Home | Resources | Research

Resources
Research

Nature research paper
3D Printing of High-Strength Aluminum Alloys
3D printing, or additive manufacturing, of metals uses a direct energy source, such as a laser or electron beam, to alloy powders, but has succeeded for only a few metals. Often, large columnar grains and cracks are generated during the solidification stage. In this paper, John Martin et al. confront...
Papers Mentioning Citrine
Learn More
A Case Study of Multimodal, Multi-Institutional Data Management for the Combinatorial Materials Science Community
A Case Study of Multimodal, Multi-Institutional Data Management for the Combinatorial Materials Science Community
Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated...
Papers By Citrine
Learn More
chimneys in chemical plant, corrosion resistance
A new open database of corrosion-resistant alloys
Using machine learning to create a new open database of corrosion-resistant alloys.
External Research
Learn More
Schemcatic of sequential learning for electrochemical catalysts
Accelerating electrochemical material discovery for sustainable chemical manufacturing
New electrochemical reactions are needed to reduce the carbon footprint of chemical production. Use AI to find new catalysts.
External Research
Learn More
Use of pycalphad to calculate alloy microstructural features for use in machine learning.
Addressing challenges in high-temperature alloy design
High-temperature alloy design is challenging, but crucial to aeronautical applications. Citrine is working on a project to improve the representation of alloys data and develop techniques to to accurately predict properties outside of the range of input data.
External Research
Learn More
AI-Guided Closed-loop Synthesis of Nanoparticles for Catalysis
AI-Guided Closed-loop Synthesis of Nanoparticles for Catalysis
Learn how Citrine worked with SLAC National Accelerator laboratory to optimize synthesis parameters for nanoparticles using an AI-guided closed-loop system in just 12 hours.
Case Studies, Metals and Glass
Learn More
MPEA Mechanical property data
Aggregating data to accelerate alloy design
Extracting, cleaning and analyzing and visualizing MPEA data.
External Research
Learn More
AI-Guided Design and Optimization of Graphite-Based Anodes via Iterative Experimental Feedback
This study presents an iterative AI-guided workflow that accelerates graphite-based anode development by improving both formulation feasibility and process robustness. Sequential learning via AI/ML-guided multiobjective inverse design for anode optimization was implemented using the Citrine Platform. Starting from a noisy, incomplete dataset, the Citrine Platform was used to generate...
Papers By Citrine
Learn More
Rapid Qualification of Additive Manufactured Parts
Rapid Qualification of Additive Manufactured Parts
AI to automatically pre-screen out parts that would fail pore-size qualification.
Case Studies, Metals and Glass
Learn More
Learnings in the Qualification of ABD®-900AM for Turbine, Aerospace, and Energy Applications
ABD®-900AM is a highly printable, gamma prime strengthened superalloy purposely designed for powder bed fusion (PBF) additive manufacturing (AM) with broad applicability to high-temperature gas turbine, aerospace, defense, and energy applications. To accelerate the adoption of the alloy, a collaborative team led by EPRI has initiated a first-of-a-kind project...
Papers By Citrine
Learn More
AutoMat: Automated Materials Discovery for Electrochemical Systems
Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and...
Papers By Citrine
Learn More
Design of Cheaper, Safer Nuclear Waste
Design of Cheaper, Safer Nuclear Waste
Multi-objective optimization for compact, durable nuclear waste forms.
Case Studies, Metals and Glass
Learn More
Closed-loop fully-automated frameworks for accelerating materials discovery
How much can a combination of sequential learning and closed-loop automation accelerate materials discovery?
External Research, Notes on Successful Projects
Learn More
Citrine Informatics: Chemical & Materials Development Platform
Today the Citrine Platform regularly powers data-driven materials discovery across industries, having moved beyond one-off demonstrations into routine industrial practice.
Papers By Citrine
Learn More
Chart showing AI guided discovery of organic semi conductors
Does machine learning work in materials?
Summary of peer reviewed, papers showing experimentally verified predictions of material properties.
External Research
Learn More
High-Dimensional Materials and Process Optimization using Data-driven Experimental Design with Well-Calibrated Uncertainty Estimates
The optimization of composition and processing to obtain materials that exhibit desirable characteristics has historically relied on a combination of scientist intuition, trial and error, and luck. We propose a methodology that can accelerate this process by fitting data-driven models to experimental data as it is collected to suggest...
Papers By Citrine
Learn More
Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior
In general, data-driven approaches aim to identify objectively (relying largely on the available data) the embedded correlations among selected inputs and outputs needed to study or model a given phenomenon. Data-driven approaches are particularly advantageous when a full, physics-based understanding of relevant phenomena is lacking, and also when gathering...
Papers By Citrine
Learn More
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...
Papers By Citrine
Learn More
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...
Papers By Citrine
Learn More
Sustainable chemistry
Materials Informatics and Sustainability – the case for urgency
Materials innovation is essential to sustainability. Materials Informatics can accelerate that. To speed up adoption of MI we need both education and public research to produce FAIR data.
What We've Learned
Learn More
Join Citrine, NIST, Cal Poly, Dow and MIT for a panel discussion about the use of AI for polymers.
Panel Discussion: AI-Driven Polymer Development – Challenges and New Horizons
Join Citrine, NIST, Cal Poly, Dow and MIT for a panel discussion about the use of AI for polymers.
Webinars, Other, All Webinars
Learn More
Quantifying the Performance of Machine Learning Models in Materials Discovery
Our findings illustrate the importance of designing a modeling and discovery workflow which is highly tailored toward the specific problem of interest. In ML and SL for materials discovery, one size does not fit all, traditional error metrics may not be a good predictor of success, and the specifics...
External Research, Notes on Successful Projects
Learn More
Quantifying Uncertainty in High-Throughput Density Functional Theory: A Comparison of AFLOW, Materials Project, and OQMD
A central challenge in high-throughput density functional theory (HT-DFT) calculations is selecting a combination of input parameters and postprocessing techniques that can be used across all materials classes, while also managing accuracy-cost tradeoffs. To investigate the effects of these parameter choices, we consolidate three large HT-DFT databases: Automatic-FLOW (AFLOW),...
Papers By Citrine
Learn More
Towards Informatics-Driven Design of Nuclear Waste Forms
Towards Informatics-Driven Design of Nuclear Waste Forms
Informatics-driven approaches, such as machine learning and sequential experimental design, have shown the potential to drastically impact next-generation materials discovery and design. In this perspective, we present a few guiding principles for applying informatics-based methods towards the design of novel nuclear waste forms. We advocate for adopting a system...
Papers By Citrine
Learn More