Recent advances in artificial intelligence boosting materials design for electrochemical energy storage
In the rapidly evolving landscape of electrochemical energy storage (EES), the advent of
artificial intelligence (AI) has emerged as a keystone for innovation in material design …
artificial intelligence (AI) has emerged as a keystone for innovation in material design …
Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design
K Choudhary - The Journal of Physical Chemistry Letters, 2024 - ACS Publications
Large language models (LLMs) such as generative pretrained transformers (GPTs) have
shown potential for various commercial applications, but their applicability for materials …
shown potential for various commercial applications, but their applicability for materials …
[HTML][HTML] Enhancing mechanical and bioinspired materials through generative AI approaches
The integration of generative artificial intelligence (AI) into the design and additive
manufacturing processes of mechanical and bioinspired materials has emerged as a …
manufacturing processes of mechanical and bioinspired materials has emerged as a …
MatText: Do language models need more than text & scale for materials modeling?
Effectively representing materials as text has the potential to leverage the vast
advancements of large language models (LLMs) for discovering new materials. While LLMs …
advancements of large language models (LLMs) for discovering new materials. While LLMs …
Llm-prop: Predicting physical and electronic properties of crystalline solids from their text descriptions
The prediction of crystal properties plays a crucial role in the crystal design process. Current
methods for predicting crystal properties focus on modeling crystal structures using graph …
methods for predicting crystal properties focus on modeling crystal structures using graph …
Towards understanding structure–property relations in materials with interpretable deep learning
Deep learning (DL) models currently employed in materials research exhibit certain
limitations in delivering meaningful information for interpreting predictions and …
limitations in delivering meaningful information for interpreting predictions and …
Graph-text contrastive learning of inorganic crystal structure toward a foundation model of inorganic materials
K Ozawa, T Suzuki, S Tonogai… - Science and Technology …, 2024 - Taylor & Francis
Developing foundation models for materials science has attracted attention. However, there
is a lack of studies on inorganic materials due to the difficulty in the comprehensive …
is a lack of studies on inorganic materials due to the difficulty in the comprehensive …
Coarse-grained crystal graph neural networks for reticular materials design
V Korolev, A Mitrofanov - Journal of Chemical Information and …, 2024 - ACS Publications
Reticular materials, including metal–organic frameworks and covalent organic frameworks,
combine the relative ease of synthesis and an impressive range of applications in various …
combine the relative ease of synthesis and an impressive range of applications in various …
Virtual laboratories: Transforming research with ai
New scientific knowledge is needed more urgently than ever, to address global challenges
such as climate change, sustainability, health, and societal well-being. Could artificial …
such as climate change, sustainability, health, and societal well-being. Could artificial …
Hybrid-LLM-GNN: integrating large language models and graph neural networks for enhanced materials property prediction
Graph-centric learning has attracted significant interest in materials informatics. Accordingly,
a family of graph-based machine learning models, primarily utilizing Graph Neural Networks …
a family of graph-based machine learning models, primarily utilizing Graph Neural Networks …