AI-Accelerated Materials Discovery
UnderationAI discovers novel materials for defense and extreme-environment applications — ceramics, coatings, high-entropy alloys, and structural composites — using autonomous AI screening, first-principles DFT validation, and molecular dynamics simulation. Target property envelope in, validated candidate shortlist out.
0
Autonomous Discovery
Iterations Completed
0
Neural Network Models
Screening Every Candidate
0
Hardening Pipeline Steps
Per Candidate
0
Known Reference Materials
in Benchmark Set
The Problem
Developing a new defense material takes 10–15 years and costs $50M+. Legacy ceramics, coatings, and structural alloys are decades-old technology operating near their theoretical limits. The mission can't wait for the next breakthrough to come from trial and error.
From initial concept to fielded production, a new defense material takes over a decade and $50M+ in R&D. The failure rate exceeds 95%.
Multi-component ceramics, high-entropy alloys, and novel nitrides/carbides represent a vast, unexplored design space. Over 10⁶ possible compositions — too many for brute-force testing.
Hypersonic platforms, directed energy systems, and next-generation threats demand materials with properties no existing material can deliver. The pace of evolution demands faster materials innovation.
How It Works
Our platform autonomously generates, screens, validates, and hardens novel material compositions — compressing years of R&D into weeks of computation.
Three generators — crystal structure prediction, high-entropy alloy explorer, and composition explorer — produce hundreds of novel candidates per iteration from a configurable element palette spanning ceramics, borides, carbides, nitrides, coatings, and refractory alloys.
MACE, ALIGNN, MatterSim, and MEGNet evaluate every candidate on GPU for hardness, elastic modulus, fracture toughness, and density. Thousands screened per iteration — only the most promising survive.
GPU-accelerated Quantum ESPRESSO performs first-principles calculations: formation energy, thermodynamic stability (energy above hull), stress tensor, and equilibrium pressure. Ground-truth physics, not approximations.
LAMMPS molecular dynamics with the MACE universal potential simulates high-strain-rate compression, shock propagation, and thermal loading. Dynamic mechanical response and failure analysis — for any element combination, without pre-fit potentials.
Configurable scoring indices evaluate candidates across mechanical merit, weight, manufacturability, thermal stability, and cost. Candidates are ranked against known reference materials in application-specific benchmark sets tailored to each campaign.
An 8-step hardening pipeline — tight relaxation, DFT elastic tensor, multistructure polymorph search, MD simulation, phonon stability, thermal AIMD, evidence bundle, benchmark ranking — produces cryptographically signed evidence packages ready for contract delivery and patent filing.
Live System Preview
Real-time dashboard running on dedicated GPU infrastructure. Compositions shown below are simulated examples — actual discoveries are proprietary.
45
Obs This Iter
250
Total Obs
22
DFT Validated
0.23
Rank ρ (Spearman)
Compositions that cleared all internal checks and are not in any known database or structural family. Strongest IP candidates — pending formal prior-art search. Only compounds with formation energy < 0 eV/atom appear here.
| # | Composition | Hardness (GPa) | E_form | Ehull | Crystal | ACI | Source | Status |
|---|---|---|---|---|---|---|---|---|
| 1 | ZrHfBN2 | 31.4 | -3.217 eV/atom | 8 meV | P6₃/mmc | 72 | Iter 5 | HIGH_PRIORITY_IP |
| 2 | TiAlCrN3 | 28.7 | -2.943 eV/atom | 14 meV | Fm3̅m | 67 | Iter 4 | HIGH_PRIORITY_IP |
| 3 | SiB4C2N | 34.1 | -2.811 eV/atom | 21 meV | R3̅m | 58 | Iter 6 | HIGH_PRIORITY_IP |
| 4 | WMoTiC3 | 26.3 | -2.654 eV/atom | 43 meV | Pm3̅m | 54 | Iter 3 | HIGH_PRIORITY_IP |
| 5 | HfTaNbN2 | 29.8 | -2.519 eV/atom | 11 meV | P4/mmm | 63 | Iter 5 | HIGH_PRIORITY_IP |
| 6 | VCrAlBN3 | 25.1 | -2.387 eV/atom | 37 meV | I4/mmm | 51 | Iter 7 | HIGH_PRIORITY_IP |
| 7 | NbZrSiC2O | 22.9 | -2.201 eV/atom | 52 meV | Cmcm | 47 | Iter 2 | HIGH_PRIORITY_IP |
10
Element Boosts
12
Plausibly Novel
2
Acquisition Targets
3
Confirmed Hypotheses
Simulated data shown for demonstration. Actual discoveries are proprietary and under IP review.
