Achieving Net Zero hinges on novel materials capable of unlocking breakthroughs in hard-to-abate sectors like heavy industry, transport, and energy. Despite advances in computing and lab technologies, materials discovery remains slow, driven largely by trial-and-error experimentation.
The average timeline from discovery to commercialization still spans an average of two decades. This delays the deployment of climate-critical technologies and highlights the need for more efficient, data-driven approaches.
In this article, we discuss scientific advancements that address the growing need for faster discovery, development, and commercialization of novel materials.Â
Innovation Landscape in Materials Discovery
Climate Tech solutions can offer promising pathways with two main goals: identifying materials whose properties align with specific applications, or shortening the average molecule-to-market lead times.
Accelerating the materials discovery process is essential to shorten the timeline from laboratory breakthroughs to real-world applications. Below, we explore some of the most advanced scientific methods driving material innovation and how they’re shaping the search for materials that can enable the green transition.
The Net Zero Insights Climate Tech Taxonomy showcases these technological advancements in a structured, multi-layered framework that simplifies the complex climate innovation landscape.
Market Map illustrating companies and key data points across the materials discovery value chain as of July 2025. *Note: Companies/deals may exist across multiple stages of the value chain.
Robotics for materials discovery
Translating promising material candidates into viable compounds remains one of the biggest hurdles in materials innovation. Self-driving labs address this challenge by merging robotics, AI, and automated workflows to radically accelerate the discovery process.
Also known as Materials Acceleration Platforms (MAPs), these labs automate key steps in materials synthesis and testing. Robotic systems carry out tasks such as sample preparation, heating, and characterization with high precision and repeatability. Machine learning models analyze results in real time and plan the next steps without human intervention. This closed-loop approach boosts efficiency, improves success rates, and cuts down the time and cost associated with traditional lab work.
A strong example is Berkeley Lab’s A-Lab, an autonomous platform designed to synthesize inorganic compounds. In just 17 days, it conducted 355 experiments and successfully created 41 out of 58 targeted materials, a 71% success rate.
High-Throughput Experimentation (HTE)
High-Throughput Experimentation (HTE) accelerates the search for novel materials by conducting hundreds or even thousands of parallel experiments in rapid succession. Using robotic systems and miniaturized reaction setups, HTE allows researchers to quickly explore vast combinations of elements with minimal material input and cost.
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In a typical HTE workflow, a computational model proposes a library of material combinations. These are synthesized and tested in parallel, with machine learning algorithms continuously refining the process to identify the most promising candidates.
This approach is especially critical in Climate Tech, where speeding up development to commercialization timelines is essential.
Computational Materials Science and Modelling
Computational materials science uses advanced simulations to understand and design materials at the atomic scale before they are synthesized. This field brings together AI, cloud computing, digital twins, and quantum simulations to model the properties of potential materials with speed and precision.
By applying theoretical and simulation-based tools, researchers can predict how a material’s composition and structure will influence its properties. This computational approach filters large material libraries to identify those with the highest potential for success. In doing so, it helps avoid unnecessary experimentation saving both time and resources.
As simulation techniques grow more sophisticated, the focus shifts from simply identifying promising materials to designing how they should be synthesized. The ultimate goal is to develop predictive systems that not only suggest ideal candidates but also guide their synthesis making materials design faster, smarter, and more targeted than ever before.
Artificial intelligenceÂ
Artificial Intelligence (AI) is transforming the materials discovery process by narrowing the field of viable candidates and optimizing how experiments are planned and executed. What once required screening millions of possibilities can now be reduced to a shortlist of high-potential compounds, saving time, cost, and energy.
AI contributes to this acceleration in two essential ways, both of which are already seeing early but impactful adoption in the field:
Data-driven materials discovery
One of the biggest barriers to accelerating materials innovation is the lack of relevant data. High-quality, large-scale datasets are essential for training robust machine learning models. In the current scenario, data is often sparse, inconsistent, or siloed, limiting the reliability and scalability of predictive tools.
Traditionally, most materials data has been locked within scientific publications and patents. Manually extracting information from thousands of articles is neither scalable nor efficient. Natural Language Processing (NLP) offers a breakthrough by automating this process by rapidly scanning and extracting details from large volumes of literature. With the help of NLP, researchers can shorten discovery pipelines, improve synthesis accuracy, and significantly reduce development timelines for new materials.
Deep learning
Deep learning is proving to be a powerful driver of accelerated materials discovery. One notable breakthrough is Google DeepMind’s Graph Networks for Materials Exploration (GNoME). The tool that uses AI models to predict the stability of new materials at scale. Recently, out of the 2.2 million predictions made by GNoME, over 380,000 were identified as highly stable materials, marking them as strong candidates for experimental synthesis.
What makes GNoME especially significant is its validation in real-world labs. External researchers have independently synthesized 736 of these predicted materials, confirming the model’s ability to accurately identify viable crystal structures. This emphasizes the potential of AI to not only generate theoretical possibilities but to guide experimental work with precision.
Among the newly discovered candidates are materials that could fuel the next-generation technologies, ranging from superconductors and high-performance computing to more efficient battery systems for electric vehicles.
Other innovations driving materials discovery
Other scientific breakthroughs are accelerating the discovery and development of novel inorganic materials:
- Density Functional Theory (DFT)Â is a powerful computational method used to model and predict the electronic structure of materials at the quantum level. It enables researchers to simulate critical properties like conductivity, reactivity, and optical behavior.
- Quantum computing: Traditional computers struggle to model the behavior of complex atomic systems due to the sheer number of variables involved. Quantum computers can naturally simulate quantum interactions such as superconductivity or thermal resilience.
- Digital Twins: Digital twins are exact virtual models that mirror the physical form and functional behavior of candidate materials. These replicas are used to predict how materials will perform under different scenarios such as extreme temperature, pressure, or stress without needing to physically test every outcome.Â
Materials Databases
Materials databases are vital tools in accelerating innovation. These digital platforms aggregate key data on material properties, structures, and behaviors sourced from experimental and computational methods. Their core purpose is to enable faster discovery, minimize duplication, and foster collaboration.
Two notable examples include:
- The Materials Project launched by the Department of Energy’s Lawrence Berkeley National Laboratory is an open-access platform for known and hypothetical materials.Â
- The Renewable Energy Materials Properties Database (REMPD) by the National Renewable Energy Laboratory (NREL) hosts information related to wind and solar energy systems.
Accelerating materials discovery from lab to market
The technologies outlined above are transforming materials discovery from a slow trial-and-error process into a fast, intelligent, and scalable engine of innovation. These scientific breakthroughs help researchers to explore novel materials with speed and accuracy. This convergence is already delivering results as some processes have the ability to synthesize more than two new materials per day with minimal human intervention.Â
To fully unlock this potential, sustained investment in these climate technologies will be critical. It will empower the scientific community to design, test, and deliver the next generation of materials that can drive the global transition to a decarbonized future.
Interested in learning more about the technologies and startups leading the in the Material Discovery Space? Book a demo of our platform to explore the innovations in materials discovery in detail, uncover emerging trends, and gain actionable insights to stay ahead in this rapidly evolving space.
