AI4X-AC 2026 Posters

AI-driven computational workflows for scalable innovative materials design

Stephan Roche

Deep Learning of Accurate Force Field of Hf$_x$Si$_{1-x}$O$_2$

Yingtao Yang

LapidaryEngine: Feedback-Guided Iterative Text-to-Crystal Structure Generation

Yusei Ito

Identification of MOFs with Unique Pore Shapes Using Computer Vision

Saad Aldin Mohamed

aLLoyM: A Large Language Model for Alloy Phase Diagram Prediction

Yuna Oikawa

Unveiling Process–Structure–Property Coupling in CNT Fibers via Multimodal AI Analytics

Daisuke Kimura

Space-Group Identification from Multi-Phase Powder XRD via Latent Phase Modeling

Tomoya Murata

Machine learning analysis of literature dataset on the CO2 reduction toward prediction of high activity multi-elemental catalysts

Umi Shin

A Unified Diffusion Framework for the End-to-End Generation of Symmetry-Constrained Complex Molecular Crystals

Wendi Cai

Large Language Models in Materials Science: Assessing RAG Evaluation Frameworks through graphene synthesis

Zen Han Cho

Automation of the Polishing-Etching Process in Metallographic Observation

Daijiro Hashimoto

A shortcut towards simulating millions of atoms using machine learning potentials

Linh La

Multi-task Attention for Doped Thermoelectric Properties Prediction

Leng Ze Tang

Generative Symmetry in Crystal Modelling

Jianghai Wang

Crystalline phase projection and identification from high temperature diffractograms using graph neural networks

Léonard Imbert

An Accurate Deep Learning Force Field for Dielectric and Ferroelectric $Hf_{x}Zr_{1-x}O_2$ Solid Solutions

Zhe Su

A Comparative Study of Molecular Dynamics Approaches for Simulating Ionic Conductivity in Solid Lithium Electrolytes.

Dounia Shaaban Kabakibo

AI-Enabled Development of Cooling Device for High-Power Electronics

Vassilis Christophides

Mapping Materials Science: a multi-modal toolbox to curate broad synthesis procedure databases from scientific literature

Mathilde Franckel

AtomWorld: A Benchmark for Evaluating Spatial Reasoning in Large Language Models on Crystalline Materials

Alexander Chen

An Autoregressive Approach For Material Generation

Niklas Dobberstein

Recent developments and applications of the machine-learned moment tensor potentials

Nikita Rybin

A topological equilibrated configuration space analysis of nanoparticle systems

Alexandros Keros

LLM-Guided High-Throughput Discovery of Antifreeze Electrolytes Enabling −50 °C Stability and 99.5% Coulombic Efficiency

Jing Jiang

Accurate and rapid measurement of fluid thermal conductivity

Mohammad Zargartalebi

Generalized convolutional many-body distribution functional representations

Anatole Von Lilienfeld

Toward Self-Driven Microscopy Exploration for the Characterization of Functional Materials

Claudia Bazan

Machine Learning-Based Surrogate Models for Performance Prediction of Triboelectric Nanogenerators

Shashank Mishra

A Multi-Modal Deep Learning Platform for Cross-Domain Property Prediction in Chemical Process Design

Mikhail Tsitsvero

SWORD: A compositional disorder-aware crystal representation for cross-dataset curation and disordered novelty assessment

Yuayo Huang

Computational investigation and generation of site-disordered sodium ion cathode materials

Martin Hoffmann Petersen

EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion- Transformer Architecture

Yeonghun Kang

Application of digitization and large language models for digital twins in permanent magnet materials

Hongbin Zhang

MatSeek: An Automated Knowledge-Driven Framework for Materials Research

Huang Jianguo

Rapid, Room Temperature Synthesis of a Biomass-Derived, Recyclable Copper Metal-Organic Framework Utilizing Automation for CO2 Capture

Daniel Davidson

Using genetic algorithms to explore compositional space of perovskite thin film

Jérôme Gautier

Anomalyspy: A Generative Defect Localization in Semiconductor Packages, with X-Ray Microscopy

Pawan Kumar

Predictive Modeling for Quantitative Compositional Engineering of InZnHfO Thin-Film Transistors

Jiho Lee

Machine Learning-Based Optimization of SOC Windows for High-Performance μ-Si Anodes

Taehoon Choi

Quantum Annealing-Driven Active Learning Benchmark for Nanophotonic Design

Serang Jung

Model–Data Coevolution as the Basis of Stateful, Problem-Scoped Materials Databases

Fengyu Xie

Literature-Grounded LLMs for Predicting High Ionic Conductivity Solid-State Electrolytes

Kunik Jang

Expanding catalyst discovery across material classes by machine learning

Junseok Moon

Simulation-to-Experiment Transfer Learning for XRD-Based Autoencoder Modeling of SOEC Half-Cell Materials

Hunmin Park

TropicalField-Tandem: Physics-Informed Indoor$\to$Outdoor Translation and Uncertainty-Aware Forecasting for Perovskite–Si Tandem Reliability

Romika Sharma

Label-Efficient Battery SOH Estimation via Domain-Aware Self-Supervised Learning

Ji Young Yun

Accelerating Catalyst Design via AI: High-Throughput Screening and Machine Learning Reveal Defect-Enhanced Activity in Pt-Au Nanoclusters

Ilia Chepkasov

AI-Assisted Architected Cementitious Microstructures for Durable and Low-Carbon Infrastructure

