Machine learning force fields
In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …
numerous advances previously out of reach due to the computational complexity of …
Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Message passing neural networks have become a method of choice for learning on graphs,
in particular the prediction of chemical properties and the acceleration of molecular …
in particular the prediction of chemical properties and the acceleration of molecular …
A molecular descriptor of intramolecular noncovalent interaction for regulating optoelectronic properties of organic semiconductors
M Liu, X Han, H Chen, Q Peng, H Huang - Nature Communications, 2023 - nature.com
In recent years, intramolecular noncovalent interaction has become an important means to
modulate the optoelectronic performances of organic/polymeric semiconductors. However, it …
modulate the optoelectronic performances of organic/polymeric semiconductors. However, it …
Machine learning force fields: Recent advances and remaining challenges
In chemistry and physics, machine learning (ML) methods promise transformative impacts by
advancing modeling and improving our understanding of complex molecules and materials …
advancing modeling and improving our understanding of complex molecules and materials …
Shining light on porous liquids: from fundamentals to syntheses, applications and future challenges
D Wang, Y ** kinetics explored with machine learning potentials and path integral molecular dynamics
Proton hop** is a key reactive process within zeolite catalysis. However, the accurate
determination of its kinetics poses major challenges both for theoreticians and …
determination of its kinetics poses major challenges both for theoreticians and …
Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments
Molecular dynamics (MD) simulations allow insights into complex processes, but accurate
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …
Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting
In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum
mechanical data have seen tremendous success recently. Top-down approaches that learn …
mechanical data have seen tremendous success recently. Top-down approaches that learn …