7 papers
Atomistic Language Models Understand and Generate Materials
Sathya Edamadaka, Krithik Ramesh, Ju Li +1
Atomistic structure and natural language have long been modeled separately, with language models either calling atomistic models as tools or being fine-tuned on lossy textual encod…
Efficient Crystal Structure Prediction Using Universal Neural Network Potential with Diversity Preservation in Genetic Algorithms
Takuya Shibayama, Hideaki Imamura, Katsuhiko Nishimra +4
Crystal structure prediction (CSP) is crucial for identifying stable crystal structures in given systems and is a prerequisite for computational atomistic simulations. Recent advan…
Matlantis-PFP v8: Universal Machine Learning Interatomic Potential with Better Experimental Agreements via r2SCAN Functional
Chikashi Shinagawa, So Takamoto, Daiki Shintani +7
Universal Machine Learning Interatomic Potentials (uMLIPs) enable atomistic simulations and high-throughput screening at scales far beyond those accessible with density functional…
A Multi-agent Framework for Physical Laws Discovery
Bo Hu, Siyu Liu, Beilin Ye +6
Discovering explicit physical laws has traditionally depended on human intuition and domain expertise. Recent advances in artificial intelligence, particularly large language model…
Universally Converging Representations of Matter Across Scientific Foundation Models
Sathya Edamadaka, Soojung Yang, Ju Li +1
Machine learning models of vastly different modalities and architectures are being trained to predict the behavior of molecules, materials, and proteins. However, it remains unclea…
LightPFP: A Lightweight Route to Ab Initio Accuracy at Scale
Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang +5
Atomistic simulation methods have evolved through successive computational levels, each building upon more fundamental approaches: from quantum mechanics to density functional theo…