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Materials, Nano & Molecular Engineering

  • Exit capability

    Move from quantum/chemical mechanisms to computational screening, characterization, processing, interfaces, reliability, and manufacturable materials.

  • Frontier targets

    Superconductors; batteries; catalysts; smart/self-healing materials; nanotech; high-temperature structures.

  • Choose this track if

    Best if you want to create new physical capabilities rather than assemble known ones.

Electronic structure

Week 1: Solid-state/quantum refresh

Reading/source: David Tong solid-state notes + MIT 3.091 electronic materials

Know: Connect crystal symmetry and electronic structure to observable material properties.

Reconstruct: Derive reciprocal-lattice/band-filling intuition and density-of-states role.

Do: Compute/plot tight-binding bands for 1D/2D toy lattices.

Context: Materials design starts with structure/electrons but must end in measurement.

Defend: Which predicted property is most sensitive to model approximation?

Gate: Pass if qualitative band behavior is predicted before computation.

Source: source

Thermodynamics

Week 2: Phase stability and chemical potentials

Reading/source: MIT 3.091 + Materials Project thermodynamic methodology

Know: Use free energies, convex hulls and chemical potentials to reason about phase stability.

Reconstruct: Derive binary convex-hull stability and lever/chemical-potential intuition.

Do: Build hull from toy formation energies; identify metastability margin.

Context: Materials Project exposes computed thermodynamic stability at scale.

Defend: Why can a metastable phase still be manufacturable/useful?

Gate: Pass if equilibrium prediction is separated from kinetic accessibility.

Source: source

Defects

Week 3: Defects, doping and nonstoichiometry

Reading/source: MIT 3.091 defects/diffusion

Know: Understand vacancies/interstitials/substitution, charge compensation and defect-controlled properties.

Reconstruct: Derive Arrhenius/Boltzmann defect concentration and diffusion length.

Do: Simulate defect concentration/diffusion versus T and process time.

Context: Many functional materials are engineered through controlled imperfection.

Defend: When does a 'defect' become the desired feature?

Gate: Pass if process conditions map to defect/property target.

Source: source

Transport

Week 4: Electronic, ionic and thermal transport

Reading/source: Solid-state + transport methods

Know: Connect carriers/scattering/phonons/ions to conductivity and heat flow.

Reconstruct: Derive Drude-like conductivity scaling and diffusion-mobility relationship.

Do: Compare electronic/ionic/thermal transport tradeoffs for battery/thermoelectric toy materials.

Context: High performance often requires contradictory transport properties.

Defend: Which transport mechanism is rate limiting at device scale?

Gate: Pass if material metric is translated into device consequence.

Source: source

Computation

Week 5: Density-functional-theory workflow literacy

Reading/source: Materials Project methodology/workflows

Know: Understand what DFT predicts, approximations, relaxation, energy/band/elastic workflows and systematic error.

Reconstruct: Reconstruct Kohn-Sham workflow conceptually without pretending to derive DFT from scratch.

Do: Use Materials Project data to compare computed properties across a chemical family.

Context: MP publishes standardized high-throughput calculation workflows and methodologies.

Defend: What error comes from functional/model versus database/data processing?

Gate: Pass if every downloaded property includes method/version caveat.

Source: source

Week 6: Materials Project API and reproducible data

Reading/source: Materials Project API getting started

Know: Programmatically query structures/properties and preserve provenance/database version.

Reconstruct: Write schema for material_id, structure, property, uncertainty/method metadata.

Do: Build reproducible search pipeline for candidate battery/catalyst/semiconductor materials.

Context: MP warns users to consider benchmarking/systematic errors and database versions.

Defend: How could database selection bias distort your candidate ranking?

Gate: Pass if query/version/source are reproducible from clean environment.

Source: source

Week 7: Descriptors and surrogate models

Reading/source: Materials informatics methods + Wave-1 ML

Know: Build property models from composition/structure descriptors and quantify extrapolation.

Reconstruct: Derive train/validation split strategy by chemical family rather than random rows.

Do: Train baseline property predictor; compare random split vs composition-held-out split.

Context: Materials ML is especially vulnerable to leakage from chemically similar samples.

Defend: Does your model interpolate chemistry or discover new chemistry?

Gate: Pass if OOD split is materially harder and reported.

Source: source

Week 8: Active learning and Bayesian optimization

Reading/source: Wave-1 probability/optimization + materials search

Know: Select next computation/experiment based on uncertainty and expected improvement.

Reconstruct: Derive Gaussian-process/expected-improvement intuition or equivalent uncertainty-guided search.

Do: Search toy composition space with active learning versus random screening.

Context: Closed-loop materials discovery depends on informative experiment selection.

Defend: What if your uncertainty model is confidently wrong?

Gate: Pass if acquisition is stress-tested on misspecified surrogate.

