DMREF Specific Highlights

An AI-driven, Cloud-based, Materials Discovery Platform for Nanomaterial Structure: PDFitc

5/28/2024 | S. Billinge, Q. Du, D. Hsu (Columbia U.)

Discovery of novel functional materials relies on a quantitative understanding of nanostructure. There is a need for advanced artificial intelligence (AI) and machine learning (ML) approaches to nanostructure determination

Data Flow Between Experiment, Continuum Models, and Atomistic Models

5/22/2024 | Gregory S. Rohrer, Carnegie Mellon University

A key challenge of learning from the outcomes of experiments and simulations is consistency of data formats and analysis techniques.  We have developed the ability to seamlessly integrate findings from atomistic simulations, mesoscale simulations, and experiments. 

Advancing the State of the Art In Piezoelectric Materials through Industry Partnership

5/14/2024 | David Martin (Broadcom)

As a GOALI project, the relationship between the DMREF team led by Geoff Brennecka and industry partner, Broadcom, is key to the success of the project. Broadcom is a global leader in Thin-film Bulk Acoustic Resonator (FBAR) products and a major supplier of radio frequency (RF) filters to leading smartphone manufacturers.

Review: Emerging Halide Superionic Conductors for All-Solid-State Batteries: Design, Synthesis, and Application

5/10/2024 | Yifei Mo (University of Maryland)

Recently, halide superionic conductors have emerged as promising solid electrolyte (SE) materials for all-solid-state batteries (ASSBs), owing to their inherent properties combining high Li+ conductivity, good chemical and electrochemical oxidation stabilities, and mechanical deformability, compared to sulfide or oxide SEs. In this Review, recent advances in halide Li+- and Na+-conducting SEs are comprehensively summarized.

Reorganization Energy Predictions with Graph Neural Networks

4/22/2024 | Daniel Tabor

These results demonstrate the feasibility of reorganization energy predictions on the benchmark QM9 data set without needing DFT-optimized geometries and demonstrate the types of features needed for robust models that work on diverse chemical spaces.

Curated Materials Data of Hybrid Perovskites

10/1/2023 | R. Chakraborty and V. Blum

Hybrid perovskites have emerged as a group of semiconductors that can solve key problems involving efficiency and production in optoelectronics and spintronics research. Over the past decade, this field has evolved to a point where the literature contains an enormous volume of chemical and physical information. The dispersed nature of the large, rapidly growing body of hybrid perovskite materials data poses a barrier to systematic discovery efforts, which can be solved by materials property databases, either by high-throughput or by systematic, accurate human-curated efforts.

Materials Simulation Toolkit

3/10/2023 | Dane Morgan (University of Wisconsin)

VTAnDeM: A Python Toolkit for Simultaneously Visualizing Phase Stability, Defect Energetics, and Carrier Concentration

1/1/2023 | M. Y. Toriyama, J. X. Qu, L. C. Gomes, and E. Ertekin

VTAnDeM offers a graphical interface that allows the user to interact directly with the chemical phase space of a given material and to visualize the defect formation energetics and ensuing carrier concentrations.

Polymer Foams for Oil Recovery


This unique combination of solvent selectivity and electro-mechanical response opens a path for the design of foam-like materials that could find applications in oil recovery, mechano-chemical sensors, flexible electronics, and energy storage.

Data-driven Shape Memory Alloy Discovery using Artificial Intelligence Materials Selection (AIMS) Framework

4/1/2022 | W. Trehern, R. Ortiz-Ayala, K. C. Atli, R. Arroyave, I. Karaman

Previous studies have focused on minimizing hysteresis under no stress, but not under applied stress.

Designing Materials to Revolutionize and Engineer our Future (DMREF)