Oral Presentation at the 2026 AIChE Annual Meeting

Matlantis Corporation will give an oral presentation at the 2026 AIChE Annual Meeting, held November 8 to November 12, 2026 (Central Standard Time/CST), at the Minneapolis Convention Center in Minneapolis, USA. Please see below for details.

Presentation Details (Oral Presentation)

Room: Room 103C (Level 1, Minneapolis Convention Center)
Paper number:  566b
Presenter:  Taku Watanabe
Authors: Qingjie Li, Joshua Young, Taku Watanabe (Matlantis Inc.)
Session: 566 Accelerated Discovery of Inorganic Materials: High-Throughput Experiments, Modeling, & Data Science
Date and time:  Wednesday, November 11, 2026, 12:55 PM to 1:09 PM (Central Standard Time/CST)
(Japan Standard Time: Thursday, November 12, 2026, 3:55 AM to 4:09 AM)
Division:  Materials Engineering and Sciences Division (08D Inorganic Materials)
Session type: Oral
Title:  Accelerating Solid Electrolyte Discovery with an Interpretable, Multimodal, and Probabilistic Machine Learning Framework
Overview:
 The discovery of solid electrolytes for all-solid-state batteries is currently constrained by two major modeling challenges. First, ionic conductivity depends on a complex interplay of structural and dynamic factors that conventional, single-modality machine learning models fail to fully capture. Second, models trained on computationally efficient, short-timescale molecular dynamics simulations suffer from severe data noise. Specifically, transient phenomena such as local ion “cage rattling” artificially inflate mobility metrics, causing models to overfit to subdiffusive artifacts rather than true macroscopic diffusion.
To bridge this simulation-to-reality gap, we introduce MuLiCon, a multimodal, probabilistic deep learning framework. MuLiCon integrates 1D structural data (X-ray diffraction), vibrational dynamics (phonon density of states), and pre-trained chemical embeddings (Matlantis PFP) into a dual-task architecture for simultaneous material classification and conductivity regression. By employing deep ensembles for Uncertainty Quantification (UQ), MuLiCon acts as a probabilistic filter, explicitly isolating and rejecting simulation noise.
Ablation studies reveal a distinct physical learning hierarchy: PFP descriptors serve as the primary chemical foundation, while XRD and PDOS offer structural gating and vibrational refinement. By filtering for high-confidence predictions, MuLiCon achieves precise conductivity regression on true conductors. Furthermore, model interpretability analyses confirm that the network autonomously identifies underlying physical mechanisms, including phonon resonance and structural bottlenecks. Ultimately, MuLiCon provides a robust, physics-aware pathway to accelerate next-generation energy storage design.

Presenter Profile

Taku Watanabe

Matlantis Inc.

Principal Researcher / Head of Global Customer Success

After obtaining his Ph.D. from the University of Florida, Taku worked as a postdoctoral researcher at Georgia Institute of Technology, where he engaged in nanoporous material (zeolite, MOF) research. After returning to Japan, he led the development of materials for next-generation batteries at Samsung R&D Institute Japan and then later joined ENEOS to pursue research in energy materials. Leveraging his diverse background in simulation and experimental work, he is now focused on customer success initiatives that employ cutting-edge materials research techniques, specifically through the use of machine learning and computational chemistry. In addition to developing computational technology for inorganic materials in general, he also oversees the organization and operation of overseas customer support.