Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
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 …
Deep dive into machine learning density functional theory for materials science and chemistry
With the growth of computational resources, the scope of electronic structure simulations has
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
increased greatly. Artificial intelligence and robust data analysis hold the promise to …
Data-driven design of electrocatalysts: principle, progress, and perspective
To achieve carbon neutrality, electrocatalysis has the potential to be applied in the
technological upgrading of numerous industries. Therefore, the search for high-performance …
technological upgrading of numerous industries. Therefore, the search for high-performance …
Application of density functional theory and machine learning in heterogenous-based catalytic reactions for hydrogen production
Various feedstocks such as natural gas, glycerol, biomass, methanol, ethane, and other
hydrocarbons can be reformed to generate hydrogen as a viable alternative source of …
hydrocarbons can be reformed to generate hydrogen as a viable alternative source of …
In silico chemical experiments in the Age of AI: From quantum chemistry to machine learning and back
A Aldossary, JA Campos‐Gonzalez‐Angulo… - Advanced …, 2024 - Wiley Online Library
Computational chemistry is an indispensable tool for understanding molecules and
predicting chemical properties. However, traditional computational methods face significant …
predicting chemical properties. However, traditional computational methods face significant …
Deep learning based spraying pattern recognition and prediction for electrohydrodynamic system
Effective recognition and prediction of spraying patterns for electrohydrodynamic (EHD)
process are extremely important for its applications in high quality micro/nanoparticles …
process are extremely important for its applications in high quality micro/nanoparticles …
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 …
Soft computing modeling and multiresponse optimization for production of microalgal biomass and lipid as bioenergy feedstock
N Sultana, SMZ Hossain, HA Albalooshi, SMB Chrouf… - Renewable Energy, 2021 - Elsevier
Microalga biomass is a reliable bioenergy feedstock to produce green fuel owing to its high
lipid and organic content. On the other hand, the microalgal biomass productivity as well as …
lipid and organic content. On the other hand, the microalgal biomass productivity as well as …
Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning
In previous work (Dandu et al., J. Phys. Chem. A, 2022, 126, 4528–4536), we were
successful in predicting accurate atomization energies of organic molecules using machine …
successful in predicting accurate atomization energies of organic molecules using machine …