Ab initio machine learning in chemical compound space

B Huang, OA Von Lilienfeld - Chemical reviews, 2021 - ACS Publications
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 …

[HTML][HTML] Integration of data-intensive, machine learning and robotic experimental approaches for accelerated discovery of catalysts in renewable energy-related …

OA Moses, W Chen, ML Adam, Z Wang, K Liu… - Materials Reports …, 2021 - Elsevier
Technological advancements in recent decades have greatly transformed the field of
material chemistry. Juxtaposing the accentuating energy demand with the pollution …

From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design

J Benavides-Hernández, F Dumeignil - ACS Catalysis, 2024 - ACS Publications
This review paper delves into synergistic integration of artificial intelligence (AI) and
machine learning (ML) with high-throughput experimentation (HTE) in the field of …

Applying Large Graph Neural Networks to Predict Transition Metal Complex Energies Using the tmQM_wB97MV Data Set

AG Garrison, J Heras-Domingo, JR Kitchin… - Journal of Chemical …, 2023 - ACS Publications
Machine learning (ML) methods have shown promise for discovering novel catalysts but are
often restricted to specific chemical domains. Generalizable ML models require large and …

Taming multiple binding poses in alchemical binding free energy prediction: the β-cyclodextrin host–guest SAMPL9 blinded challenge

S Khuttan, S Azimi, JZ Wu, S Dick, C Wu… - Physical Chemistry …, 2023 - pubs.rsc.org
We apply the Alchemical Transfer Method (ATM) and a bespoke fixed partial charge force
field to the SAMPL9 bCD host–guest binding free energy prediction challenge that …

Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design

K Karandashev, J Weinreich, S Heinen… - Journal of Chemical …, 2023 - ACS Publications
Optimizing a target function over the space of organic molecules is an important problem
appearing in many fields of applied science but also a very difficult one due to the vast …

[HTML][HTML] Arbitrarily accurate quantum alchemy

GF von Rudorff - The Journal of Chemical Physics, 2021 - pubs.aip.org
Doping compounds can be considered a perturbation to the nuclear charges in a molecular
Hamiltonian. Expansions of this perturbation in a Taylor series, ie, quantum alchemy, have …

Machine learning corrected alchemical perturbation density functional theory for catalysis applications

CD Griego, L Zhao, K Saravanan, JA Keith - AIChE Journal, 2020 - Wiley Online Library
Alchemical perturbation density functional theory (APDFT) has promise for enabling
computational screening of hypothetical catalyst sites. Here, we analyze errors in first order …

Effects of perturbation order and basis set on alchemical predictions

G Domenichini, GF von Rudorff… - The Journal of chemical …, 2020 - pubs.aip.org
Alchemical perturbation density functional theory has been shown to be an efficient and
computationally inexpensive way to explore chemical compound space. We investigate …

Alchemical geometry relaxation

G Domenichini, OA von Lilienfeld - The Journal of Chemical Physics, 2022 - pubs.aip.org
We propose the relaxation of geometries throughout chemical compound space using
alchemical perturbation density functional theory (APDFT). APDFT refers to perturbation …