Molecular excited states through a machine learning lens
Theoretical simulations of electronic excitations and associated processes in molecules are
indispensable for fundamental research and technological innovations. However, such …
indispensable for fundamental research and technological innovations. However, such …
A look inside the black box of machine learning photodynamics simulations
J Li, SA Lopez - Accounts of Chemical Research, 2022 - ACS Publications
Conspectus Photochemical reactions are of great importance in chemistry, biology, and
materials science because they take advantage of a renewable energy source, mild reaction …
materials science because they take advantage of a renewable energy source, mild reaction …
[HTML][HTML] Perspective on integrating machine learning into computational chemistry and materials science
Machine learning (ML) methods are being used in almost every conceivable area of
electronic structure theory and molecular simulation. In particular, ML has become firmly …
electronic structure theory and molecular simulation. In particular, ML has become firmly …
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 …
Deep learning study of tyrosine reveals that roaming can lead to photodamage
Amino acids are among the building blocks of life, forming peptides and proteins, and have
been carefully 'selected'to prevent harmful reactions caused by light. To prevent …
been carefully 'selected'to prevent harmful reactions caused by light. To prevent …
High-throughput property-driven generative design of functional organic molecules
The design of molecules and materials with tailored properties is challenging, as candidate
molecules must satisfy multiple competing requirements that are often difficult to measure or …
molecules must satisfy multiple competing requirements that are often difficult to measure or …
A universal and accurate method for easily identifying components in Raman spectroscopy based on deep learning
X Fan, Y Wang, C Yu, Y Lv, H Zhang, Q Yang… - Analytical …, 2023 - ACS Publications
Raman spectroscopy has been widely used to provide the structural fingerprint for molecular
identification. Due to interference from coexisting components, noise, baseline, and …
identification. Due to interference from coexisting components, noise, baseline, and …
Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining density-functional theory and …
D Golze, M Hirvensalo, P Hernández-León… - Chemistry of …, 2022 - ACS Publications
We present a quantitatively accurate machine-learning (ML) model for the computational
prediction of core–electron binding energies, from which X-ray photoelectron spectroscopy …
prediction of core–electron binding energies, from which X-ray photoelectron spectroscopy …
A deep equivariant neural network approach for efficient hybrid density functional calculations
Hybrid density functional calculations are essential for accurate description of electronic
structure, yet their widespread use is restricted by the substantial computational cost. Here …
structure, yet their widespread use is restricted by the substantial computational cost. Here …
Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
Symmetry considerations are at the core of the major frameworks used to provide an
effective mathematical representation of atomic configurations that is then used in machine …
effective mathematical representation of atomic configurations that is then used in machine …