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Found 12 papers, 3 papers with code
MaScQA: A Question Answering Dataset for Investigating Materials Science Knowledge of Large Language Models
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.
Graph Neural Stochastic Differential Equations for Learning Brownian Dynamics
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.
StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
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.
Cementron: Machine Learning the Constituent Phases in Cement Clinker from Optical Images
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.
Predicting Oxide Glass Properties with Low Complexity Neural Network and Physical and Chemical Descriptors
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.
Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network
Lagrangian and Hamiltonian neural networks (LNNs and HNNs, respectively) encode strong inductive biases that allow them to outperform other models of physical systems significantly.
Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems
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.
Learning the Dynamics of Particle-based Systems with Lagrangian Graph Neural Networks
Physical systems are commonly represented as a combination of particles, the individual dynamics of which govern the system dynamics.
DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles
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.
Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias
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.
MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction
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.
Looking Through Glass: Knowledge Discovery from Materials Science Literature using Natural Language Processing
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
