Course Catalog & Credential Pathway — Dwg. No. PF-002I
A field branch of the Construction Track, for students aimed at AI-driven discovery of superconductors, batteries, and the materials that move and store energy without loss — the same screen-then-verify architectures used in drug and materials discovery, pointed at critical temperature and ionic conductivity instead.
Field branch of Dwg. No. PF-002, The Construction Track — same core sequence through Phase II, concentrated here into energy-storage materials & superconductor discovery from Phase II onward. See also Dwg. No. PF-001, The Verification Track.
Five core strands, plus the domain strand this branch concentrates in
No frameworks yet, and no electrochemistry yet either. Two years of straight math and programming fundamentals, identical to the core Construction Track.
Precalculus, Accelerated
Compressed to clear room for calculus by grade 11 — the standard on-ramp for anyone headed toward a quantitative degree.
Programming I — Python
Variables, control flow, functions, data structures. Fluency, not tricks.
Discrete Mathematics & Logic
Sets, proofs, graphs, combinatorics — the math CS theory actually runs on, usually skipped until it's overdue.
Technical Writing for Research
Writing a clear methods section and an honest results section — the two paragraphs every paper lives or dies on.
The math and CS sequences converge into an actual first model, same as the core track. The energy-materials layer begins concurrently — by the end of Phase II a student can both train a small model and read a condensed-matter or electrochemistry paper without translation help.
Calculus I & II
Through multivariable and the gradient — backpropagation is the chain rule with bookkeeping, and it should read that way.
MATH 101
Linear Algebra
Vector spaces, eigendecomposition, matrix calculus. Every tensor operation is this course wearing a framework's syntax.
MATH 101
Probability & Statistics for ML
Distributions, estimation, Bayes — framed toward loss functions and uncertainty, not toward the social-science stats track.
Data Structures & Algorithms
Complexity analysis and the standard structures, drilled to fluency — still the baseline technical-interview bar at every AI lab.
CS 100
Intro to Machine Learning
Regression through a first neural net, built from array operations before any framework is allowed to hide the mechanics.
MATH 210, STAT 220
Research Seminar — Reading Group
Weekly seminal-paper reads (perceptron through transformers), presented and defended aloud, not just summarized.
Solid-State Physics & Superconductivity
Band structure, phonons, and BCS theory — what critical temperature physically is, so a predicted −451°F threshold means something more than a number in a table.
MATH 201 (concurrent)
Energy Storage Materials & Electrochemistry
Cathode, anode, and electrolyte chemistry; voltage, capacity, and ionic conductivity as the quantities every battery-screening model is actually predicting, sodium- and graphene-based solid-state chemistries included.
PHYS 220 (concurrent)
Here the domain degree takes over from the general CS/applied-math track. A condensed-matter physics or materials science/chemistry-plus-CS program replaces DEG 300 — the credential and peer network it buys are specific to this field.
Condensed Matter Physics / Materials Science Degree
Formal core in a condensed-matter physics or materials science/chemistry-plus-CS program — the credential and the peer network both matter here, and both are field-specific.
Superconductor & Battery Materials Screening
Structure-to-property prediction in the BEE-NET pattern — screening candidate compounds for critical temperature, stability, and DFT-confirmed superconductivity — plus the parallel screen for battery electrode and solid-state electrolyte candidates, the applied layer between ML 310's architectures and a compound a lab would actually attempt to synthesize or cycle.
ENR 230, ML 230
Deep Learning for Energy Materials Discovery
Graph neural networks and generative architectures in depth, taught directly against the systems that made them work in this field — BEE-NET's critical-temperature prediction, the kagome-lattice search that turned up YRu₃B₂ and LuRu₃B₂ — rather than as a generic survey.
ML 230
Systems for ML
Distributed training, GPU/accelerator programming, the infrastructure that turns a notebook model into one that trains at scale — the same infrastructure a 1.3-million-candidate screening run needs.
CS 210
Supervised Research Practicum
Placement inside a superconductor-discovery, battery-materials, or computational condensed-matter lab, with a mentor of record. Graded on a reproduction that actually reproduces, or a contribution that gets merged.
RES 240, ML 310
Capstone — Build & Ship
One model or tool — a critical-temperature screening pipeline, a battery-electrolyte candidate ranker, a structure-to-property dashboard — taken from idea to a deployed, load-bearing artifact with real users.
ENG 320, RES 350
No end date: the ML baseline moves every conference cycle, and the experimental-validation baseline — which candidates actually synthesize, which superconduct at the predicted threshold, which batteries survive a thousand cycles — moves on its own slower schedule that a purely computational graduate can lose track of.