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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 …
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 …
Choosing the right molecular machine learning potential
Quantum-chemistry simulations based on potential energy surfaces of molecules provide
invaluable insight into the physicochemical processes at the atomistic level and yield such …
invaluable insight into the physicochemical processes at the atomistic level and yield such …
[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 …
Ab initio machine learning in chemical compound space
Chemical compound space (CCS), the set of all theoretically conceivable combinations of
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …
chemical elements and (meta-) stable geometries that make up matter, is colossal. The first …
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 …
MLatom software ecosystem for surface hop** dynamics in Python with quantum mechanical and machine learning methods
We present an open-source MLatom@ XACS software ecosystem for on-the-fly surface
hop** nonadiabatic dynamics based on the Landau–Zener–Belyaev–Lebedev algorithm …
hop** nonadiabatic dynamics based on the Landau–Zener–Belyaev–Lebedev algorithm …
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Computational study of molecules and materials from first principles is a cornerstone of
physics, chemistry, and materials science, but limited by the cost of accurate and precise …
physics, chemistry, and materials science, but limited by the cost of accurate and precise …
Advances of machine learning in materials science: Ideas and techniques
In this big data era, the use of large dataset in conjunction with machine learning (ML) has
been increasingly popular in both industry and academia. In recent times, the field of …
been increasingly popular in both industry and academia. In recent times, the field of …
AI in computational chemistry through the lens of a decade-long journey
This article gives a perspective on the progress of AI tools in computational chemistry
through the lens of the author's decade-long contributions put in the wider context of the …
through the lens of the author's decade-long contributions put in the wider context of the …