Artificial intelligence (AI) is transforming material discovery, ushering in a new era of innovation across key industries such as renewable energy, semiconductors, and pharmaceuticals. This shift is enabling faster, more efficient discovery processes, breaking down traditional barriers in research and development, and paving the way for unprecedented advancements in material science, according to GlobalData, a leading data and analytics firm.
Saurabh Daga, Associate Project Manager of Disruptive Tech at GlobalData, comments: “The advancement of AI in material discovery is being propelled by specific needs across industries. In renewable energy, AI is key to overcoming efficiency and cost barriers that are essential for growth. In semiconductors, it is crucial to find materials for miniaturization and heat management, which are vital for future tech. In pharmaceuticals, AI accelerates drug discovery and biocompatibility, advancing personalized medicine. In essence, AI is increasingly becoming the linchpin for unlocking innovative materials and driving forward industry-specific developments.”
The potential of AI in material discovery is further demonstrated by recent initiatives from both established tech giants and emerging startups. Notable developments include Google DeepMind’s Graphical Networks for Material Exploration (GNoME), which employs advanced deep-learning models for new material structure discovery. This AI tool is currently in use at Lawrence Berkeley National Laboratory’s A-Lab, which combines robotics and machine learning to synthesize novel materials.
GlobalData’s Disruptor Intelligence Center also highlights other significant AI-driven endeavors in material discovery. These include the US-based Quantum Generative Materials LLC’s (GenMat) Generative AI for faster simulation of materials, a collaboration between Fujitsu and Icelandic startup Atmonia leveraging high-performance computing and AI for carbon-neutral technology advancements, and IBM’s AI-enhanced, cloud-based molecular design platform ‘Molecule Generation Experience (MolGX).
Daga concludes: “While AI’s role in materials science is set to streamline development processes in key industries, challenges remain. Overcoming obstacles related to data, algorithms, and cross-industry collaboration is crucial for AI models to effectively accelerate material discovery. To fully leverage the benefits offered by AI-powered material discovery, a robust supporting infrastructure is vital.”