--design_space=lattice_opt_v4
--optimizer=bayesian_active
--target=multi_objective_pareto
Accelerating materials
discovery at scale.
Groundstate Systems builds the computational substrate for next-generation R&D. We compress the timeline from theoretical candidate to validated material.
The Computational
Matter Stack
We replace ad-hoc intuition with systematic exploration. Our engine treats materials discovery as a search problem over high-dimensional design spaces.
Design Space
Define vast chemical and structural search spaces using our declarative constraints API.
node_count: 10^6
High-Throughput Screening
Parallelized simulation kernels evaluate thousands of candidates simultaneously across compute clusters.
throughput: 5k/hr
Unified Data Lake
Every simulation result, failure, and success is structured, indexed, and queryable forever.
storage: persistent
Active Learning
Our models learn from every iteration, intelligently guiding the search toward optimal regions.
model: gaussian_proc
Why Computational
Design Matters
Modern industrial demands outpace traditional trial-and-error discovery. The complexities of new energy storage, carbon capture, and aerospace alloys require a fundamental shift in methodology.
Groundstate Systems provides the rigor of software engineering applied to atomic scale problems. We don't just find materials; we verify their existence and viability through reproducible computational evidence.
Collaboration Models
Flexible engagement structures designed for commercial and research partners.
Joint Development
Co-development partnerships for specific material targets. We deploy our engineers and compute resources to solve your defined problem.
> team.assign(engineers=[gnd_01, gnd_04])
> status: active
"The next era of material science will not be defined by serendipitous discovery, but by deterministic, computational search."