Recently, some researchers have tried to use machine-learning models to discover transition state structures. However, models developed so far require considering two reactants as a single entity in which the reactants maintain the same orientation with respect to each other. Any othe...
language modelsmachine learningregressionMolecular properties and reactions form the foundation of chemical space. Over the years, innumerable molecules have been synthesized, a smaller fraction of them found immediate applications, while a larger proportion served as a testimony to creative and empirical ...
In this collection we highlight a selection of recent computational studies published in Nature Communications, featuring advances in computational chemistry methods and progress towards machine learning approaches.
Statistical tools based on machine learning are becoming integrated into chemistry research workflows. We discuss the elements necessary to train reliable, repeatable and reproducible models, and recommend a set of guidelines for machine learning reports
BMC Chemistry invited researchers to contribute to a new Collection examining the intersection of Chemistry and machine learning. Machine learning has ...
Kernel ridge regression (KRR) is a powerful and popular tool for supervised machine learning in quantum chemistry.1, 2, 7, 13, 14, 15, 16, 17, 18, 19, 20 It is also very instructional and relatively easy to understand and implement.5, 7, 11 General principles of ML will be demonstr...
fairchem.core: State of the art machine learning models for materials science and chemistry fairchem.data: Dataset downloads and input generation codes fairchem.demo: Python API for the Open Catalyst Demo fairchem.applications: Follow up applications and works (AdsorbML, CatTSunami, etc.) Install...
Currently, various machine learning techniques especially deep learning have been widely applied to different chemometrics areas, such as signal processing, exploratory data analysis, multivariate calibration, multiway data analysis, classification and r
We present machine learning (ML) models for hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD) strengths. Quantum chemical (QC) free energies in solution for 1:1 hydrogen-bonded complex formation to the reference molecules 4-fluorophenol and acetone serve as our target values. Our acc...
Recently, I have been interested in adding a confidence metric to the predictions made by a machine learning model I have been working on. In this blog post, I will outline a few strategies I have been exploring to do this. Powerful deep learning models like AlphaFold are great, not only...