182 citations · 251 across the 19 of their papers we have counts for
6 papers · 1 filter
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…
Are LLMs Ready for Real-World Materials Discovery?
Santiago Miret, N M Anoop Krishnan
Large Language Models (LLMs) create exciting possibilities for powerful language processing tools to accelerate research in materials science. While LLMs have great potential to ac…
Disorder-induced enhancement of lithium-ion transport in solid-state electrolytes
Zhimin Chen, Tao Du, N. M. Anoop Krishnan +2
Enhancing the ion conduction in solid electrolytes is critically important for the development of high-performance all-solid-state lithium-ion batteries (LIBs). Lithium thiophospha…
Revealing the Predictive Power of Neural Operators for Strain Evolution in Digital Composites
Meer Mehran Rashid, Souvik Chakraborty, N. M. Anoop Krishnan
The demand for high-performance materials, along with advanced synthesis technologies such as additive manufacturing and 3D printing, has spurred the development of hierarchical co…
Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors
Suresh Bishnoi, Skyler Badge, Jayadeva +1
Due to their disordered structure, glasses present a unique challenge in predicting the composition-property relationships. Recently, several attempts have been made to predict the…