Ziqi Wang
PhD candidate, Mechanical Engineering and Scientific Computing, University of Michigan
I work at the intersection of computational materials science and agentic AI systems. I build multi-agent systems that run the physics simulations behind materials discovery autonomously on supercomputers. Because a correct answer can hide fabricated reasoning, I also build the verification systems that catch agents fabricating results. I was selected for Anthropic’s AI for Science program. I am seeking applied research scientist roles and graduate in summer 2026.
Headline results
- 0.2% error on adsorption-energy differences between preferred sites, using the same exchange-correlation functional (CO/Pt(111))
- Below 1% error on lattice-constant calculations across 27 elemental bulk materials spanning 3 crystal structures (Sol27LC)
- Up to 3x token reduction for the lightweight DREAMS framework compared to baseline frameworks
- Worker error rate and judge success rate are currently under benchmarking
- DREAMS-OER catalyst exploration is currently running; result analysis is preliminary
Projects
Resume
Trustworthy scientific LLM agent researcher. First author of DREAMS.
Open full resume (PDF) ↗Education
2021 – 2026
Ph.D., Mechanical Engineering and Scientific Computing
Carnegie Mellon University and University of Michigan
Advisor: Prof. Venkatasubramanian Viswanathan
2016 – 2021
B.S., Mechanical Engineering and B.S., Computer Science
University of Michigan and Florida Institute of Technology
Research experience
arXiv 2025
DREAMS first author
Fully autonomous LLM agent that runs expert-level materials simulations
- Reached human-expert accuracy on multi-step autonomous workflows: reproduced a long-debated surface-chemistry benchmark (CO/Pt(111)) to within 0.2% and matched expert structures on all 27 systems of a crystal benchmark, where a single-agent baseline failed every run and a multi-agent baseline missed by 389%.
- Canvas: a shared communication-and-memory scheme for long-horizon, complex runs, with report-based history compression suited to scientific workflows, giving finer control over agent context than general-purpose agent frameworks.
- Deterministic and LLM-based safety guards that vouch for each result's credibility, validating every parameter setting against customizable rule levels (R1, R2, ...) and user-defined sensitive parameters.
- Full traceability and white-box transparency: every result is registered with its inputs and outputs, verified by reference ID (ref_id), and linked to its sources through a provenance DAG, with each claim backed by a context-aware reasoning chain annotated with those IDs.
- Enables recursive, deep debugging of long, multi-step runs.
- Benchmarked against representative single-agent and multi-agent frameworks, reporting accuracy, success rate, and token usage (cost), using up to roughly 3x fewer tokens than the baseline framework on long-horizon tasks.
In preparation
DREAMS-OER
Autonomous agent for open-ended catalyst discovery over a roughly 380,000-material space
- Scaled the agent from a single fixed task to open-ended search over roughly 380,000 candidate materials (Google DeepMind's GNoME set), choosing what to study next (material, surface, site, three intermediates) from accumulated evidence rather than a fixed plan. Demonstrated on behavioral runs; production screening in progress.
- Built a multi-level relational experiment log, enabling efficient information retrieval and progress tracking across hundreds of partial, interdependent studies.
- Gave the agent live time- and compute-budget awareness to submit work opportunistically, keep the cluster busy while reasoning, and re-plan under pressure, completing a bounded 7-hour study without overrunning.
- Hardened it against scale-only failure modes: a disposition gate enforcing genuine engagement with each result (removing a queue-occupancy gaming pattern), a runaway-loop guard, retrieval-augmented literature grounding, and required hypothesis and limitation logging, including a "too credulous" failure now being addressed.
In preparation
Non-linear Material-Property Prediction with Machine-Learning Interatomic Potentials
- Built a committee-based active-learning loop to fine-tune several universal ML interatomic potentials (MACE, NequIP, GRACE, MatterSim) into accurate, low-cost surrogates for the Li-Mg alloy system, cutting prediction error on target physical properties by roughly 5x versus off-the-shelf models.
- Showed that weighting training toward higher-order (stress) information, not just energy, most improves accuracy on hard second-derivative properties, with the gain transferring across multiple properties.
- Mapped how alloying produces non-linear excess effects in stiffness and atomic-transport barriers across composition, quantifying behavior that cannot be inferred from the pure end-members alone.
Publications and preprints
2025
Z. Wang, H. Huang, H. Zhao, C. Xu, S. Zhu, J. Janssen, V. Viswanathan. "DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation." arXiv:2507.14267 (2025).
Technical skills
Agentic AI / LLM
Multi-agent system design, harness design, SKILL design, guardrail and provenance systems, agent evaluation and benchmarking, LangGraph, LangChain
Machine learning
PyTorch, machine-learning interatomic potentials (MACE, NequIP, MatterSim, GRACE, SchNet), graph neural networks, active learning
Atomistic simulation
DFT (Quantum ESPRESSO), ASE, molecular dynamics (LAMMPS), Monte Carlo, nudged elastic band, Bayesian error estimation (BEEF)
Computing
Python, C++, HPC / SLURM, Git
Honors and awards
2026
Selected for the Anthropic AI for Science Program
2019 – 2021
University Honors, University of Michigan