Gaussian process regression for materials and molecules
We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …
methods in computational materials science and chemistry. The focus of the present review …
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 …
Machine learning for electronically excited states of molecules
Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …
as well as photobiology and also play a role in material science. Their theoretical description …
Opportunities and challenges for machine learning in materials science
Advances in machine learning have impacted myriad areas of materials science, such as
the discovery of novel materials and the improvement of molecular simulations, with likely …
the discovery of novel materials and the improvement of molecular simulations, with likely …
Operando characterization of organic mixed ionic/electronic conducting materials
Operando characterization plays an important role in revealing the structure–property
relationships of organic mixed ionic/electronic conductors (OMIECs), enabling the direct …
relationships of organic mixed ionic/electronic conductors (OMIECs), enabling the direct …
Integrating data mining and machine learning to discover high-strength ductile titanium alloys
Based on the growing power of computational capabilities and algorithmic developments,
with the help of data-driven and high-throughput calculations, a new paradigm accelerating …
with the help of data-driven and high-throughput calculations, a new paradigm accelerating …
[HTML][HTML] r2SCAN-D4: Dispersion corrected meta-generalized gradient approximation for general chemical applications
We combine a regularized variant of the strongly constrained and appropriately normed
semilocal density functional [J. Sun, A. Ruzsinszky, and JP Perdew, Phys. Rev. Lett. 115 …
semilocal density functional [J. Sun, A. Ruzsinszky, and JP Perdew, Phys. Rev. Lett. 115 …
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 …
Application of high-throughput first-principles calculations in ceramic innovation
Recent technical progress in the industry has led to an urgent requirement on new materials
with enhanced multi-properties. To meet this multi-property requirement, the materials …
with enhanced multi-properties. To meet this multi-property requirement, the materials …
Simulating the electronic structure of spin defects on quantum computers
We present calculations of both the ground-and excited-state energies of spin defects in
solids carried out on a quantum computer, using a hybrid classical-quantum protocol. We …
solids carried out on a quantum computer, using a hybrid classical-quantum protocol. We …