人工智能（AI）在材料发现领域的无限可能与突破

AI is Unlocking the Door to EndlessPossibilities in Materials Discovery From generating novel material candidates to exploring vast chemical spaces, AI is ushering in a transformative leap in material science. Through extraordinary speedand precise calculations: AI can... AcceleratedDiscovery More accurate predictions:Reduced Costand Waste Starting with the desiredmaterial properties:TargetedDiscoveryand Design ...all in a matter of days AI Methods Among AI methods, these offer promising potential noting their neural network backbone. AI-driven materials discovery is not without its challenges: Multiple scales of interaction makemodelling a complicated process Existing synthesis methodsand tools are slow and limited Data is scarce, noisy,and complex Possibilities Innovation in the materials science field could revolutionise many other industries. Novel battery chemistries could vastly improve energystorage, making renewables a stable, 24/7 power source. Re-engineered photovoltaic materials may boost solar efficiency, reducing costsand land use. At the frontier, discovering materials that endure extreme conditions in nuclear reactors could enable safer, cleaner power. AIis accelerating this shift by navigating complex material classes like high-entropy alloys and metal-organic frameworks. Powered by large, high-quality datasets and grounded in a deep understanding of chemistry and physics, AI is becoming anessential tool in materials discovery. AI ENABLES INVERSE DESIGN: REDEFININGMATERIAL DISCOVERY Artificial Intelligence (AI) has becomedeeply embedded in modern life, drivingadvancements far beyond consumerapplications like facial recognition and automatedservices. Its transformative potential extendsacross industries and scientific disciplines,accelerating discovery, optimising processes, andunlocking new frontiers in innovation. At the core of AI-powered materials discovery liesinverse design—a transformative approach thatredefines how breakthroughs occur. Instead of relying on intuition or trial and error,scientists specify desired material properties, andAI models can then predict molecular structureswhich meet those criteria with increasingprecision. This flips the traditional discoveryprocess on its head: AI starts with the end goal,navigating the vast chemical space to uncoverunknown structures and properties. Material science, in particular, stands at the brinkof a revolution. Traditionally, discovering newmaterials has been a slow, iterative process, guidedby human expertise, intuition, and often sheerserendipity. An example is the accidental discoveryof Polytetrafluoroethylene (PTFE), a novel polymerwith exceptional chemical resistance and non-stickproperties—more commonly known to consumersas Teflon™ which is used in cookware today. Supported by unprecedented computationalpower, AI-driven materials discovery is advancingtowards an inflection point, with major technologyplayers accelerating progress. Google DeepMind’sGNoME demonstrated how graph neural networkscan predict new material candidates, expandingthe catalogue of stable materials tenfold.Microsoft’s MatterGen and MatterSim validatedthe feasibility of novel material candidatesusing generative AI methods, while Meta’s OpenMaterials 2024 initiative is driving democratisationthrough open-source datasets and AI models. The field is highly complex, with an immensesearch space of material classes, states, andphase behaviours that challenge conventionalapproaches. AI is uniquely positioned to tacklethis complexity, offering powerful predictivecapabilities that can systematically acceleratebreakthroughs and change how we design anddiscover novel materials. By combining advanced algorithms with well-understood chemistry and physics, AI is poisedto create a quantum leap in material science.Machine learning, deep neural networks, andgenerative AI models can rapidly analyse complexcompositional landscapes, proposing novelcandidates that might otherwise go unnoticed.When powered with scientifically validateddata, AI could enable a closed-loop discoverysystem—compressing development cyclesfrom years to mere days. Beyond acceleratingbreakthroughs, this approach optimises multipleparameters simultaneously, unlocking next-generation solutions in energy, computing, andbeyond. This harnesses the predictive power ofgenerative AI to propose novel candidates, thecomputational power of quantum computing tosieve out the most viable candidates, and thefast turnaround of high-throughput automatedsynthesis for rapid empirical validation. However,the true benefit from the combined use of thethree potent techniques lies in the ability ofgenerative AI to learn from real-world data that isgenerated to improve its predictive performanceand reliability. We are witnessing a renaissance inmaterial science, catalysed by theconvergence of AI and advancedmaterials. AI is not just an enablingtool—it is a catalytic force reshapinghow materials are disco