Matlantis at the 2026 MRS Spring Meeting & Exhibit

We are pleased to announce that members of Matlantis, Inc. (U.S.) will deliver two oral presentations at the 2026 MRS Spring Meeting & Exhibit, held in Honolulu, Hawai'i from April 26 to May 1, 2026.
Organized by the Materials Research Society (MRS), the Spring Meeting is one of the world's foremost international gatherings in materials science. Spanning six days across four official venues in Honolulu, the Meeting brings together researchers from academia, industry, government, and national laboratories for cross-disciplinary collaboration and scientific exchange. This year's program highlights breakthroughs in areas such as sustainable manufacturing, advanced characterization, and energy materials.
Our team will present the latest research results leveraging Matlantis and its universal machine learning interatomic potential, PFP, for catalyst and battery materials discovery.

Presentation 1

Accelerating Discovery of High Entropy Alloy Catalysts for Nitrate Reduction to Ammonia with a Universal Machine Learning Interatomic Potential

Ammonia is critical as a fertilizer feedstock and an emerging hydrogen carrier, but the energy-intensive, carbon-heavy Haber-Bosch process has driven interest in alternatives such as electrocatalytic nitrate reduction (NO₃RR), which also helps mitigate nitrate pollution in water. High-entropy alloys (HEAs) are attractive catalyst candidates due to their diverse active-site environments, yet their vast compositional space is intractable for conventional DFT-based screening. In this work, we use a universal machine learning interatomic potential to rapidly map Gibbs free energies along the NO₃RR pathway across thousands of HEA surfaces (e.g., FeCoNiCuZn), and then leverage graph-neural-network latent embeddings as descriptors to train ML models that predict nitrate and ammonia binding energies and identify compositions with optimal reactant–product binding balance—an approach readily extensible to other challenging electrocatalytic reactions such as CO₂ reduction.

Date & Time (local): Sunday, April 26, 2026, 8:30 AM
Session: EN02.01 — ML- and AI-Enabled Design of Sustainable Materials
Symposium: EN02 — Emergent Materials Paradigms—Catalytic, Photonic and Energy Conversion Technologies for a Sustainable Future
Venue: Sheraton Waikiki, Lanai Room, 2nd Floor
Presenter: Joshua Young (Matlantis, Inc.)

Presentation 2

Accelerating Discovery of High Entropy Alloy Catalysts for Nitrate Reduction to Ammonia with a Universal Machine Learning Interatomic Potential

Discovering solid electrolytes for all-solid-state batteries is bottlenecked by the fact that high ionic conductivity arises from an intricate coupling of structural and vibrational properties, which single-modality machine learning models struggle to capture. To address this, we introduce a multimodal deep learning framework that integrates several physically motivated representations—long-range crystal structure, local atomic environments, and lattice dynamics—within a single architecture, learning cross-modal correlations to build a richer, more physically grounded material description. The approach outperforms single-modality baselines while offering interpretable insights into the factors governing ionic mobility, providing a practical route to accelerate solid electrolyte screening and uncover the structure–dynamics–property relationships that guide rational design of next-generation energy storage materials.

Date & Time (local): Tuesday, April 28, 2026, 8:45 AM
Session: MT01.03 — Physics-Informed, Expert Guided AI-Driven Experiments
Symposium: MT01 — Interfacing AI with Prior Knowledge and Human Expertise for Data-Efficient Autonomous Materials Research
Venue: Hilton Hawaiian Village, Tapa Conference Center, Iolani Suite VI, 2nd Floor
Presenter: Qingjie Li (Matlantis, Inc.)

Event details and registration
For event details and registration, please check the link below.
https://www.mrs.org/meetings-events/annual-meetings/2026-mrs-spring-meeting-exhibit