DataLab: How to Prepare Chemicals and Materials Data and Teams for AI
Join Dr. Lenore Kubie and Dr. Robert Kieschke for a webinar discussing how teams can efficiently prepare their data as part of digital transformation initiatives.
Accelerating Polyolefin-Based Adhesives Development with AI
To meet ever-shifting market, customer, and supply demands, leaders in the formulated adhesives industry have adopted artificial intelligence (AI) to accelerate new polyolefin development.
Join Citrine Informatics and Synthomer to...
Accelerating Consumer Product Development with AI
As consumer product innovation organizations continue to navigate supply chain disruption, ever-increasing sustainability objectives, and global economic uncertainty, today’s consumer goods companies must confront these challenges while seeking growth...
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...
Autonomous electrocatalyst discovery for sustainable nitrogen reduction
Sequential learning and autonomous atomistic simulations help discover new catalysts with co-optimized properties.
Voltaiq + Citrine: Accelerating the Battery Sector with Artificial Intelligence and Advanced Data Analysis
The battery industry must dramatically accelerate both technology development and production in order to meet rising global demand in the transportation and grid energy storage sectors.
Removing PFAS From Formulations Using AI
Learn how chemicals industry leaders are prioritizing exclusion of per- and polyfluoroalkyl substances from their product development, and leveraging the capabilities of artificial intelligence to quickly react to regulatory...
How AI is being used to drive innovation, sustainability, and product development for the paints and coatings industry
Companies in our industry are facing increasing challenges to simultaneously meet sustainability and performance targets, overcome supply chain complications, and manage price increases on raw materials. Leaders in our...
Multivariate prediction intervals for bagged models
Accurate uncertainty estimates can significantly improve the performance of iterative design of experiments, as in sequential and reinforcement learning. For many such problems in engineering and the physical sciences,...
Quantifying the performance of machine learning models in materials discovery
The predictive capabilities of machine learning (ML) models used in materials discovery are typically measured using simple statistics such as the root-mean-square error (RMSE) or the coefficient of determination...