Why UnderationAI
Six generators propose novel crystal structures. An ML ensemble (MACE, ALIGNN, MatterSim, MEGNet) screens in seconds. GPU-accelerated Quantum ESPRESSO validates with DFT physics. LAMMPS simulates extreme mechanical and thermal loading. A 6-objective Pareto optimizer decides what to explore next. Every discovery gets a cryptographically signed evidence bundle. The entire loop runs autonomously on dedicated GPU hardware — no human in the loop, no cloud dependencies.
GA-CSP evolves structures across 8 crystal prototypes (HCP, BCC, FCC, corundum, spinel, rutile, rocksalt, B4C-rhomb). A composition explorer samples from a configurable palette of defense-relevant elements. A substitution engine mutates DFT-confirmed hits. HEA, DiffCSP, and MatterGen generators provide additional diversity. Every iteration proposes 32 novel candidates.
Quantum ESPRESSO runs on an RTX 5090 with CUDA Fortran — full PBE relaxation, spin-polarized calculations for magnetic transition metals, formation energy, elastic tensor, and bulk/shear/Young's modulus. Each DFT calculation provides ground-truth physics that trains the ML models to be more accurate next iteration.
Every hardened candidate gets a cryptographically signed evidence package: SHA-256 content hashes, Ed25519 digital signatures, per-metric provenance labels (DFT-computed, ML-predicted, elastic-derived), readiness scores, and evidence confidence ratings. Contract-deliverable and patent-ready.
LAMMPS with the MACE-MP-0 universal potential simulates extreme mechanical and thermal loading for any element combination — no pre-fit potentials needed. Dynamic compressive strength, failure analysis, and configurable scoring indices answer “will this survive the mission environment?” computationally, before any lab time.
Before spending GPU time on DFT, every composition is checked against 12M+ entries across Materials Project, AFLOW, OQMD, NOMAD, arXiv, and CrossRef. Structural family classification catches obvious variants. Only compositions that survive all 5 tiers proceed to validation — we only pursue materials that are genuinely novel.
The entire stack — ML models, DFT engine, LLM agent, knowledge store — runs on dedicated GPU hardware with no cloud dependencies. The local LLM provides agent guidance (element boosts, hypothesis testing) without sending data to third-party APIs.
Results to Date
The platform has been running autonomously on dedicated GPU hardware since early 2026. These are real numbers from a live system — not projections.
9
Novelty-Screened
Candidate Compounds
No matches in Materials Project, OQMD, AFLOW, or open literature.
130+
Autonomous Discovery
Iterations Completed
Each iteration: generate, screen, validate, learn, repeat.
GPU
Accelerated DFT
on RTX 5090
CUDA Fortran QE. 4 parallel DFT jobs. 99% GPU utilization.
6
Structure Generators
Running in Parallel
GA-CSP, HEA, composition explorer, substitution, DiffCSP, MatterGen.
191+
DFT observations
recorded
8
Crystal prototypes
explored
28
Defense-relevant
elements
12M+
Known compounds
checked per candidate
Evidence System
We don't just discover materials — we produce defensible evidence packages suitable for contract deliverables, patent filings, and defense prime technical review.
Rapid neural network predictions. Broad coverage, explicitly provisional. Use for initial down-selection.
First-principles physics confirmation. Formation energy, stress tensor, and equilibrium structure verified by quantum mechanics.
Full 8-step pipeline: DFT elastic tensor, multistructure polymorph search, impact MD, phonon, thermal AIMD, evidence bundle, benchmark ranking. Cryptographically signed. Patent-ready.
Every metric is labeled with its origin: DFT-computed, ML-predicted, elastic-derived, or MD-derived. You always know exactly how each number was produced.
How We Work With You
Our discovery platform is material-class agnostic. Give us a target property envelope and we run autonomous computational campaigns that produce ranked, validated candidate materials with full documentation.
Step 1
You specify target properties — hardness, density, thermal limits, radar characteristics, cost constraints. We translate them into computational screening thresholds.
Step 2
Our platform generates thousands of candidates, screens with ML ensembles, validates top hits with first-principles DFT, and ranks by feasibility — all in a closed loop.
Step 3
You receive 3–5 high-confidence candidate materials with predicted properties, uncertainty estimates, DFT validation data, and synthesis feasibility assessments.
Step 4
Full technical reports, candidate datasets in standard formats (CIF, JSON), and synthesis route recommendations ready for experimental validation by your team or ours.
SBA-Certified SDVOSB — Active on DARPA & AFRL BAAs
Give us a target property envelope. We deliver ranked candidate materials with DFT-validated properties, calibrated uncertainty estimates, and synthesis feasibility assessments — in weeks, not years. Your requirements in, novel material candidates out.
