Interpretable machine learning for knowledge generation in heterogeneous catalysis
Most applications of machine learning in heterogeneous catalysis thus far have used black-
box models to predict computable physical properties (descriptors), such as adsorption or …
box models to predict computable physical properties (descriptors), such as adsorption or …
Computational discovery of transition-metal complexes: from high-throughput screening to machine learning
Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
materials. The behavior of the metal–organic bond, while very tunable for achieving target …
Autonomous experimentation systems for materials development: A community perspective
Solutions to many of the world's problems depend upon materials research and
development. However, advanced materials can take decades to discover and decades …
development. However, advanced materials can take decades to discover and decades …
Emerging materials intelligence ecosystems propelled by machine learning
The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its
successes and promises, several AI ecosystems are blossoming, many of them within the …
successes and promises, several AI ecosystems are blossoming, many of them within the …
Accelerating high-throughput virtual screening through molecular pool-based active learning
DE Graff, EI Shakhnovich, CW Coley - Chemical science, 2021 - pubs.rsc.org
Structure-based virtual screening is an important tool in early stage drug discovery that
scores the interactions between a target protein and candidate ligands. As virtual libraries …
scores the interactions between a target protein and candidate ligands. As virtual libraries …
Theory-guided experimental design in battery materials research
A reliable energy storage ecosystem is imperative for a renewable energy future, and
continued research is needed to develop promising rechargeable battery chemistries. To …
continued research is needed to develop promising rechargeable battery chemistries. To …
Perspective—combining physics and machine learning to predict battery lifetime
Forecasting the health of a battery is a modeling effort that is critical to driving improvements
in and adoption of electric vehicles. Purely physics-based models and purely data-driven …
in and adoption of electric vehicles. Purely physics-based models and purely data-driven …
Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation
The ever-increasing demand for novel materials with superior properties inspires retrofitting
traditional research paradigms in the era of artificial intelligence and automation. An …
traditional research paradigms in the era of artificial intelligence and automation. An …
Machine-learning predictions of polymer properties with Polymer Genome
Polymer Genome is a web-based machine-learning capability to perform near-
instantaneous predictions of a variety of polymer properties. The prediction models are …
instantaneous predictions of a variety of polymer properties. The prediction models are …
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 …