Machine-learned interatomic potentials are revolutionising atomistic materials simulations by providing accurate and scalable predictions within the scope covered by the training data. However, ...

Pretrained universal machine-learning interatomic potentials (MLIPs) have revolutionized computational materials science by enabling rapid atomistic simulations as efficient alternatives to ab initio ...

Fine-tuning an AI model is like teaching a student who already knows a lot to become an expert in a specific subject. Instead of starting from scratch, we take a model that has learned from a vast ...

Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford ...

Have you ever wished AI could truly understand the complexities of your field—not just replicate data but reason through intricate, domain-specific challenges? Whether you’re a researcher analyzing ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now A new study by Anthropic shows that ...

The hype and awe around generative AI have waned to some extent. “Generalist” large language models (LLMs) like GPT-4, Gemini (formerly Bard), and Llama whip up smart-sounding sentences, but their ...

A strategy borrowed from generative AI — train cheaply on the familiar, then fine-tune on the hard problem — can cut the number of expensive physics simulations needed by nearly a factor of ten. But a ...