AI-Driven Advancements in Energy Materials to Propel U.S. Towards Clean Energy Goals

A recent report from the Advanced Research Directions on AI for Energy’s Winter 2023 workshops underscores the critical role of energy materials in the generation, storage, and efficient use of energy. These materials, which include those for energy storage, photovoltaics, thermoelectrics, catalysts, and advanced multicomponent alloys, are essential for achieving U.S. clean energy goals, economic growth, and energy justice while minimizing environmental impacts.

Achieving the U.S. sustainability and clean energy targets by 2050 requires significant advancements in the discovery, design, production, and certification of energy materials with tailored properties and performance. This entails exploring extensive parameter spaces that are beyond the scope of human-driven exploration. The need for cost-effective and sustainable production methods, along with addressing challenges related to durability and lifecycle management, are crucial hurdles to overcome.

Current developments in artificial intelligence (AI) are playing a transformative role in advancing energy materials research. AI is accelerating the design and discovery of new energy materials, enhancing laboratory automation for faster synthesis, iterative testing, and refinement, and bridging the gap from laboratory-scale research to certified industrial-scale use.

AI’s potential to revolutionize the field of energy materials lies in its ability to uncover new materials, forecast their properties, and lead to discoveries that address significant challenges in the energy sector. Successfully leveraging AI will establish U.S. leadership in developing high-performance, clean-by-design energy materials, thereby accelerating the nation’s shift towards a circular economy based on sustainable and environmentally friendly reuse and regeneration of materials.

Grand Challenges in Energy Materials

The report identifies three primary impact areas in energy materials:

• Energy Generation, Harvesting, and Conversion: Developing materials that enhance the efficiency and cost-effectiveness of generating energy from renewable and nuclear sources is paramount. This includes improvements in solar energy technologies for better sunlight capture and conversion, advancements in materials for safer and more efficient nuclear reactors, innovations in materials for hydrogen production, and the development of thermoelectric materials to convert waste heat into electricity.
• Energy Storage and Efficiency: Designing materials that can store and convert energy more efficiently is crucial. This includes new materials for advanced batteries and supercapacitors that offer improved energy storage, advancements in fuel cell technologies, efficient hydrogen storage, better insulation materials for energy-efficient buildings, and lightweight materials that enhance energy conservation in the transportation sector.
• Environmental Sustainability and Scalability: Reducing the environmental impacts of energy use and production is essential. This involves developing new materials for carbon capture and utilization, discovering new catalysts for energy-related chemical conversions, and designing materials that are sustainable and scalable for widespread use and mass production.

To address these needs, the scientific community must focus on accelerating materials discovery, improving predictive material design, and bridging the scales from lab experiments to industrial production.

AI’s Role in Energy Materials Research

The report highlights several ways AI can contribute to advancing energy materials research:

• Accelerating Energy Materials Discovery: AI tools can explore large chemical and materials spaces, screen and predict materials, discover feasible synthetic routes, and optimize multimodal functionalities using large datasets. Efficient and scalable methods for acquiring high-quality experimental data and high-throughput computational simulations are essential for building a high-fidelity representation of the materials space.
• Improving Predictive Modeling and Design: AI will play a significant role in accelerating the design process and predicting material properties, thereby reducing the time required for prototyping, development, and certification. Advances in AI and multi-fidelity modeling will enable high-accuracy predictions of material behaviors by connecting multiple data modalities, spatiotemporal scales, and physics.
• Bridging the Scale from Lab Experiments to Industrial Production: Enhancing AI capabilities to accurately predict material properties and performance across scales will expedite the transition from lab-scale experiments to industrial manufacturing. Explainable and trustworthy AI models with reliable extrapolation capabilities will streamline the scale-up process by integrating AI systems with advancements in manufacturing technology.

Challenges and Solutions

The report outlines several challenges in applying AI techniques to energy materials research:

• Data Requirements: The need for vast datasets for AI/machine learning material informatics due to the heterogeneity of materials requires substantial investments and is time-consuming. Efforts must be made to develop a latent representation of the energy materials space by integrating existing and new experimental and computational data across modalities, scales, and fidelity levels.
• Precision Synthesis and Manufacturing: AI-enabled synthesis and manufacturing platforms need to validate new synthesis routes and scale up production using autonomous experimentation approaches. Developing edge-deployable AI models for real-time synthesis process control and precision material synthesis is crucial for successful industrial manufacturing.
• Lifecycle-Aware Materials Design: Integrating AI models with lifecycle and supply chain constraints into the materials design process will enable end-of-life recycling/upcycling and minimize supply chain dependencies. AI-driven models for energy materials lifecycle, coupled with supply chain information, will optimize the design and production of high-performance, clean-by-design energy materials.

Expected Outcomes

The AI-accelerated discovery, synthesis, and manufacturing of precision energy materials have the potential to deliver impacts akin to precision medicine. Advanced AI techniques and tools will expand the explorable materials space by several orders of magnitude, significantly reducing the time and cost associated with discovering new materials for energy applications.

Precision control in the materials synthesis and manufacturing process will create highly efficient materials with specific property profiles and performance. This will facilitate the design of improved fuels and structural materials for next-generation nuclear energy systems. Enhanced efficiency will contribute to reducing energy consumption and improving resource utilization.

Incorporating clean and safe-by-design constraints in the design of new materials will reduce environmental impacts and enable better reuse and recycling. Embedding supply-chain constraints in the design of new energy materials will leverage abundant U.S. sources of materials, avoiding reliance on critical materials.

Overall, the development of advanced AI systems coupled with physics-based simulations and the unique experimental and computational facilities of the Department of Energy (DOE) will transform the discovery, design, synthesis, production, and certification of energy materials. This effort will establish U.S. leadership in applied energy, driving the nation towards a sustainable and secure energy future.