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Found 8 papers, 2 papers with code
Are LLMs Ready for Real-World Materials Discovery?
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.
Reconstructing Materials Tetrahedron: Challenges in Materials Information Extraction
The discovery of new materials has a documented history of propelling human progress for centuries and more.
CoNO: Complex Neural Operator for Continuous Dynamical Systems
These models have successfully solved continuous dynamical systems represented by differential equations, viz weather forecasting, fluid flow, or solid mechanics.
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields for Atomistic Simulations
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.
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CoDBench: A Critical Evaluation of Data-driven Models for Continuous Dynamical Systems
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.
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks
Here, we present a Hamiltonian graph neural network (HGNN), a physics-enforced GNN that learns the dynamics of systems directly from their trajectory.
Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems
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.
Momentum Conserving Lagrangian Neural Networks
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.
