Computational discovery of transition-metal complexes: from high-throughput screening to machine learning
Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
Polyoxometalate‐soft matter composite materials: design strategies, applications, and future directions
JH Kruse, M Langer, I Romanenko… - Advanced functional …, 2022 - Wiley Online Library
Molecular metal oxides or polyoxometalates (POMs) offer unrivaled properties in areas
ranging from catalysis and energy conversion through to molecular electronics, biomimetics …
ranging from catalysis and energy conversion through to molecular electronics, biomimetics …
Automated in silico design of homogeneous catalysts
M Foscato, VR Jensen - ACS catalysis, 2020 - ACS Publications
Catalyst discovery is increasingly relying on computational chemistry, and many of the
computational tools are currently being automated. The state of this automation and the …
computational tools are currently being automated. The state of this automation and the …
AARON: an automated reaction optimizer for new catalysts
Y Guan, VM Ingman, BJ Rooks… - Journal of chemical …, 2018 - ACS Publications
We describe an open-source computational toolkit (AARON: An Automated Reaction
Optimizer for New catalysts) that automates the quantum mechanical geometry optimization …
Optimizer for New catalysts) that automates the quantum mechanical geometry optimization …
Theoretical study on conformational energies of transition metal complexes
Conformational energies are an important chemical property for which a performance
assessment of theoretical methods is mandatory. Existing benchmark sets are often limited …
assessment of theoretical methods is mandatory. Existing benchmark sets are often limited …
Designing in the face of uncertainty: exploiting electronic structure and machine learning models for discovery in inorganic chemistry
Recent transformative advances in computing power and algorithms have made
computational chemistry central to the discovery and design of new molecules and …
computational chemistry central to the discovery and design of new molecules and …
Putting density functional theory to the test in machine-learning-accelerated materials discovery
Accelerated discovery with machine learning (ML) has begun to provide the advances in
efficiency needed to overcome the combinatorial challenge of computational materials …
efficiency needed to overcome the combinatorial challenge of computational materials …
Learning from failure: predicting electronic structure calculation outcomes with machine learning models
High-throughput computational screening for chemical discovery mandates the automated
and unsupervised simulation of thousands of new molecules and materials. In challenging …
and unsupervised simulation of thousands of new molecules and materials. In challenging …
Computational methods for asymmetric catalysis
Impressive progress in computational asymmetric catalysis has been made in the past
twenty years owing to advancements in algorithm and method development for predicting …
twenty years owing to advancements in algorithm and method development for predicting …
The quest to simulate excited-state dynamics of transition metal complexes
This Perspective describes current computational efforts in the field of simulating
photodynamics of transition metal complexes. We present the typical workflows and feature …
photodynamics of transition metal complexes. We present the typical workflows and feature …