Toward accelerated discovery of solid catalysts using extrapolative machine learning approach
T Toyao - Chemistry Letters, 2024 - academic.oup.com
Designing novel catalysts is pivotal for overcoming numerous energy and environmental
challenges. Although data science approaches, particularly machine learning (ML) …
challenges. Although data science approaches, particularly machine learning (ML) …
What is a minimal working example for a self-driving laboratory?
Self-driving laboratories (SDLs) are the future; however, the capital and expertise required
can be daunting. We introduce the idea of an optimization task for less than 100 USD, a …
can be daunting. We introduce the idea of an optimization task for less than 100 USD, a …
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach
Designing novel catalysts is key to solving many energy and environmental challenges.
Despite the promise that data science approaches, including machine learning (ML), can …
Despite the promise that data science approaches, including machine learning (ML), can …
Compactness matters: Improving Bayesian optimization efficiency of materials formulations through invariant search spaces
Would you rather search for a line inside a cube or a point inside a square? Physics-based
simulations and wet-lab experiments often have symmetries (degeneracies) that allow …
simulations and wet-lab experiments often have symmetries (degeneracies) that allow …
High-throughput ensemble-learning-driven band gap prediction of double perovskites solar cells absorber
S Djeradi, T Dahame, MA Fadla, B Bentria… - Machine Learning and …, 2024 - mdpi.com
Perovskite materials have attracted much attention in recent years due to their high
performance, especially in the field of photovoltaics. However, the dark side of these …
performance, especially in the field of photovoltaics. However, the dark side of these …
Tales from Sabbatical II: During your stay
TD Sparks - Matter, 2023 - cell.com
This Matter of Opinion is the second of three in a" Tales from Sabbatical" series focusing on
sabbaticals in academia with perspectives before you go, during the sabbatical, and once …
sabbaticals in academia with perspectives before you go, during the sabbatical, and once …
Predicting mechanical properties of non-equimolar high-entropy carbides using machine learning
X Zhao, S Cheng, S Yu, J Zheng, RZ Zhang, M Guo - Digital Discovery, 2025 - pubs.rsc.org
High-entropy carbides (HECs) have garnered significant attention due to their unique
mechanical properties. However, the design of novel HECs has been limited by extensive …
mechanical properties. However, the design of novel HECs has been limited by extensive …
The most compact search space is not always the most efficient: a case study on maximizing solid rocket fuel packing fraction via constrained bayesian optimization
Would you rather search for a line inside a cube or a point inside a square? This type of
solution degeneracy often exists in physics-based simulations and wet-lab experiments, but …
solution degeneracy often exists in physics-based simulations and wet-lab experiments, but …
Effect of reducible and irreducible search space representations on adaptive design efficiency: a case study on maximizing packing fraction for solid rocket fuel …
Would you rather search for a point inside of a line or a line inside of a rectangle? This is a
type of solution degeneracy that often exists physics-based simulations and wetlab …
type of solution degeneracy that often exists physics-based simulations and wetlab …