Source: source

Characterization

Week 9: Diffraction and structure determination

Reading/source: MIT 3.091 crystal/XRD materials

Know: Infer structure from diffraction and understand ambiguity/resolution.

Reconstruct: Derive Bragg law and reciprocal-space peak relation.

Do: Generate/index synthetic XRD patterns with noise/mixtures.

Context: Predicted crystal structure means little without characterization.

Defend: What distinct structures could produce similar patterns?

Gate: Pass if uncertainty/multiphase alternatives are considered.

Source: source

Week 10: Electrical/magnetic/thermal measurements

Reading/source: Materials characterization principles + MP property methodology

Know: Design measurements that discriminate mechanisms, not merely produce one number.

Reconstruct: Regenerate four-probe/contact-resistance distinction and temperature/field sweep logic.

Do: Design superconductivity/semiconductor conductivity experiment in simulation with contact artifacts.

Context: Frontier material claims frequently fail at measurement interpretation.

Defend: What measurement would falsify your favored mechanism?

Gate: Pass if at least two independent observables are proposed.

Source: source

Week 11: Microscopy/spectroscopy and multiscale evidence

Reading/source: MIT 3.091 + characterization survey

Know: Connect spatial/chemical/structural probes to microstructure/property claims.

Reconstruct: Map measurement resolution/volume to defect length scales.

Do: Create characterization plan for grain-boundary-controlled material.

Context: No single instrument sees 'the material'; each samples scales/contrasts.

Defend: What feature could be invisible to your chosen measurement?

Gate: Pass if cross-validation across modalities is designed.

Source: source

Replication

Week 12: Reproduce a Materials Project trend

Reading/source: MP API + methodology

Know: Verify a computed materials trend and its methodological caveats.

Reconstruct: Rebuild candidate-selection criteria and one computed descriptor.

Do: Replicate published/database trend across a material family and compare to available experiment.

Context: MP exists to accelerate materials discovery through public computed property data.

Defend: Where is computation systematically biased?

Gate: Replication Gate: notebook + provenance + experimental cross-check where available.

Source: source

Electrochemistry

Week 13: Battery materials and interfaces

Reading/source: Chem/materials core + MP battery-relevant properties

Know: Connect voltage, capacity, diffusion, phase stability and interfaces to cell behavior.

Reconstruct: Derive intercalation voltage from free-energy difference intuition.

Do: Screen cathode/electrolyte candidates; build simple cell-level energy/degradation model.

Context: Battery gains require interfaces, safety and manufacturing beyond bulk energy density.

Defend: Which bulk-property improvement is erased at cell/pack level?

Gate: Pass if candidate is evaluated at material and device levels.

Source: source

Catalysis

Week 14: Catalysts and surface energetics

Reading/source: MP surface/adsorption methodology + physical chemistry

Know: Understand adsorption, activation barriers, selectivity and catalyst stability.

Reconstruct: Derive Sabatier-volcano qualitative tradeoff.

Do: Create toy catalyst screening with activity/selectivity/stability multiobjective score.

Context: Catalyst discovery is constrained optimization over competing mechanisms.

Defend: Why is strongest binding usually not best?

Gate: Pass if selectivity and degradation are not omitted.

Source: source

Functional materials

Week 15: Semiconductors, dielectrics, piezoelectrics, magnetics

Reading/source: Materials Project property methodology

Know: Map band gaps, dielectric/piezo/magnetic properties to devices.

Reconstruct: Derive simple device figure of merit from material constants.

Do: Query MP for a functional-material family and construct Pareto frontier.

Context: Materials databases expose many computed device-relevant properties.

Defend: Which figure of merit is incomplete at device/manufacturing scale?

Gate: Pass if screening includes stability/abundance constraints.

Source: source

Frontier

Week 16: Superconductivity and extraordinary claims

Reading/source: Solid-state theory + rigorous characterization

Know: Understand signatures/constraints without pretending current theory predicts high-Tc materials reliably.

Reconstruct: Regenerate zero-resistance vs Meissner distinction and critical field/current concepts.

Do: Write preregistered verification protocol for claimed ambient superconductor.

Context: The target is scientifically open; evidence standards must be unusually strong.

Defend: What mundane artifact could mimic each claimed signature?

Gate: Pass if protocol includes independent replication and sample provenance.

Source: source

Processing

Week 17: Processing-structure-property

Reading/source: MIT 3.091 phase/kinetics + Wave-2 manufacturing

Know: Treat synthesis/annealing/deposition as state-control of microstructure.

Reconstruct: Derive diffusion/process-time scaling and nucleation/growth intuition.

Do: Optimize a virtual heat-treatment/process schedule for target microstructure/property.

Context: A computational candidate that cannot be synthesized reproducibly is not a technology.

