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Home / Resources / Research / Publications / Papers By Citrine

Papers By Citrine

2021

Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures

Single-crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure determination and refinement is not always straightforward. Methods for simplifying and rationalizing the path to the most optimal crystal structure model have been incorporated into various data processing and crystal structure solution […]

Papers By Citrine
Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities

Data‐driven science and technology have helped achieve meaningful technological advancements in areas such as materials/drug discovery and health care, but efforts to apply high‐end data science algorithms to the areas of glass and ceramics are still limited. Many glass and ceramic researchers are interested in enhancing their work by using more data and data analytics […]

Papers By Citrine
Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches

The rapidly growing interest in machine learning (ML) for materials discovery has resulted in a large body of published work. However, only a small fraction of these publications includes confirmation of ML predictions, either via experiment or via physics-based simulations. In this review, we first identify the core components common to materials informatics discovery pipelines, […]

Papers By Citrine
Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials

Materials exhibiting higher mobilities than conventional organic semiconducting materials such as fullerenes and fused thiophenes are in high demand for applications such as printed electronics, organic solar cells, and image sensors. In order to discover new molecules that might show improved charge mobility, combined density functional theory (DFT) and molecular dynamics (MD) calculations were performed, […]

Papers By Citrine
Expanded dataset of mechanical properties and observed phases of multi-principal element alloys

This data article presents a compilation of mechanical properties of 630 multi-principal element alloys (MPEAs). Built upon recently published MPEA databases, this article includes updated records from previous reviews (with minor error corrections) along with new data from articles that were published since 2019. The extracted properties include reported composition, processing method, microstructure, density, hardness, yield […]

Papers By Citrine
Using Machine Learning to Explore Formulations Recipes with New Ingredients

A system and a method are disclosed that, in an embodiment, receive first input from a user of a candidate formulation recipe, and second input from the user of target properties and target property constraints. The system inputs the first input into a machine learning model, the model having been trained using historical training data, […]

Papers By Citrine

2020

Machine-Learned Metrics for Predicting the Likelihood of Success in Materials Discovery

Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials with machine learning (ML), which amounts to evaluating whether a given candidate is a piece of straw or a needle, less attention has been paid to a […]

Papers By Citrine
Design space visualization for guiding investments in biodegradable and sustainably sourced materials

In many materials development projects, scientists and research heads make decisions to guide the project direction. For example, scientists may decide which processing steps to use, what elements to include in their material selection, or from what suppliers to source their materials. Research heads may decide whether to invest development effort in reducing the environmental […]

Papers By Citrine

2019

Assessing the Frontier: Active Learning, Model Accuracy, and Multi objective Materials Discovery and Optimization

Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates—active learning. However, standard global error metrics for model quality are not predictive of discovery performance, and can be misleading. We introduce the notion of Pareto shell error to help judge the suitability of a model for proposing material candidates. Further, through […]

Papers By Citrine

2018

Machine Learning for Alloy Composition and Process Optimization

The drive for greater efficiency in turbomachinery has led to increasingly stringent specifications for the materials used. Current methods for optimizing alloy composition and processing to meet these requirements typically rely on a combination of expert judgment and trial and error. Machine learning offers an alternative approach that leverages data resources to significantly accelerate the […]

Papers By Citrine

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