The success of the clean energy transition depends on reliable access to critical minerals and breakthroughs in advanced materials. Global demand for minerals such as lithium, nickel, and cobalt is projected to triple by 2030 and quadruple by 2040. As demand for clean technologies like solar panels, wind turbines, and grid-scale batteries grows, innovation in materials discovery will be essential to deliver efficient, durable, and scalable solutions. These advancements are increasingly central to climate mitigation, energy security, and economic resilience.Ā
This article introduces the fundamentals of materials discovery and explores what makes a novel material suitable for large-scale deployment in clean energy systems. It also examines the key challenges that continue to slow progress despite the fieldās potential to drive sustainable innovation.
What is materials discovery?
Materials discovery is the process of identifying or designing new materials with properties that enable technological advancement. It is a foundational pillar of innovation across industries. Developments in materials discovery helps achieve decarbonization goals in energy storage, renewable generation, sustainable manufacturing, and more.
Traditionally, this process relied heavily on trial-and-error experimentation, requiring significant time and resources. Today, the field is undergoing a transformative shift. Instead of relying solely on lab-based testing, scientists now use high-throughput simulations, AI-driven predictions, and automation to dramatically accelerate discovery timelines. These tools enable the rapid screening of thousands of possible material compositions, predicting properties like conductivity, thermal stability, or environmental resilience before an experiment is run. This shift from purely empirical methods to data-driven and computational approaches is unlocking new classes of materials previously impossible.
The end goal is to accelerate discovery by building a robust knowledge base that enables the design and synthesis of materials with specific, targeted properties.
When is a novel material fit for commercialization?
For a new material to be commercially viable, it must meet requirements related to both product performance and ease of manufacturing.
Product-related attributes refer to the properties needed to achieve the desired function in a specific application. These often include mechanical, chemical, thermal, optical, or electrical characteristics, depending on where and how the material will be used.
In addition, the material must be easy to manufacture at industrial scale. This includes achieving consistent quality, acceptable production yield, and keeping fabrication costs within feasible limits. Costs are influenced by raw materials, processing methods, and quality control requirements.
Ultimately, a novel material can only succeed in the market if it performs as needed and can be produced reliably and affordably.
Challenges Hindering Progress in Materials Discovery
Despite recent advances, several obstacles continue to limit the pace and effectiveness of materials discovery.
Data Quality and availability
Modern AI and machine learning models require large, diverse, and high-quality datasets to make accurate predictions. In materials science, such datasets are often scarce, fragmented, or inconsistent. Experimental data can be noisy and the lack of standardized formats further complicates efforts to consolidate findings.
While data is increasingly used for studies and AI-driven discovery, most databases remain dispersed, incomplete, and difficult to access. This fragmentation slows down progress and limits the potential of computational tools to drive innovation.
To enable meaningful breakthroughs, a coordinated effort is needed to build comprehensive, high-quality data resources covering material synthesis, characterization, and performance.
Long commercialization cycles
One of the most persistent challenges in materials discovery is the long timeline from the lab to market adoption. It typically takes up to 20 years for a new material to move from initial discovery to widespread commercial use. This extended time frame not only delays impact but also limits profitability, as patent protections often expire just as commercialization begins, eroding any proprietary advantage.
Bridging the gap between lab-based material properties and real-world customer needs adds further complexity. For instance, a new battery material must satisfy objectives like safety, cost, energy density, and charging speed. Yet, these practical demands are not always met from early-stage research data.
The goal must be to discover, develop, manufacture, and deploy advanced materials in a fraction of the current timeline, ideally in a few years.
High cost of advanced solutions
Despite the promise of data-driven materials discovery, the high cost of deploying advanced infrastructure remains a significant barrier. Cutting-edge computational platforms, AI-driven discovery tools, and automated labs require substantial upfront investment. Such upfront costs are often beyond the reach of early-stage research teams or startups.
Today, the discovery of a new material typically demands a decade or more of effort and an investment ranging from $10 million to $100 million. Scaling this process through AI and robotics promises to reduce time and cost but building the ecosystem requires significant capital.
Recent estimates suggest that building the foundational capabilities for next-generation materials discovery would require investments between $130 million and $240 million. These costs span dataset creation, foundational model training, lab infrastructure, and the development of a robotics supply chain.
While the required investment is substantial, the potential return is even greater. Reducing discovery timelines and costs at scale would fundamentally reshape the innovation cycle in materials science, unlocking breakthroughs critical to industrial, environmental, and technological progress.
Accuracy of predictions
Computational methods and AI-driven models continue to struggle with accurate prediction of complex properties and behaviors of novel materials. As a result, experimental validation remains essential.
A key issue is that many materials datasets contain a large number of redundant entries, materials that are highly similar in structure and composition. For example, a materials database might have multiple iterations of a successful novel material. When machine learning models are tested on datasets containing similar materials, their predictive performance appears artificially high. However, once the redundancy is reduced, model accuracy drops significantly revealing the limitations of these models.
Additionally, challenges persist in accurately representing complex molecular structures. Many AI models still lack the ability to capture the structural diversity seen in advanced materials. Models trained on one set of materials often perform poorly when applied to entirely different systems.
Moreover, simulating material behavior at the atomic or molecular level is computationally expensive despite AIās promise to improve simulation efficiency. Improving model design capability, chemical representation, and algorithmic efficiency will be essential to overcoming current predictive limitations.
Trust, adoption, and interdisciplinary gaps
The introduction of AI and automation into the traditionally experiment-driven field of materials science demands a fundamental cultural shift. As this article outlines, progress in materials discovery remains constrained by long development timelines, fragmented data, limited predictive accuracy, and high infrastructure costs.
Despite these barriers, recent advances in AI, automation, and computational modeling are creating new pathways to accelerate discovery and reduce time to commercialization.Ā The future of clean energy will be shaped by how quickly and effectively the materials discovery ecosystem can evolve. Investment is essential to develop next-gen technologies to enable faster decarbonization, greater industrial resilience, and long-term economic competitiveness.
This article is drawn directly from our comprehensive Materials Discovery Market Snapshot Report. The market snapshot offers an in-depth analysis of the solutions landscape, key technologies, market size, and emerging investment trends shaping the next breakthrough in finding novel materials.