Defend: Which process variable controls variance rather than mean property?

Gate: Pass if process window and measurement plan are specified.

Source: source

Scale

Week 18: Manufacturability, supply and reliability

Reading/source: Wave-2 manufacturing + materials constraints

Know: Integrate yield, purity, critical minerals, recycling and lifetime.

Reconstruct: Compute material intensity per GWh/million devices and sensitivity to yield.

Do: Scale candidate material to 10k/1M units; identify precursor/purity/thermal/process bottleneck.

Context: Materials innovation can shift bottlenecks into supply chains.

Defend: What happens if the rarest element becomes 10× more expensive?

Gate: Pass if substitute/recycling path is evaluated.

Source: source

Research

Week 19: Failure and uncertainty map

Reading/source: All track work

Know: Rank computational, synthesis, characterization and lifetime uncertainties.

Reconstruct: Construct Bayesian/causal dependency graph from composition/process -> structure -> property -> device.

Do: Run sensitivity/value-of-information analysis to choose next measurement.

Context: High leverage often comes from resolving uncertainty, not searching more candidates.

Defend: What experiment most changes your decision?

Gate: Pass if next step is chosen quantitatively.

Source: source

Week 20: Reproduce a current materials paper/workflow

Reading/source: Choose open computational/experimental paper + MP data

Know: Practice reproducible materials science.

Reconstruct: Reconstruct structure, method, characterization and claimed mechanism.

Do: Replicate computed trend or public data analysis; challenge with alternate functional/split/metric.

Context: Current materials research is increasingly data/workflow intensive.

Defend: Which conclusion survives method changes?

Gate: Extension Gate: reproduction + one countermodel/alternative explanation.

Source: source

Week 21: Closed-loop discovery prototype

Reading/source: MP API + surrogate/active-learning stack

Know: Integrate candidate generation, prediction, uncertainty and acquisition.

Reconstruct: Regenerate decision loop and stopping rule.

Do: Build autonomous computational materials-discovery loop on bounded search space.

Context: This is a safe analog of an autonomous lab before real synthesis.

Defend: How do you detect that the loop is exploiting model/database artifacts?

Gate: Pass if hidden 'ground truth' benchmark exposes and measures exploitation.

Source: source

Week 22: Mechanism-focused extension

Reading/source: Selected material system

Know: Move beyond leaderboard property prediction to mechanistic hypothesis.

Reconstruct: Write mechanism + discriminating observation.

Do: Add descriptor/physics constraint/measurement simulation testing mechanism.

Context: Industry-leading materials work couples prediction to explanatory measurement.

Defend: What observation would force a different mechanism?

Gate: Pass if hypothesis makes risky prediction.

Source: source

Capstone

Week 23: New-material design dossier

Reading/source: All track sources

Know: Produce candidate -> computation -> synthesis concept -> characterization -> device -> scale chain.

Reconstruct: Reconstruct full processing-structure-property-performance argument.

Do: Capstone material family for battery/catalyst/semiconductor/smart material with reproducible screening.

Context: No 'miracle material' claim without evidence ladder.

Defend: Which link in the chain is least verified?

Gate: Systems Gate: computational + experimental + manufacturing reviewers.

Source: source

Week 24: Research program and kill criteria

Reading/source: All sources

Know: Define experiments and milestones that could rapidly falsify the program.

Reconstruct: Write top hypotheses, predicted signatures, null models and value-of-information ordering.

Do: Paper/notebook + candidate database + characterization plan + scale/cost/supply model + 12-month roadmap.

Context: Good materials programs kill bad candidates early.

Defend: What result makes you stop working on this material?

Gate: Capstone Gate: defend novelty, evidence, manufacturability and falsifiability.

Source: source

Research gates

G1 Replication

Required performance: Reproduce computed/experimental material trend with provenance and method sensitivity.

Minimum artifacts: Notebook; database version; structures; method; uncertainty; experimental cross-check.

Pass criterion: Random row split alone is not adequate ML evidence.

G2 Extension

Required performance: Test a mechanism or improve candidate selection under realistic OOD/process constraints.

Minimum artifacts: Competing mechanism predictions; OOD split; active-learning/measurement plan.

Pass criterion: Must include a risky discriminating prediction.

G3 System Closure

Required performance: Close composition->process->structure->property->device->manufacturing/supply chain.

Minimum artifacts: Characterization ladder; process window; device model; yield/material-intensity model.

Pass criterion: A computed property without synthesis/measurement path fails.

G4 Research Defense

Required performance: Propose fastest sequence of experiments that kills bad candidates and validates good ones.

Minimum artifacts: Candidate dossier; characterization plan; scale/cost/supply; 12-month roadmap.

Pass criterion: Must state exact observation that ends the program.

Frontier technologies primarily routed here