Joshua Schrier

Joshua Schrier is the Kim B. and Stephen E. Bepler Chair and Professor of Chemistry at Fordham University. His research focuses on Physical Chemistry, Computational Chemistry, and Machine Learning for chemical experiments. He leads a lab in JMH 620 and maintains an office in JMH 616. His work emphasizes autonomous experimentation, AI-driven materials discovery, and open-source laboratory automation. Schrier’s research bridges theoretical and experimental approaches, leveraging machine learning to accelerate discovery in areas like perovskite nanocrystals, rare-earth separation, and self-assembling vesicles.

His research interests include developing self-driving labs to reduce experimental bias, optimizing synthesis workflows with active learning, and enhancing reproducibility through data science. Key projects include the 'Dark Reactions' initiative, which uses failed experiments to train predictive models, and the 'Frugal Twin' concept for low-cost automation. Schrier collaborates on the Materials Acceleration Platform (MAP) for energy materials discovery.

Publications highlight themes like machine learning for crystal polymorph prediction, AI-guided synthesis protocols, and automated pH estimation. While no scientific awards are listed, his contributions to lab automation and data-driven methodologies have advanced experimental chemistry practices. Advising and grants are not detailed here, but his lab engages in collaborative, interdisciplinary research with undergraduate and graduate students.

His lab’s infrastructure includes custom-built robots like Sidekick (a 3D-printed liquid handler) and software frameworks like ESCALATE for experiment management. Current work explores AI’s role in hypothesis generation, sustainable materials development, and democratizing access to advanced experimental tools.