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 …
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 …
Technological advancements in recent decades have greatly transformed the field of
material chemistry. Juxtaposing the accentuating energy demand with the pollution …
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 …
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
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 …
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
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 …
field to the SAMPL9 bCD host–guest binding free energy prediction challenge that …
Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design
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 …
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 …
Hamiltonian. Expansions of this perturbation in a Taylor series, ie, quantum alchemy, have …
Machine learning corrected alchemical perturbation density functional theory for catalysis applications
Alchemical perturbation density functional theory (APDFT) has promise for enabling
computational screening of hypothetical catalyst sites. Here, we analyze errors in first order …
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 …
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 …
alchemical perturbation density functional theory (APDFT). APDFT refers to perturbation …