Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Deep-learning density functional perturbation theory
Calculating perturbation response properties of materials from first principles provides a vital
link between theory and experiment, but is bottlenecked by the high computational cost …
link between theory and experiment, but is bottlenecked by the high computational cost …
Qh9: A quantum hamiltonian prediction benchmark for qm9 molecules
Supervised machine learning approaches have been increasingly used in accelerating
electronic structure prediction as surrogates of first-principle computational methods, such …
electronic structure prediction as surrogates of first-principle computational methods, such …
Neural-network Density Functional Theory Based on Variational Energy Minimization
Deep-learning density functional theory (DFT) shows great promise to significantly
accelerate material discovery and potentially revolutionize materials research. However …
accelerate material discovery and potentially revolutionize materials research. However …
DeepH-2: Enhancing deep-learning electronic structure via an equivariant local-coordinate transformer
Deep-learning electronic structure calculations show great potential for revolutionizing the
landscape of computational materials research. However, current neural-network …
landscape of computational materials research. However, current neural-network …
Equivariant neural network force fields for magnetic materials
Neural network force fields have significantly advanced ab initio atomistic simulations across
diverse fields. However, their application in the realm of magnetic materials is still in its early …
diverse fields. However, their application in the realm of magnetic materials is still in its early …
An equivariant graph neural network for the elasticity tensors of all seven crystal systems
The elasticity tensor is a fundamental material property that describes the elastic response of
a material to external force. The availability of full elasticity tensors for inorganic crystalline …
a material to external force. The availability of full elasticity tensors for inorganic crystalline …
Breaking the size limitation of nonadiabatic molecular dynamics in condensed matter systems with local descriptor machine learning
Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium
processes, such as photochemical reactions and charge transport. NA-MD application to …
processes, such as photochemical reactions and charge transport. NA-MD application to …
Interatomic Interaction Models for Magnetic Materials: Recent Advances
TS Kostiuchenko, AV Shapeev… - Chinese Physics …, 2024 - iopscience.iop.org
Atomistic modeling is a widely employed theoretical method of computational materials
science. It has found particular utility in the study of magnetic materials. Initially, magnetic …
science. It has found particular utility in the study of magnetic materials. Initially, magnetic …
[HTML][HTML] Integrating chemistry knowledge in large language models via prompt engineering
This paper presents a study on the integration of domain-specific knowledge in prompt
engineering to enhance the performance of large language models (LLMs) in scientific …
engineering to enhance the performance of large language models (LLMs) in scientific …