在寻找新材料的过程中，科学家们传统上依赖于实验室里的修修补补，在直觉的指导下，进行大量的试验和错误。
但是现在，一种新的电池材料已经被发现，它结合了两种超级计算能力:人工智能和超级计算。这一发现凸显了利用计算机帮助科学家发现适合特定需求的材料的潜力，从电池到碳捕获技术再到催化剂
微软和太平洋西北国家实验室(PNNL)的研究人员在1月8日提交给arXiv.org的一篇论文中报告说，通过计算，从3200多万种候选材料中筛选出了23种有希望的材料。然后，该团队合成并测试了其中一种材料，并创造了一个可工作的电池原型。
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While scientists have used AI to predict materials’ properties before, previous studies typically haven’t seen that process through to producing the new material. “The nice thing a
The researchers targeted a coveted type of battery material: a solid electrolyte. An electrolyte is a material that transfers ions — electrically charged atoms — back and forth between a battery’s electrodes. In standard lithium-ion batteries, the electrolyte is a liquid. But that comes with hazards, like batteries leaking or causing fires. Developing batteries with solid electrolytes is a major aim of materials scientists.
The original 32 million candidates were generated via a game of mix-and-match, substituting different elements in crystal structures of known materials. Sorting through a list this large with traditio
First, the researchers used AI to filter the materials ba
That left the researchers with 23 candidates, five of which were already known. Researchers at PNNL picked a material that looked promising — it was related to other materials that the researchers knew how to make in the lab, and it had suitable stability and conductivity. Then they set to work synthesizing it, eventually fashio
“That’s when we got very excited,” says materials scientist Vijay Murugesan of PNNL in Richland, Wash. Going from the synthesis stage to the functio
The new electrolyte is similar to a known material co
Combining lithium and sodium is unconventional. “In a usual approach … we would not mix these two together,” says materials scientist Yan Zeng of Florida State University in Tallahassee, who was not involved in the research. The typical practice is to use either lithium or sodium ions as a conductor, not both. The two types of ions might be expected to compete with one another, resulting in worse performance. The unorthodox material highlights one hope for AI in research, Zeng says: “AI can sort of step out of the box.”
In the new work, the researchers created a series of AI models that could predict different properties of a material, ba
To perform both the AI and physics-ba
The project, Baker says, is an example of a practice known in tech circles as “eating your own dog food,” in which a company uses its own product to co
The study is one of many efforts to use AI to discover new materials. In November, researchers from Google DeepMind used graph neural networks to predict the existence of?hundreds of thousands of stable materials, they reported in the Dec. 7?Nature. And in the same issue of?Nature,?Zeng and colleagues reported on a?laboratory?operated by AI,?designed to produce new materials autonomously.
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