In pursuit of the exceptional: Research directions for machine learning in chemical and materials science
Exceptional molecules and materials with one or more extraordinary properties are both
technologically valuable and fundamentally interesting, because they often involve new …
technologically valuable and fundamentally interesting, because they often involve new …
Accelerating the prediction of stable materials with machine learning
Despite the rise in computing power, the large space of possible combinations of elements
and crystal structure types makes large-scale high-throughput surveys of stable materials …
and crystal structure types makes large-scale high-throughput surveys of stable materials …
Open catalyst 2020 (OC20) dataset and community challenges
Catalyst discovery and optimization is key to solving many societal and energy challenges
including solar fuel synthesis, long-term energy storage, and renewable fertilizer production …
including solar fuel synthesis, long-term energy storage, and renewable fertilizer production …
[HTML][HTML] Prediction of nature of band gap of perovskite oxides (ABO3) using a machine learning approach
A material's electronic properties and technological utility depend on its band gap value and
the nature of band gap (ie direct or indirect). This nature of band gaps is notoriously difficult …
the nature of band gap (ie direct or indirect). This nature of band gaps is notoriously difficult …
Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)
Needle-in-a-Haystack problems exist across a wide range of applications including rare
disease prediction, ecological resource management, fraud detection, and material property …
disease prediction, ecological resource management, fraud detection, and material property …
Discovery of complex oxides via automated experiments and data science
L Yang, JA Haber, Z Armstrong… - Proceedings of the …, 2021 - National Acad Sciences
The quest to identify materials with tailored properties is increasingly expanding into high-
order composition spaces, with a corresponding combinatorial explosion in the number of …
order composition spaces, with a corresponding combinatorial explosion in the number of …
ET-AL: Entropy-targeted active learning for bias mitigation in materials data
Growing materials data and data-driven informatics drastically promote the discovery and
design of materials. While there are significant advancements in data-driven models, the …
design of materials. While there are significant advancements in data-driven models, the …
Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization
We focus on gas sorption within metal-organic frameworks (MOFs) for energy applications
and identify the minimal set of crystallographic descriptors underpinning the most important …
and identify the minimal set of crystallographic descriptors underpinning the most important …
A perspective on digital knowledge representation in materials science and engineering
The amount of data generated worldwide is constantly increasing. These data come from a
wide variety of sources and systems, are processed differently, have a multitude of formats …
wide variety of sources and systems, are processed differently, have a multitude of formats …
[HTML][HTML] Data driven design of alkali-activated concrete using sequential learning
This paper presents a novel approach for developing sustainable building materials through
Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali …
Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali …