The Materials Challenge

Next-Generation Devices Demand Materials Breakthroughs

Advanced semiconductors, clean energy systems, and quantum devices push materials beyond known performance envelopes. Thermal management, electronic transport, mechanical durability: each domain requires atomic-level understanding. Traditional discovery takes years. Competitive advantage demands months.

Thermal Interface Bottlenecks

Sub-3nm nodes and AI accelerators generate 300+ W/cm² heat densities. Interface resistance at die-to-thermal interface boundaries dominates thermal budgets. Novel heat spreaders and interface materials require phonon engineering at atomic scale.

Electronic Performance Limits

Wide-bandgap semiconductors (GaN, SiC) demand precise defect control and doping optimization. Battery electrolytes need ionic conductivity >10 mS/cm with electrochemical stability. Rational design requires quantum-accurate electronic structure predictions.

Slow Discovery Cycles

Traditional synthesis-test-iterate loops take 12-24 months per material candidate. Characterizing thermal, electronic, and mechanical properties across temperature and stress conditions multiplies validation timelines. Time-to-market delays cost millions.

Unexplored Design Space

Billions of possible material compositions, dopant configurations, and interface structures remain uncharted. Industry relies on empirical rules and vendor datasheets. Computational screening can explore 1000x more candidates than physical synthesis.

Atomistic Intelligence for Materials Discovery

Predict properties, engineer interfaces, validate performance from quantum mechanics to device integration.

A novel conversational interface keeps this workflow inside a guided chat, so the same thread that captures requirements also launches simulations, interprets results, and stores provenance.

User: "I need a thermal interface material for GaN power devices operating at 200°C. Current materials degrade above 150°C."
Copilot: "High-temperature thermal interface material for GaN-on-SiC packages. Let me screen candidates with >5 W/m·K thermal conductivity and >200°C stability. Should I focus on carbon-based composites, metal matrix, or ceramic-filled polymers?"
User: "Carbon-based composites. We need flexibility for die-to-substrate contact."
Copilot: "Screening graphene-nanoplatelet and carbon-nanotube polymer matrices. I'll simulate phonon transport across filler-matrix interfaces and predict effective thermal conductivity. What's your target thermal resistance budget?"
User: "Under 0.05 K·cm²/W for 50 μm bondline thickness."
Copilot: "That requires >10 W/m·K effective conductivity. Running MD simulations with machine learning potentials to optimize graphene loading fraction and alignment. Also calculating interfacial thermal conductance using phonon spectral analysis. Any reliability requirements for thermal cycling?"
User: "Yes, and ensure it survives thermal cycling between -40°C and 200°C without degradation."
Copilot: "Launching multi-objective optimization: thermal conductivity, mechanical compliance, and thermal cycling durability. Using 32 A100 GPUs for parallel candidate screening. I'll simulate interfacial adhesion and thermal expansion mismatch under cycling. ETA: 6 hours. I'll rank top 5 formulations with predicted performance, synthesis routes, and validation test plans."

Core Platform Capabilities

Transform materials development from years to months. MaterialsCodeGraph combines quantum mechanics, machine learning potentials, and multi-scale simulation to predict properties across thermal, electronic, and mechanical domains. Explore vast design spaces computationally before committing to physical synthesis. Build competitive advantage through materials innovation.

Interactive Scientific Copilot - AI assistant visualization

Materials Intelligence Platform

AI copilot trained on physics literature and materials databases. Automatically configures DFT calculations, selects optimal ML potentials, interprets band structures and phonon spectra. Suggests experiments based on simulation results.

Scalable Cloud Compute - Cloud icon over atomic lattice network

Massively Parallel Screening

Screen 1000+ material candidates in parallel across thermal, electronic, and mechanical properties. GPU-accelerated workflows scale from 8 to 512 GPUs on-demand. Run DFT, MD, and BTE calculations simultaneously for comprehensive characterization.

Interpretable Science - Control parameters and observe material properties

Physics
First
Validation

Every prediction rooted in quantum mechanics and statistical mechanics. Visualize electronic structure, phonon modes, defect formation energies. Benchmark ML predictions against ab initio calculations. Export publication-ready analysis and validation reports.

Provenance Ledger - Blockchain cubes with atomic lattice

Complete Property Lineage

Graph-based provenance tracking for proprietary materials development. Record every composition, structure modification, simulation parameter, and performance metric. Reconstruct discovery pathways, defend patent claims, ensure regulatory compliance across industries.

The Platform Advantage

Transform materials R&D from cost center to strategic differentiator

10x Faster Validation

Compress 18-month materials cycles to weeks. Launch products on schedule. Win market timing advantages that translate to share and pricing power.

5-10x ROI

Eliminate 70-80% of physical prototyping. Allocate synthesis resources only to computational winners. Typical first-cycle return through avoided experimental costs alone.

Proprietary Materials

Design materials competitors can't buy off the shelf. Thermal solutions for chipsets, electrolytes for batteries, coatings for turbines, all optimized for your architecture.

Generational Leverage

Amortize platform investment across product lines and technology nodes. Today's 3nm thermal frameworks extend to 2nm, 1.4nm, and beyond.

Strategic Foresight

Evaluate next-generation materials years before commercial availability. Make architecture decisions backed by atomistic physics, not vendor promises.

Sustainable by Design

Meet ESG targets while improving performance. Optimized thermal materials reduce cooling energy. Advanced battery chemistries extend cycle life. Lightweight composites cut emissions.

Multiphysics Simulation Capabilities

Phonons Thermal Transport - Crystalline lattice with heat waves

Thermal
Transport

Predict thermal conductivity from first principles using Boltzmann Transport Equation with anharmonic phonon scattering. Compute interfacial thermal conductance (Kapitza resistance) at material boundaries. Model composites and nanostructures from atomic interfaces to effective medium properties.

Electronic Structure - Band structure and defect states

Electronic Properties

Calculate band structures, bandgaps, and density of states with hybrid Density Functional Theory (DFT). Predict defect formation energies, charge transition levels, and doping limits for semiconductors. Model ionic conductivity in solid electrolytes using ab initio molecular dynamics with thermostatting.

Mechanical Properties - Stress-strain and fracture mechanics

Mechanical Behavior

Compute elastic constants, fracture toughness, and yield strength from atomistic simulations. Model crack propagation, dislocation dynamics, and grain boundary sliding. Predict thermal expansion coefficients and mechanical stability under operational stress and temperature.

API Reference

REST API

Submit jobs, check status, download results via simple HTTP requests.

Python SDK

Native Python library for seamless integration with your workflows.

Webhooks

Real-time notifications when your simulations complete.