Deep learning for molecular design—a review of the state of the art
In the space of only a few years, deep generative modeling has revolutionized how we think
of artificial creativity, yielding autonomous systems which produce original images, music …
of artificial creativity, yielding autonomous systems which produce original images, music …
From platform to knowledge graph: evolution of laboratory automation
High-fidelity computer-aided experimentation is becoming more accessible with the
development of computing power and artificial intelligence tools. The advancement of …
development of computing power and artificial intelligence tools. The advancement of …
Learning to make chemical predictions: the interplay of feature representation, data, and machine learning methods
Recently, supervised machine learning has been ascending in providing new predictive
approaches for chemical, biological, and materials sciences applications. In this …
approaches for chemical, biological, and materials sciences applications. In this …
Advances of machine learning in molecular modeling and simulation
In this review, we highlight recent developments in the application of machine learning for
molecular modeling and simulation. After giving a brief overview of the foundations …
molecular modeling and simulation. After giving a brief overview of the foundations …
Machine learning with enormous “synthetic” data sets: predicting glass transition temperature of polyimides using graph convolutional neural networks
In the present work, we address the problem of utilizing machine learning (ML) methods to
predict the thermal properties of polymers by establishing “structure–property” relationships …
predict the thermal properties of polymers by establishing “structure–property” relationships …
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 …
Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data …
Applying artificial intelligence to materials research requires abundant curated experimental
data and the ability for algorithms to request new experiments. ESCALATE (Experiment …
data and the ability for algorithms to request new experiments. ESCALATE (Experiment …
ChemML: A machine learning and informatics program package for the analysis, mining, and modeling of chemical and materials data
ChemML is an open machine learning (ML) and informatics program suite that is designed
to support and advance the data‐driven research paradigm that is currently emerging in the …
to support and advance the data‐driven research paradigm that is currently emerging in the …
Machine learning of coupled cluster (T)-energy corrections via delta (Δ)-learning
Accurate thermochemistry is essential in many chemical disciplines, such as astro-,
atmospheric, or combustion chemistry. These areas often involve fleetingly existent …
atmospheric, or combustion chemistry. These areas often involve fleetingly existent …
Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining
We present a high-throughput computational study to identify novel polyimides (PIs) with
exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our …
exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our …