Active & Target Engagements
Autonomous multi-fidelity materials discovery for rapid response to emerging defense material requirements
Executive Summary Submitted
AI-accelerated ceramic materials discovery for extreme-environment survivability and protective applications
POC Engagement In Progress
Overlay and bond coatings for gas turbine hot corrosion resistance using ICME methodology
Proposal In Development
About the Founder
I grew up in Rittman, Ohio — a small town surrounded by industries that couldn't adapt and eventually faded away. I refuse to let that happen to our defense industrial base.
As a 9½-year USSF veteran with 12 years in the space community, I've operated spacecraft in the most demanding 24/7 environments on the planet. As a Mission Operations Lead, I've logged 1,400+ console flight hours, executed 6,000 satellite contacts, and resolved hundreds of anomalies in real time.
I built this platform the same way I learned to operate weapon systems — hands-on, asking hard questions until the system delivers exactly what the mission demands. Every line of code, every DFT pipeline, every neural network integration — built from scratch by one person who believes our defense forces deserve better materials.
Based in Hazel Green, Alabama — running real GPU inference on dedicated hardware, discovering materials that don't exist in any database in the world. Underation Federal Services LLC is an SBA-certified Service-Disabled Veteran-Owned Small Business (SDVOSB) actively engaged with DARPA, AFRL, and the Navy SBIR program.
Get in Touch
Whether you're a defense program office, a prime contractor, a materials manufacturer, or a research lab — we'd like to hear from you.
Or email us directly:
G.Underation@UnderationFederalServices.comWe'll get back to you within 24 hours.
UnderationAI is built on the shoulders of world-class open-source projects. We gratefully acknowledge the following software, models, and data sources that power our platform.
Universal interatomic potential for energy, force, and stress prediction. Batatia et al., University of Cambridge.
github.com/ACEsuit/maceAtomistic line graph neural network for materials property prediction. Choudhary et al., NIST.
github.com/usnistgov/alignnFormation energy prediction and structure relaxation. Microsoft Research.
github.com/microsoft/mattersimGraph neural network interatomic potentials for hardness and modulus prediction. Materials Virtual Lab.
github.com/materialsvirtuallab/matglLarge language model powering AI advisory agents for discovery, validation, and IP scouting. DeepSeek AI.
huggingface.co/deepseek-aiCrystal Diffusion Variational Autoencoder for generative structure design. Xie et al.
github.com/txie-93/cdvaeDensity functional theory for formation energy, elastic tensors, phonon stability, and AIMD. Giannozzi et al.
quantum-espresso.orgLarge-scale molecular dynamics for impact simulation and shock propagation. Thompson et al., Sandia National Labs.
lammps.orgAtomic Simulation Environment for structure manipulation and calculator interfaces.
wiki.fysik.dtu.dk/aseMaterials analysis, phase diagrams, and energy above hull computation. Ong et al.
pymatgen.orgPhonon calculations for dynamical stability analysis. Togo & Tanaka.
phonopy.github.io/phonopyReference data for known materials, phase diagrams, and energy above hull. Jain et al.
materialsproject.orgOpen Quantum Materials Database for novelty verification and formation energy reference.
oqmd.orgAutomatic FLOW for bulk material data and novelty checks. Curtarolo et al., Duke University.
aflow.orgNovel Materials Discovery laboratory for novelty verification across 12M+ entries.
nomad-lab.euCrystal structure tools and materials data. Choudhary et al., NIST.
jarvis.nist.govOpen scholarly knowledge graph (~250M works) for literature novelty verification.
openalex.orgGPU-accelerated computing for DFT, ML inference, and MD simulation.
developer.nvidia.com/cuda-toolkitLocal LLM inference engine with GPU offload. Gerganov et al.
github.com/ggerganov/llama.cppVector database for agent semantic memory and retrieval-augmented generation.
github.com/chroma-core/chromaPBE-PAW/RRKJUS pseudopotentials for 49+ elements. Dal Corso, Comput. Mater. Sci. 95, 337 (2014).
dalcorso.github.io/pslibraryPerdew, Burke & Ernzerhof, Phys. Rev. Lett. 77, 3865 (1996). Standard DFT functional used for all first-principles calculations.
All trademarks and registered trademarks are the property of their respective owners. The use of these open-source tools and data does not imply endorsement by their developers or institutions. UnderationAI complies with all applicable open-source licenses. Computational results are screening-level predictions and do not constitute certified material qualification. All predictions require experimental validation before real-world deployment.