Course Catalog & Credential Pathway — Dwg. No. PF-002B
A field branch of the Construction Track, for students aimed at AI-driven materials discovery — the same generative and screening architectures, pointed at crystal structures instead of language.
Field branch of Dwg. No. PF-002, The Construction Track — same core sequence through Phase II, concentrated here into materials science & autonomous 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 chemistry 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 materials-chemistry layer begins concurrently — by the end of Phase II a student can both train a small model and read a crystallography 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 & Materials Chemistry
Crystal structure, bonding, phase behavior — the vocabulary every course below this one assumes.
MATH 201 (concurrent)
Crystallography & Structure–Property Modeling
Space groups, diffraction, and how a structure's geometry predicts its stability and properties — what a GNoME-style stability score actually encodes, and why a valid crystal lattice can still be practically unsynthesizable.
CHEM 220
Here the domain degree takes over from the general CS/applied-math track. A materials science or chemistry-plus-CS program replaces DEG 300 — the credential and peer network it buys are specific to this field.
Materials Science / Chemistry Degree
Formal core in a materials science or chemistry-plus-CS program — the credential and the peer network both matter here, and both are field-specific.
Generative Screening & Autonomous Synthesis
Graph-neural-network stability screening in the style of GNoME, generative structure proposal, and the closed-loop robotic synthesis pipelines exemplified by Berkeley's A-Lab — the applied layer between ML 310's architectures and a compound a lab would actually attempt to make.
MAT 230, ML 230
Deep Learning for Materials Discovery
Graph neural networks and generative architectures in depth, taught directly against the systems that made them famous in this field — GNoME's stability screening, diffusion-based structure generation — 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 large-scale screening run needs.
CS 210
Supervised Research Practicum
Placement inside a materials-discovery or computational-chemistry 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 stability-screening pipeline, a structure-generation fine-tune, a synthesis-planning service — 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 properties actually hold — moves on its own slower schedule that a purely computational graduate can lose track of.