Materials define what industries can build, how products perform and how societies progress. Yet taking a promising material from concept to industrial application remains slow, costly and uncertain.

Today, advances in computational modelling, artificial intelligence, physical experimentation, and quantum computing are opening new possibilities. But technology alone will not transform materials discovery.

Many R&D workflows still treat computation, synthesis, characterization, processing, and scale-up as separate stages. In reality, each influences the others. The next leap forward will come from connecting these capabilities into integrated workflows designed around real-world performance, manufacturability and industrial outcomes.

Our perspective

At Capgemini’s Quantum Lab, we believe materials science is approaching an inflection point. The next breakthrough is unlikely to come from a single technology. It will emerge from the convergence of AI, computational simulation, physical experimentation and quantum computing.

Together, these capabilities can help researchers navigate vast materials spaces, identify promising candidates, quantify uncertainty and validate performance under real-world conditions. This connected approach can also bring considerations such as manufacturability, degradation, supply-chain constraints and regulatory requirements into the discovery process much earlier.

The future belongs to organizations that redesign their R&D workflows accordingly.

From isolated methods to transdisciplinary collaboration

New technologies are already transforming individual parts of materials research. Their greatest value, however, lies in how they work together.

AI can reveal patterns across complex datasets and propose new material candidates. Simulation can predict behaviour across different conditions and scales. Physical experimentation can validate predictions, expose unexpected failure modes and improve future models. Quantum computing may provide additional insight into selected problems that remain difficult for classical approaches.

The opportunity is to create a continuous discovery loop in which data, theory, simulation and experimentation reinforce one another. This shift can help organizations move beyond promising laboratory results and develop materials that are robust, manufacturable and economically viable.

What you will learn from this point of view

This paper invites organizations to consider several important dimensions of the next phase of materials discovery, such as:

  • Why promising materials frequently fail to reach industrial application
  • How to restructure R&D workflows
  • Where to invest in specialized computational infrastructure
  • How to create pre-competitive consortia
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Why it matters

Connected materials discovery can help organizations identify promising candidates with greater confidence, address manufacturability and real-world constraints earlier, and reduce the risk of costly late-stage failures. It can also help focus experiments and computing resources where they provide the greatest value, improve knowledge transfer across disciplines and institutions, and build more adaptive and outcome-oriented R&D workflows.

Beyond faster discovery, the result is a more intelligent path from prediction to production.

Bringing connected discovery to life: The point of view illustrates how converging technologies could address complex challenges across industries:

  • Aerospace and defense: predicting aircraft corrosion before it starts
    Quantum methods can improve the modelling of critical chemical interactions, simulation can assess their engineering consequences, AI can explore candidate materials and physical testing can validate performance under real-world conditions.
  • Life sciences: identifying drug toxicity sooner
    Simulation can model biological behaviour, quantum computing can improve selected molecular calculations, AI can identify patterns and potential risks, and physical testing can validate the results across multiple scales.
  • Energy and utilities: discovering new materials for net-zero energy systems
    AI and simulation can explore possible candidates, uncertainty analysis can identify where greater accuracy is needed, and classical or quantum methods can focus on the most promising options before physical validation.

Key takeaway

The next breakthrough in materials discovery is unlikely to come from a single technology. It will come from connecting existing capabilities more effectively. AI, simulation, experimentation and quantum computing are individually powerful. Their convergence creates an unprecedented engine for discovery. The future belongs to the organizations that adjust  their workflows accordingly.