5 papers
UniFFBench: Evaluating Universal Machine Learning Force Fields Against Experimental Measurements
Sajid Mannan, Vaibhav Bihani, Carmelo Gonzales +5
Universal machine learning force fields (UMLFFs) promise to revolutionize materials science by enabling rapid atomistic simulations across the periodic table. However, their evalua…
Sustainable Materials Discovery in the Era of Artificial Intelligence
Sajid Mannan, Rupert J. Myers, Rohit Batra +3
Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening. Yet current genera…
AI scientists produce results without reasoning scientifically
Martiño RÃos-GarcÃa, Nawaf Alampara, Chandan Gupta +5
Large language model (LLM)-based systems are increasingly deployed to conduct scientific research autonomously, yet whether their reasoning adheres to the epistemic norms that make…
Natural Language Embeddings of Synthesis and Testing conditions Enhance Glass Dissolution Prediction
Sajid Mannan, K. Sidharth Nambudiripad, Indrajeet Mandal +2
Long-term chemical durability of glass, crucial for immobilizing nuclear waste, is governed by glass properties such as composition, surface geometry, as well as external factors l…
Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales +2
Universal Machine Learning Interactomic Potentials (MLIPs) enable accelerated simulations for materials discovery. However, current research efforts fail to impactfully utilize MLI…