How AI can help in materials innovation – from discovery to design

Picture this scenario: David, a materials scientist at a top artificial intelligence (AI)-enabled lab, collaborates with an AI platform to design a new high-performance composite. The platform instantly generates millions of unprecedented molecular structures, screens their feasibility, predicts their properties and proposes cost-effective synthesis pathways.

It then autonomously generates synthesis tasks and dispatches them to high-throughput experimental equipment. Through iterative cycles informed by rapid AI feedback, David achieves a breakthrough formula. The system evaluates his findings, suggests further refinements and alerts the project manager to begin scale-up production.

This scenario is likely to be a common day for high-tech companies developing new materials in the near future. Thanks to advances in AI, material scientists today are making breakthroughs at unprecedented rates, addressing the urgent need for novel materials that enable decarbonization across all sectors. Traditional materials often fall short for next-generation green technologies, creating a bottleneck in climate response efforts.

However, as highlighted in the World Economic Forum's report Top 10 Emerging Technologies of 2024, AI is revolutionizing how we discover and apply new knowledge, potentially unlocking the advanced materials required for more efficient solar cells, higher-capacity batteries and critical carbon capture technologies – accelerating our path to carbon neutrality.

This technological transformation comes at a critical juncture. As global competition for finite resources intensifies, and the urgency to address climate challenges grows, breakthroughs in advanced materials will be foundational for delivering cleaner, more resilient technologies.

Traditional approaches to materials development – primarily driven by experience, intuition and trial-and-error methodologies – are increasingly insufficient to meet current challenges. Materials scientists and engineers today face several critical hurdles:

This is precisely where ‘AI for science’, or AI4S, offers transformative potential. Advanced machine learning models trained on extensive datasets, combined with high-throughput computational methods, can predict properties for numerous substances in minimal time, rapidly screening candidate materials against desired parameters.

Beyond discriminative models that predict material properties, generative models can directly design molecular structures based on desired properties, creating the molecules needed.

Deep Principle’s latest generative model, ReactGen, for example, can propose novel and complex chemical reaction pathways by learning underlying reaction principles, enabling efficient and innovative synthesis route discovery.

These models can also generate and iteratively explore diverse synthesis pathways, incorporating chemical information and physical constraints to recommend feasible reaction routes. In product formulation, AI enables multi-objective optimization to meet complex and varying market requirements precisely, saving significant human capital, material resources and development time.

Global technology leaders, from Microsoft and Google to Lawrence Berkeley National Laboratory, have launched bold initiatives such as MatterGen and GNOME, using AI to vastly augment the scale and precision of materials research.

In parallel, emerging players like XtalPi are constructing integrated data-generation and inference “flywheels” – combining automated laboratories with advanced AI – to enable accelerated, iterative discovery at unprecedented scale.

"2024 has also been a transformative year for startups in the AI for science ecosystem, particularly in biotechnology and the emerging fields of chemistry and materials science," Chenru Duan and other AI for science community organizers wrote in their annual review.

In biotech and pharmaceuticals, several major milestones have been achieved. XtalPi went public with a valuation of $2.5 billion, and other notable companies, such as Terray Therapeutics and Iambic Therapeutics, closed significant fundraising rounds while advancing their drug discovery pipelines.

A standout case was Isomorphic Labs, a Google DeepMind spinoff. The company announced strategic partnerships with Eli Lilly and Novartis, securing $82.5 million upfront and projecting a potential total of $3 billion (excluding royalties). This partnership underscores the immense business value of specialized AI models, even for well-established pharmaceutical giants.

Meanwhile, startups focused on chemistry and materials science have begun to emerge. Companies like Orbital Materials and DP Technology released large pre-trained machine learning potentials – Orb and DPA-2, respectively – designed to accelerate molecular dynamic simulations with higher precision.

Additionally, new startups such as CuspAI, Lila Sciences and my own, Deep Principle, have successfully closed seed funding rounds, each with a vision of reshaping chemistry and materials discovery.

In the case of Deep Principle, we are transforming material innovation from discovery to design by using generative AI models. We integrate generative AI, quantum chemistry and automated experimentation into a unified workflow to achieve the full chain from molecule generation and synthesis design to reaction and formulation optimization, where our self-developed generative AI platform will be one of the keys.

Despite the promise of this new paradigm, significant challenges remain before AI4S can achieve widespread industrial implementation:

To overcome these obstacles, businesses and research institutions tend to forge collaborative partnerships to create universal or domain-specific datasets, while continuously improving algorithms through iterative development cycles.

Crucially, integrating purely data-driven approaches with first principles methodologies enhances extensibility, while deep learning algorithms excel at fitting available data, first principles and domain knowledge can effectively extrapolate to areas with limited or no empirical data.

AI is rapidly becoming a pivotal force, ushering in a new era of materials innovation. Its unrivalled ability to scan and assess vast landscapes of known structures and combinations empowers scientists to quickly identify optimal materials that would otherwise take years or even decades to discover by traditional means.

Generative AI models go a step further, directly creating new-to-nature molecules and reaction pathways tailored for specific applications, unlocking unprecedented possibilities in reverse-design and innovation. When integrated with automated experimental platforms, AI enables a fast, efficient cycle of hypothesis, prediction and validation, drastically accelerating the progression from theoretical discovery to scalable industrial production.

Far more than just added convenience, this agility directly addresses longstanding bottlenecks inherent in conventional materials R&D, which is often time-consuming, labour-intensive and marked by low success rates – falling short of what fast-moving markets and industries increasingly demand.

By fostering seamless interdisciplinary and even cross-border collaboration, and by leveraging both the depth of domain expertise and the breadth of data-driven insights, AI-enabled platforms are poised to transform materials science – delivering the breakthroughs necessary for a more sustainable and robust future for all.