no code implementations • 17 Aug 2023 • Mohd Zaki, Jayadeva, Mausam, N. M. Anoop Krishnan
Further, we evaluate the performance of GPT-3. 5 and GPT-4 models on solving these questions via zero-shot and chain of thought prompting.
no code implementations • 20 Jun 2023 • Suresh Bishnoi, Jayadeva, Sayan Ranu, N. M. Anoop Krishnan
Here, we propose a framework, namely Brownian graph neural networks (BROGNET), combining stochastic differential equations (SDEs) and GNNs to learn Brownian dynamics directly from the trajectory.
no code implementations • 29 Jan 2023 • Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry, Sayan Ranu, N. M. Anoop Krishnan
Optimization of atomic structures presents a challenging problem, due to their highly rough and non-convex energy landscape, with wide applications in the fields of drug design, materials discovery, and mechanics.
no code implementations • 6 Nov 2022 • Mohd Zaki, Siddhant Sharma, Sunil Kumar Gurjar, Raju Goyal, Jayadeva, N. M. Anoop Krishnan
Specifically, we finetune the image detection and segmentation model Detectron-2 on the cement microstructure to develop a model for detecting the cement phases, namely, Cementron.
no code implementations • 19 Oct 2022 • Suresh Bishnoi, Skyler Badge, Jayadeva, N. M. Anoop Krishnan
In addition, we combine the LCNN with physical and chemical descriptors that allow the development of universal models that can provide predictions for components beyond the training set.
1 code implementation • 23 Sep 2022 • Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan
Lagrangian and Hamiltonian neural networks (LNNs and HNNs, respectively) encode strong inductive biases that allow them to outperform other models of physical systems significantly.
no code implementations • 22 Sep 2022 • Suresh Bishnoi, Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan
Neural networks with physics based inductive biases such as Lagrangian neural networks (LNN), and Hamiltonian neural networks (HNN) learn the dynamics of physical systems by encoding strong inductive biases.
no code implementations • 3 Sep 2022 • Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan
Physical systems are commonly represented as a combination of particles, the individual dynamics of which govern the system dynamics.
1 code implementation • 3 Jul 2022 • Tanishq Gupta, Mohd Zaki, Devanshi Khatsuriya, Kausik Hira, N. M. Anoop Krishnan, Mausam
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 published research articles.
no code implementations • 7 Oct 2021 • Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan
However, these models still suffer from issues such as inability to generalize to arbitrary system sizes, poor interpretability, and most importantly, inability to learn translational and rotational symmetries, which lead to the conservation laws of linear and angular momentum, respectively.
1 code implementation • 30 Sep 2021 • Tanishq Gupta, Mohd Zaki, N. M. Anoop Krishnan, Mausam
Here, we present a materials-aware language model, namely, MatSciBERT, which is trained on a large corpus of scientific literature published in the materials domain.
no code implementations • 5 Jan 2021 • Vineeth Venugopal, Sourav Sahoo, Mohd Zaki, Manish Agarwal, Nitya Nand Gosvami, N. M. Anoop Krishnan
Most of the knowledge in materials science literature is in the form of unstructured data such as text and images.
Image Comprehension Digital Libraries Computational Physics Data Analysis, Statistics and Probability