In pursuit of the exceptional: Research directions for machine learning in chemical and materials science
Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …
technologically valuable and fundamentally interesting, because they often involve new …
Accelerating the prediction of stable materials with machine learning
Despite the rise in computing power, the large space of possible combinations of elements
and crystal structure types makes large-scale high-throughput surveys of stable materials …
and crystal structure types makes large-scale high-throughput surveys of stable materials …
Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study
In real-world materials research, machine learning (ML) models are usually expected to
predict and discover novel exceptional materials that deviate from the known materials. It is …
predict and discover novel exceptional materials that deviate from the known materials. It is …
Deep learning generative model for crystal structure prediction
Recent advances in deep learning generative models (GMs) have created high capabilities
in accessing and assessing complex high-dimensional data, allowing superior efficiency in …
in accessing and assessing complex high-dimensional data, allowing superior efficiency in …
WyCryst: Wyckoff inorganic crystal generator framework
Recent advancements in property-directed generative design of inorganic materials account
for periodicity and global Euclidian symmetry through translations, rotations, and reflections; …
for periodicity and global Euclidian symmetry through translations, rotations, and reflections; …
cv-PINN: Efficient learning of variational physics-informed neural network with domain decomposition
We propose a novel approach for tackling scientific problems governed by differential
equations, based on the concept of a physics-informed neural networks (PINNs). The …
equations, based on the concept of a physics-informed neural networks (PINNs). The …
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 …
Improving biosensor accuracy and speed using dynamic signal change and theory-guided deep learning
False results and time delay are longstanding challenges in biosensing. While classification
models and deep learning may provide new opportunities for improving biosensor …
models and deep learning may provide new opportunities for improving biosensor …
Data-driven score-based models for generating stable structures with adaptive crystal cells
The discovery of new functional and stable materials is a big challenge due to its complexity.
This work aims at the generation of new crystal structures with desired properties, such as …
This work aims at the generation of new crystal structures with desired properties, such as …
A deep generative modeling architecture for designing lattice-constrained perovskite materials
In modern materials discovery, materials are now efficiently screened using machine
learning (ML) techniques with target-specific properties for meeting various engineering …
learning (ML) techniques with target-specific properties for meeting various engineering …