AI刚发现了5种能替代锂电池的强大材料

Researchers from New Jersey Institute of Technology (NJIT) have used artificial intelligence to tackle a critical problem facing the future of energy storage: finding affordable, sustainable alternatives to lithium-ion batteries.
新泽西理工学院（NJIT）的研究人员利用人工智能技术，解决了未来能源存储面临的一个关键问题：寻找可负担、可持续的锂离子电池替代品。

In research published in Cell Reports Physical Science, the NJIT team led by Professor Dibakar Datta successfully applied generative AI techniques to rapidly discover new porous materials capable of revolutionizing multivalent-ion batteries. These batteries, using abundant elements like magnesium, calcium, aluminum and zinc, offer a promising, cost-effective alternative to lithium-ion batteries, which face global supply challenges and sustainability issues.
在发表于《Cell Reports Physical Science》的研究中，由Dibakar Datta教授领导的新泽西理工学院团队成功运用生成式AI技术，快速发现了能彻底改变多价离子电池的新型多孔材料。这类电池使用镁、钙、铝和锌等富量元素，为面临全球供应挑战和可持续性问题的锂离子电池提供了极具成本效益的替代方案。

Unlike traditional lithium-ion batteries, which rely on lithium ions that carry just a single positive charge, multivalent-ion batteries use elements whose ions carry two or even three positive charges. This means multivalent-ion batteries can potentially store significantly more energy, making them highly attractive for future energy storage solutions.
与依靠仅带单个正电荷锂离子的传统锂离子电池不同，多价离子电池使用的元素离子带有两个甚至三个正电荷。这意味着多价离子电池可能储存更多能量，使其成为未来储能方案的极具吸引力的选择。

However, the larger size and greater electrical charge of multivalent ions make them challenging to accommodate efficiently in battery materials -- an obstacle that the NJIT team's new AI-driven research directly addresses.
然而，多性向离子较大的尺寸和较高的电荷使其难以高效嵌入电池材料——这正是NJIT团队利用新型AI驱动研究直接攻克的障碍。

"One of the biggest hurdles wasn't a lack of promising battery chemistries -- it was the sheer impossibility of testing millions of material combinations," Datta said. "We turned to generative AI as a fast, systematic way to sift through that vast landscape and spot the few structures that could truly make multivalent batteries practical.
达塔表示：“最大的障碍之一并不是没有前景的电池化学成分，而是测试数百万种材料组合的绝对不可能性。我们转向生成式AI，将其作为一种快速、系统的方法，来筛选广阔的可能性空间，并找出真正能使多性向电池实用化的少数结构。”

"This approach allows us to quickly explore thousands of potential candidates, dramatically speeding up the search for more efficient and sustainable alternatives to lithium-ion technology."
这种方法使我们能够快速探索数千种潜在候选材料，极大加快了寻找比锂离子技术更高效、更可持续的替代方案的进程。

To overcome these hurdles, the NJIT team developed a novel dual-AI approach: a Crystal Diffusion Variational Autoencoder (CDVAE) and a finely tuned Large Language Model (LLM). Together, these AI tools rapidly explored thousands of new crystal structures, something previously impossible using traditional laboratory experiments.
为了克服这些障碍，NJIT团队开发了一种新颖的双AI方法：晶体扩散变分自动编码器（CDVAE）和精细调优的大型语言模型（LLM）。这些AI工具共同快速探索了数千种新的晶体结构，这是传统实验室实验以前无法实现的。

The CDVAE model was trained on vast datasets of known crystal structures, enabling it to propose completely novel materials with diverse structural possibilities. Meanwhile, the LLM was tuned to zero in on materials closest to thermodynamic stability, crucial for practical synthesis.
CDVAE模型在已知晶体结构的大型数据集上进行了训练，使其能够提出具有多样化结构可能性的全新材料。与此同时，LLM被调整为专注于最接近热力学稳定性的材料，这对实际合成至关重要。

"Our AI tools dramatically accelerated the discovery process, which uncovered five entirely new porous transition metal oxide structures that show remarkable promise," said Datta. "These materials have large, open channels ideal for moving these bulky multivalent ions quickly and safely, a critical breakthrough for next-generation batteries."
达塔表示："我们的AI工具极大地加速了发现过程，揭示了五种全新的多孔过渡金属氧化物结构，这些结构展现出非凡的前景。这些材料具有大而开放的通道，非常适合快速安全地移动这些庞大的多价离子，这是下一代电池的关键突破。"

The team validated their AI-generated structures using quantum mechanical simulations and stability tests, confirming that the materials could indeed be synthesized experimentally and hold great potential for real-world applications.
该团队通过量子力学模拟和稳定性测试验证了他们由AI生成的结构，确认这些材料确实可以实验合成，并在现实应用中具有巨大潜力。

Datta emphasized the broader implications of their AI-driven approach: "This is more than just discovering new battery materials -- it's about establishing a rapid, scalable method to explore any advanced materials, from electronics to clean energy solutions, without extensive trial and error."
达塔强调了他们人工智能驱动方法的更广泛影响："这不仅仅是发现新的电池材料——更是要建立一种快速、可扩展的方法来探索任何先进材料，从电子产品到清洁能源解决方案，无需进行大量的反复试验。"

With these encouraging results, Datta and his colleagues plan to collaborate with experimental labs to synthesize and test their AI-designed materials, pushing the boundaries further towards commercially viable multivalent-ion batteries.
基于这些令人鼓舞的结果，Datta和他的同事们计划与实验团队合作，合成并测试他们AI设计的材料，进一步推动商业可行的多性向离子电池的发展。