2 citations · 6 across the 7 of their papers we have counts for
8 papers
AI scientists produce results without reasoning scientifically
Martiño Ríos-García, 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…
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…
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…
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…
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan +7
Equivariant graph neural networks force fields (EGraFFs) have shown great promise in modelling complex interactions in atomic systems by exploiting the graphs' inherent symmetries.…
Glass Hardness: Predicting Composition and Load Effects via Symbolic Reasoning-Informed Machine Learning
Sajid Mannan, Mohd Zaki, Suresh Bishnoi +8
Glass hardness varies in a non-linear fashion with the chemical composition and applied load, a phenomenon known as the indentation size effect (ISE), which is challenging to predi…
Accelerated Design of Chalcogenide Glasses through Interpretable Machine Learning for Composition Property Relationships
Sayam Singla, Sajid Mannan, Mohd Zaki +1
Chalcogenide glasses possess several outstanding properties that enable several ground breaking applications, such as optical discs, infrared cameras, and thermal imaging systems.…