-39-hypothesis-generation/
  5. 40
    Research Agents Literature, coding, analysis, and reviewer agents; Coscientist, ChemCrow, AI Co-Scientist; memory and long-horizon planning.
    part-4-knowledge/module-40-research-agents/
  6. 41
    Scientific Claim Validation Claim extraction, evidence mapping, citation checking, artifact verification, and reproducibility auditing.
    part-4-knowledge/module-41-claim-validation/

Part V · Simulation and Optimization

6 chapters

Differentiable programming, scientific simulation, world models, Bayesian optimization, automated experiment design, and provenance. The computational machinery of discovery.

  1. 42
    Differentiable Programming for Discovery Automatic differentiation, JAX transforms, differentiable physics, adjoint methods, and differentiable molecular dynamics.
    part-5-simulation-optimization/module-42-differentiable-programming/
  2. 43
    Scientific Simulation Agent-based models, Gillespie algorithm, molecular dynamics, synthetic data, and approximate Bayesian computation.
    part-5-simulation-optimization/module-43-scientific-simulation/
  3. 44
    World Models for Discovery Recurrent and transformer world models, counterfactual reasoning, model-based planning, and uncertainty estimation.
    part-5-simulation-optimization/module-44-world-models/
  4. 45
    Optimization for Discovery Bayesian optimization, evolutionary algorithms, reinforcement learning, multi-objective Pareto frontiers, and expensive function optimization.
    part-5-simulation-optimization/module-45-optimization/
  5. 46
    Automated Experiment Design Active learning, sequential design, bandit algorithms, adaptive trials, closed-loop optimization, and stopping rules.
    part-5-simulation-optimization/module-46-experiment-design/
  6. 47
    Experiment Registries and Scientific Provenance Experiment registry design, artifact versioning, data lineage, configuration management, and reproducible report generation.
    part-5-simulation-optimization/module-47-experiment-registries/

Part VI · Scientific Domains

5 chapters

Deep application chapters: the AI toolkit deployed in biology, chemistry, physics, climate science, and social systems. Domain models, benchmarks, and end-to-end recipes.

  1. 48
    Discovery AI for Biology and Medicine Drug discovery, AlphaFold, de novo protein design, single-cell genomics, clinical AI, and validation constraints.
    part-6-scientific-domains/module-48-biology-medicine/
  2. 49
    Discovery AI for Chemistry and Materials De novo molecular generation, protein-ligand cofolding, reaction prediction, crystal design, universal force fields, and autonomous labs.
    part-6-scientific-domains/module-49-chemistry-materials/
  3. 50
    Discovery AI for Physics and Engineering Symbolic regression for physics laws, PINNs, neural operators, engineering optimization, and simulation-based inference.
    part-6-scientific-domains/module-50-physics-engineering/
  4. 51
    Discovery AI for Climate and Earth Science AI weather prediction, climate emulation, extreme event detection, carbon accounting, and uncertainty quantification.
    part-6-scientific-domains/module-51-climate-earth/
  5. 52
    Discovery AI for Social and Economic Systems Market discovery, network analysis, behavioral modeling, causal inference in social data, and policy simulation.
    part-6-scientific-domains/module-52-social-economic/

Part VII · Autonomous Discovery

6 chapters

AI scientists, multi-agent discovery teams, self-driving laboratories, system evaluation, governance, and the research frontier of autonomous science.

  1. 53
    AI Scientists Automated research loops; AI Scientist v1 and v2, Coscientist, AI Co-Scientist, FunSearch; human supervision and research integrity.
    part-7-autonomous-discovery/module-53-ai-scientists/
  2. 54
    Multi-Agent Discovery Systems Scientific agent teams, debate and adversarial critique, peer review simulation, collective intelligence, and coordination mechanisms.
    part-7-autonomous-discovery/module-54-multi-agent-discovery/
  3. 55
    Self-Driving Laboratories Laboratory robotics, closed-loop experimentation, ChemOS, digital-physical interfaces, and safety constraints.
    part-7-autonomous-discovery/module-55-self-driving-labs/
  4. 56
    Evaluating Discovery Systems ScienceAgentBench, DiscoveryBench, MLE-bench, SWE-bench, GPQA Diamond, FrontierMath; construct validity and benchmark saturation.
    part-7-autonomous-discovery/module-56-evaluating-discovery/
  5. 57
    Responsible Discovery AI Safety, dual-use risks, scientific integrity, alignment, governance, publication ethics, and regulatory frameworks.
    part-7-autonomous-discovery/module-57-responsible-discovery/
  6. 58
    Future Directions Autonomous innovation, human-AI co-discovery, AI research organizations, open problems, and scientific institutions in the agent era.
    part-7-autonomous-discovery/module-58-future-directions/

Appendices

7 appendices
  1. A
    Mathematical FoundationsProbability, statistics, information theory, optimization, linear algebra, graph theory, Bayesian inference, causal inference, and experimental design.
  2. B
    Python for Discovery AIScientific Python, reproducible notebooks, command-line tools, packaging, testing, type checking, and data pipelines.
  3. C
    Vibe Coding Practitioner GuideSpecifying intent, steering by tests, inspecting diffs, recovering from bad changes, context engineering, and red-teaming prompts.
  4. D
    Discovery AI Tool EcosystemCoding agents, agent frameworks, scientific foundation model registry, retrieval systems, optimization libraries, and generative model zoo.
  5. E
    Research Paper Reading GuideHow to read a paper with AI assistance: extracting claims, checking assumptions, mapping evidence, and using PaperQA2.
  6. F
    Reproducibility and Artifact GuideData versioning, environment capture, experiment registries, result tables, claim-to-artifact traceability, and reproducibility packages.
  7. G
    Discovery AI Capstone ProjectsSix capstone tracks: graduate research, software practitioner, scientific computing, knowledge systems, generative discovery, and multimodal science.