Materials Science

Design the next generation of materials with quantum computing

Innovation in advanced materials underpins some of the world’s most competitive industries—from batteries and semiconductors to aerospace, chemicals and clean energy. Yet many of the materials that could transform these sectors remain impossible to accurately simulate using today’s classical computers.

Quantum computing is changing that. By combining quantum algorithms with AI and high-performance computing (HPC), organizations will be able to model materials at the atomic level with unprecedented accuracy, accelerating discovery while reducing costly laboratory experimentation.

Who this applies to

This solution is particularly relevant for organizations in:

• Battery and energy storage
• Automotive and electric vehicles
• Aerospace and defense
• Chemicals and specialty materials
• Semiconductor manufacturing
• Renewable energy
• Steel, metals and advanced manufacturing
• Industrial R&D organizations

The challenge

Developing new materials is a slow, expensive and highly iterative process.

Whether designing a battery cathode, a corrosion-resistant alloy, a semiconductor material or a catalyst for hydrogen production, engineers must predict how atoms and electrons interact before investing in manufacturing and physical testing.

Today’s simulation tools provide valuable approximations but struggle as materials become more complex.

As a result, organizations face:

• Long material development cycles
• Extensive laboratory validation
• High computational costs
• Slow optimization of manufacturing processes
• Missed opportunities for breakthrough materials

The next generation of industrial innovation requires a new level of computational accuracy.

Business impact

Quantum-enhanced materials simulation has the potential to create value across multiple industries.

Potential business outcomes include:

• Faster materials discovery
• Reduced R&D costs
• Shorter product development cycles
• Higher battery performance and lifetime
• More efficient catalysts for sustainable chemistry
• Improved corrosion resistance
• Better semiconductor materials
• Increased energy efficiency
• Reduced dependence on physical prototyping

Applications span electric vehicles, renewable energy, industrial manufacturing, aerospace and advanced electronics.

Evaluate your materials challenge for quantum advantage

Discover where quantum simulation can complement your existing AI, HPC and computational chemistry workflows.

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Why classical computing reaches its limits

Most industrial materials are governed by quantum mechanical interactions between electrons.

While Density Functional Theory (DFT) has become the standard computational approach, it relies on approximations that become increasingly inaccurate for many technologically important materials.

These limitations become particularly significant when studying:

Strongly correlated materials
Electron interactions become too complex for conventional computational methods, limiting the development of superconductors, magnetic materials and advanced semiconductors.

Multi-scale simulations
Small inaccuracies in atomic-scale calculations propagate into incorrect predictions of mechanical strength, stability, corrosion resistance and material lifetime.

Chemical reaction pathways
Understanding catalytic reactions is essential for hydrogen production, carbon capture, sustainable chemistry and industrial process optimization.

Material properties
Electronic, thermal, optical and mechanical properties determine the performance and safety of batteries, photovoltaics, structural materials and electronic devices.

AI-driven materials discovery
Machine learning models depend on high-quality training data. Since most datasets originate from DFT simulations, their accuracy is constrained by the limitations of classical methods.

The quantum approach

Quantum computing offers a fundamentally different way to simulate matter.

Rather than replacing existing computational tools, quantum processors work alongside classical HPC and AI to solve the most computationally demanding electronic structure calculations.

Near-term hybrid workflows already allow organizations to explore quantum-ready applications while preparing for the next generation of fault-tolerant quantum computers.

As logical qubit counts continue to scale, advanced algorithms such as Quantum Phase Estimation (QPE) will enable highly accurate simulations of materials that remain beyond the reach of classical architectures.

Representative use cases

Next-generation battery materials
Improve the design of lithium-ion, solid-state and future battery chemistries by accurately predicting:

• Cell voltage
• Ionic diffusion
• Thermal stability
• Material degradation
• Energy density

Better simulations can significantly reduce laboratory iterations while accelerating commercialization.

Sustainable catalysts
Design catalysts for:

• Green hydrogen production
• Ammonia synthesis
• Carbon capture and utilization
• CO₂ conversion
• Industrial chemical manufacturing

More accurate electronic structure calculations enable researchers to identify highly efficient catalyst candidates before experimental validation.

Corrosion-resistant materials
Corrosion represents one of industry’s largest hidden costs.

Quantum-enabled materials simulation can improve predictions of degradation mechanisms, helping engineers design longer-lasting infrastructure, transportation systems and industrial equipment.

Semiconductors and advanced electronics
Develop new electronic materials with improved electrical, magnetic and optical properties for:

• Microelectronics
• Quantum technologies
• Photonics
• High-performance computing

Aerospace and advanced manufacturing
Optimize lightweight structural materials that improve:

• Mechanical strength
• Fatigue resistance
• Thermal performance
• Manufacturing efficiency

Assess your quantum readiness

Our quantum application engineers can evaluate your computational workflows and identify where quantum simulation can create measurable business value.

→ Book a technical assessment

Technical deep dive

Electronic structure calculations remain one of the most computationally demanding problems in materials science.

Although Density Functional Theory (DFT) has become the industry standard, it struggles to accurately model strongly correlated electron systems, transition states and many catalytic processes.

Wavefunction-based methods such as CASSCF and CCSD improve accuracy but scale exponentially, making simulations of realistic industrial materials computationally impractical.

Fault-tolerant quantum computers executing algorithms such as Quantum Phase Estimation (QPE) are expected to overcome many of these limitations by directly computing highly accurate ground-state energies.

Representative applications include:

Nitrogen fixation
The iron-molybdenum cofactor (FeMoco), the catalytic center of nitrogenase enzymes, exhibits complex multi-reference electron correlation that remains beyond the practical capabilities of classical simulation. Accurate quantum simulation could accelerate the development of synthetic catalysts capable of reducing the energy requirements of ammonia production.

Battery materials
For cathode materials such as lithium iron silicate (Li₂FeSiO₄), quantum algorithms have demonstrated the potential to compute chemically accurate ground-state energies, enabling more reliable predictions of voltage, ionic mobility and thermal stability than current DFT approaches.