DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science
In this paper, the history, present status, and future of density-functional theory (DFT) is
informally reviewed and discussed by 70 workers in the field, including molecular scientists …
informally reviewed and discussed by 70 workers in the field, including molecular scientists …
The central role of density functional theory in the AI age
Density functional theory (DFT) plays a pivotal role in chemical and materials science
because of its relatively high predictive power, applicability, versatility, and computational …
because of its relatively high predictive power, applicability, versatility, and computational …
Metrics for benchmarking and uncertainty quantification: Quality, applicability, and best practices for machine learning in chemistry
This review aims to draw attention to two issues of concern when we set out to make
machine learning work in the chemical and materials domain, that is, statistical loss function …
machine learning work in the chemical and materials domain, that is, statistical loss function …
Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems
This research aims to explore more efficient machine learning (ML) algorithms with better
performance for short-term forecasting. Up-to-date literature shows a lack of research on …
performance for short-term forecasting. Up-to-date literature shows a lack of research on …
A new model of air quality prediction using lightweight machine learning
Air pollution has become one of the environmental concerns in recent years due to its
harmful threats to human health. To inform people about the air quality in their living areas, it …
harmful threats to human health. To inform people about the air quality in their living areas, it …
Machine learning, artificial intelligence, and chemistry: How smart algorithms are resha** simulation and the laboratory
D Kuntz, AK Wilson - Pure and Applied Chemistry, 2022 - degruyter.com
Abstract Machine learning and artificial intelligence are increasingly gaining in prominence
through image analysis, language processing, and automation, to name a few applications …
through image analysis, language processing, and automation, to name a few applications …
QDataSet, quantum datasets for machine learning
The availability of large-scale datasets on which to train, benchmark and test algorithms has
been central to the rapid development of machine learning as a discipline. Despite …
been central to the rapid development of machine learning as a discipline. Despite …
The long road to calibrated prediction uncertainty in computational chemistry
P Pernot - The Journal of Chemical Physics, 2022 - pubs.aip.org
Uncertainty quantification (UQ) in computational chemistry (CC) is still in its infancy. Very few
CC methods are designed to provide a confidence level on their predictions, and most users …
CC methods are designed to provide a confidence level on their predictions, and most users …
[HTML][HTML] On the quality requirements of demand prediction for dynamic public transport
Abstract As Public Transport (PT) becomes more dynamic and demand-responsive, it
increasingly depends on predictions of transport demand. But how accurate need such …
increasingly depends on predictions of transport demand. But how accurate need such …
Prediction uncertainty validation for computational chemists
P Pernot - The Journal of Chemical Physics, 2022 - pubs.aip.org
Validation of prediction uncertainty (PU) is becoming an essential task for modern
computational chemistry. Designed to quantify the reliability of predictions in meteorology …
computational chemistry. Designed to quantify the reliability of predictions in meteorology …