Mat3ra Featured in PNNL Autonomous Science Series

On February 24, 2026, Mat3ra was featured on the Pacific Northwest National Laboratory (PNNL) portal as part of the Autonomous Science Lecture Series, where Timur Bazhirov, CEO of Mat3ra, delivered a presentation titled “Connecting Theory and Experiment with Mat3ra.com.” The talk was published on the PNNL event page together with the webinar recording and speaker information.

In the presentation, Timur introduced Mat3ra.com as a data-centric materials R&D platform that combines physics-based methods such as density functional theory and molecular dynamics with machine-learning techniques and experimental data streams through FAIR, JSON-based standards and repeatable workflows. The talk also highlighted use cases in heterogeneous interfaces and semiconductor-relevant materials, as well as a collaborative theory–experiment case study with Prof. Sergei Kalinin’s group at the University of Tennessee, Knoxville.

The webinar also emphasized Mat3ra’s open-source data convention and related tooling for workflow and model categorization, as well as interoperability with broader U.S. Department of Energy data infrastructure efforts. The publication of this presentation on the PNNL portal marks another important opportunity for Mat3ra to share its approach with the broader scientific community working at the intersection of AI, autonomous science, and advanced materials research.

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In the recorded presentation, Timur Bazhirov discusses the methodological basis for connecting experimental observations with atomistic and simulation-ready models in a consistent digital environment. Particular attention is given to the role of structured data representations in enabling reproducible transitions from measured materials behavior to computational analysis.

The webinar further examines how such an approach can be applied to scientifically relevant systems, including heterogeneous interfaces and semiconductor-related materials, where realistic structural complexity must be taken into account. The presentation emphasizes the importance of preserving chemical, structural, and contextual information in forms that remain usable across different stages of modeling and interpretation.

A broader scientific theme of the talk is the development of interoperable research infrastructure for materials discovery. By combining standardized data conventions, workflow orchestration, and scalable computational methods, the presentation illustrates how digital platforms can support more rigorous integration of theory and experiment in advanced materials science.

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