182 citations · 251 across the 19 of their papers we have counts for
20 papers
Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
Karn Tiwari, Niladri Dutta, N M Anoop Krishnan +1
Modeling interacting dynamical systems requires capturing spatial interactions alongside long-range temporal dependencies. Graph neural networks (GNNs) provide a natural representa…
GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
Parth Verma, Parv P. Singh, Vipul Garg +3
Graph Neural Networks (GNNs) have revolutionized Neural Force Fields for atomistic simulations, achieving near-quantum accuracy at reduced cost, yet adapting these models to new ch…
Beyond Manual Curation: Augmenting Targeted Protein Degradation Databases via Agentic Literature Extraction Workflows
Yaochen Rao, Farzaneh Jalalypour, N. M. Anoop Krishnan +1
Predictive models in biomedicine depend on structured assay data locked in the text, tables, and supplements of primary publications. This bottleneck is especially acute in targete…
Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery
Harshit Bisht, Vinay Kumar, Kevin Maik Jablonka +2
A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they already function as co-scientists,…
MDGYM: Benchmarking AI Agents on Molecular Simulations
Vinay Kumar, Satyendra Rajput, Mausam +1
The promise of AI-driven scientific discovery hinges on whether AI agents can autonomously design and execute the computational workflows that underpin modern science. Molecular dy…
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…
MatSKRAFT: A framework for large-scale materials knowledge extraction from scientific tables
Kausik Hira, Mohd Zaki, Mausam +1
Scientific progress increasingly depends on synthesizing knowledge across vast literature, yet most experimental data remains trapped in semi-structured formats that resist systema…
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…
Latent Mamba Operator for Partial Differential Equations
Karn Tiwari, Niladri Dutta, N M Anoop Krishnan +1
Neural operators have emerged as powerful data-driven frameworks for solving Partial Differential Equations (PDEs), offering significant speedups over numerical methods. However, e…
CoNO: Complex Neural Operator for Continous Dynamical Physical Systems
Karn Tiwari, N M Anoop Krishnan, A P Prathosh
Neural operators extend data-driven models to map between infinite-dimensional functional spaces. While these operators perform effectively in either the time or frequency domain,…
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…
CoNO: Complex Neural Operator for Continuous Dynamical Systems
Karn Tiwari, N M Anoop Krishnan, Prathosh A P
Neural operators extend data-driven models to map between infinite-dimensional functional spaces. These models have successfully solved continuous dynamical systems represented by…
CoDBench: A Critical Evaluation of Data-driven Models for Continuous Dynamical Systems
Priyanshu Burark, Karn Tiwari, Meer Mehran Rashid +2
Continuous dynamical systems, characterized by differential equations, are ubiquitously used to model several important problems: plasma dynamics, flow through porous media, weathe…
Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction
Kausik Hira, Mohd Zaki, Dhruvil Sheth +2
The discovery of new materials has a documented history of propelling human progress for centuries and more. The behaviour of a material is a function of its composition, structure…
MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models
Mohd Zaki, Jayadeva, Mausam +1
Information extraction and textual comprehension from materials literature are vital for developing an exhaustive knowledge base that enables accelerated materials discovery. Langu…
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…
DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles
Tanishq Gupta, Mohd Zaki, Devanshi Khatsuriya +3
A crucial component in the curation of KB for a scientific domain (e.g., materials science, foods & nutrition, fuels) is information extraction from tables in the domain's publishe…
A New Transferable Interatomic Potential for Molecular Dynamics Simulations of Borosilicate Glasses
Mengyi Wang, N. M. Anoop Krishnan, Bu Wang +3
Borosilicate glasses are traditionally challenging to model using atomic scale simulations due to the composition and thermal history dependence of the coordination state of B atom…