no code implementations • 7 Feb 2024 • Santiago Miret, N M Anoop Krishnan
Given those shortcomings, we outline a framework for developing Materials Science LLMs (MatSci-LLMs) that are grounded in materials science knowledge and hypothesis generation followed by hypothesis testing.
1 code implementation • 12 Oct 2023 • Kausik Hira, Mohd Zaki, Dhruvil Sheth, Mausam, N M Anoop Krishnan
The discovery of new materials has a documented history of propelling human progress for centuries and more.
no code implementations • 3 Oct 2023 • Karn Tiwari, N M Anoop Krishnan, Prathosh A P
These models have successfully solved continuous dynamical systems represented by differential equations, viz weather forecasting, fluid flow, or solid mechanics.
no code implementations • 3 Oct 2023 • Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M Smedskjaer, Sayan Ranu, N M Anoop Krishnan
In addition to our thorough evaluation and analysis on eight existing datasets based on the benchmarking literature, we release two new benchmark datasets, propose four new metrics, and three challenging tasks.
Ranked #1 on Formation Energy on GeTe
no code implementations • 2 Oct 2023 • Priyanshu Burark, Karn Tiwari, Meer Mehran Rashid, Prathosh A P, N M Anoop Krishnan
Continuous dynamical systems, characterized by differential equations, are ubiquitously used to model several important problems: plasma dynamics, flow through porous media, weather forecasting, and epidemic dynamics.
no code implementations • 11 Jul 2023 • Suresh Bishnoi, Ravinder Bhattoo, Jayadeva, Sayan Ranu, N M Anoop Krishnan
Here, we present a Hamiltonian graph neural network (HGNN), a physics-enforced GNN that learns the dynamics of systems directly from their trajectory.
1 code implementation • 10 Nov 2022 • Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi, Ravinder Bhattoo, N M Anoop Krishnan, Sayan Ranu
We evaluate these models on spring, pendulum, gravitational, and 3D deformable solid systems to compare the performance in terms of rollout error, conserved quantities such as energy and momentum, and generalizability to unseen system sizes.
no code implementations • 29 Sep 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.