FAIR data enabling new horizons for materials research
The prosperity and lifestyle of our society are very much governed by achievements in
condensed matter physics, chemistry and materials science, because new products for …
condensed matter physics, chemistry and materials science, because new products for …
Extending machine learning beyond interatomic potentials for predicting molecular properties
Abstract Machine learning (ML) is becoming a method of choice for modelling complex
chemical processes and materials. ML provides a surrogate model trained on a reference …
chemical processes and materials. ML provides a surrogate model trained on a reference …
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 …
Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials
Crystal‐graph attention neural networks have emerged recently as remarkable tools for the
prediction of thermodynamic stability. The efficacy of their learning capabilities and their …
prediction of thermodynamic stability. The efficacy of their learning capabilities and their …
A perspective on sustainable computational chemistry software development and integration
The power of quantum chemistry to predict the ground and excited state properties of
complex chemical systems has driven the development of computational quantum chemistry …
complex chemical systems has driven the development of computational quantum chemistry …
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
SchNetPack is a versatile neural network toolbox that addresses both the requirements of
method development and the application of atomistic machine learning. Version 2.0 comes …
method development and the application of atomistic machine learning. Version 2.0 comes …
Operando modeling of zeolite-catalyzed reactions using first-principles molecular dynamics simulations
Within this Perspective, we critically reflect on the role of first-principles molecular dynamics
(MD) simulations in unraveling the catalytic function within zeolites under operating …
(MD) simulations in unraveling the catalytic function within zeolites under operating …
DeepQMC: An open-source software suite for variational optimization of deep-learning molecular wave functions
Computing accurate yet efficient approximations to the solutions of the electronic
Schrödinger equation has been a paramount challenge of computational chemistry for …
Schrödinger equation has been a paramount challenge of computational chemistry for …
Nonadiabatic coupling in trajectory surface hopping: accurate time derivative couplings by the curvature-driven approximation
X Zhao, ICD Merritt, R Lei, Y Shu… - Journal of Chemical …, 2023 - ACS Publications
Trajectory surface hopping (TSH) is a widely used mixed quantum-classical dynamics
method that is used to simulate molecular dynamics with multiple electronic states. In TSH …
method that is used to simulate molecular dynamics with multiple electronic states. In TSH …
Nonempirical range-separated hybrid functional with spatially dependent screened exchange
Electronic structure calculations based on density functional theory (DFT) have successfully
predicted numerous ground-state properties of a variety of molecules and materials …
predicted numerous ground-state properties of a variety of molecules and materials …