Abhimanyu Goel

MOFology: Ontologically-Grounded MOF Knowledge Graph and Prediction Framework

Matthew Hart

3D Microstructure Reconstruction of X70 Pipeline Steel from 2D EBSD Data Using SliceGAN

Joonas Lahikainen

Multiscale modeling of resistive switching in two-dimensional heterostructures

Vitalii Kapitan

A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes

Junmin Chen

Motif-driven scalable design of rutile multi-cation rutile oxides for oxygen evolution reaction

Yiwen Yao

An Explainable Conversational AI Framework for the Discovery of Cobalt Electrocatalysts in Industrial Water Electrolysis

Jongyeop Baek

Data-driven discovery of novel materials for smart optoelectronics

Liudmila Klimova

Improving the univariate calibration approach with Bayesian modeling for IR reaction monitoring

Jiayu Zhang

Selective and Sustainable Oxidation of Allyl Alcohol in Water Using a TS-1 Catalyst in Continuous Flow: A Machine-Learning-Driven Approach

Riko I Made

Eliminating Cryogenic Constraints in Ipsenol Synthesis through Real-Time Reaction Landscape Mapping

Charlotte Smith

AI-Assisted Reaction Network and Catalyst Exploration for CO₂ Conversion

Mikhail Polynski

Impact of explicit long-range interactions on the accuracy of machine learning interatomic potentials in modeling molecular liquids

Olga Chalykh

BigSolDB 2.0, dataset of solubility values for organic compounds in different solvents at various temperatures

Marina Kiseleva

GoFlow: efficient transition state geometry prediction with flow matching and E(3)-equivariant neural networks

Leonard Galustian

iGRASP: A Modular Platform for Improved, Graph-Based Spectra Prediction

Nick Wierich

Future-Proofing Data for Catalyst Fingerprinting and Better Design Validation

Wesley McNutt

ChemSIE: From Document Based Records to Machine Actionable Experimental Data

Itamar Wallwater

DPA4: A Generalist Foundation Model for the Era of On-Demand Interatomic Potentials

Tiancheng Li

An automatically generated reaction network for the synthesis of hBN using the Molecular High-Index Saddle Dynamics Search method

Vitalii Sagan

Multi-fidelity Bayesian optimisation for DFT-in-the-loop discovery of manufacturable homogeneous catalysts, auxiliaries, and ligands.

Yong Lee

Beyond Greedy Acquisition: A Distribution-Aware Active Learning Framework for Molecular Discovery

Ruixuan Chen

A Flexible, Code-Free, and Scalable Workflow for Structured Data Extraction from CO2 Electrochemical Reduction Literature

Haiwen Dai

Smarter Electrolysis in Rock Reactors

Joseph Chiong

Automated Process Synthesis via Generative AI-based Dual-Loop Simulation and Optimization

Yeong Woo Son

Evaluating Chemistry-Guided Filtering Heuristics Using LLM-Extracted Reaction SMILES

Chaewon Lee

Predicting mechanisms and outcomes of complex radical cascades via a hybrid, network-QM-AI approach

Kangjie Lin

CryoEM 2D classification with translational and rotational invariant features

yong Mingda

Single-Shot Phase Retrieval in Cryo–EM via Latent Conditional Diffusion Model

xiaodong Yang

Simulation-to-Real Alignment for Cryo-EM Image Modeling via Conditional GAN-Based Refinement

Ziyue Jiang

Flow-Matching-Based Posterior Sampling for Single-Shot Phase Retrieval in Cryo-EM

Rongtao Zhang

Evolving Robust Drug Candidates via Co-Evolutionary Artificial Life Simulators

Junhan Wang

Recent Progress in AI-Driven 3D (Bio)Printing for Tissue Engineering

Mihaela-Raluca Dobrisan

BioPrint-LKM: An evidence-grounded large knowledge model for bioprinting knowledge retrieval and parameter initialization

Xi Huang

Spatial Machine-Learning Prediction of Lung Function and Body Mass Index in Australian Cystic Fibrosis Patients

Arul Earnest

FHIR-Driven Medical Network Traffic Simulation for Multi-Departmental Healthcare Security Testing

Miro Moffett

Post-PCI Cardiac Death Prediction via Synthetic Minority Augmentation and Stress-Tested Probability Quality

Mikhail Lazarev

A Connected Workflow to Streamline Tablet Manufacture and Dissolution Testing

Maria Chang

FedAdaPriv-CPU: Adaptive Differential Privacy with Frozen Backbone for Resource-Constrained Federated Medical Imaging

Anusha Randhawa / Muhammad Hamza Zaman

Toward TheraAgent: Evidence-Grounded, Self-Verifying AI for Oncology Treatment Recommendation

Junhan Wang

A Fairness Audit of Medical Imaging Foundation Models on a Multimodal Structured Clinical Benchmark

Saketh Lingisetty

ADMET Scoring Models for Real-World Drug Design

Zihan Wang

Translating Natural Language Processing Frameworks in Humanistic Data Analysis

Kenneth Y T Lim

Impact of Disruptive Technologies on Value Creation and Capture: Comparing OTT and LLMs providers

Maria Tsenzharik

Democratizing Microscopy Training through Digital Surrogates

Ali Shayesteh

ZKBioVault: A Privacy-Preserving Biometric Authentication Framework for Fraud-Resilient Financial Identity Management

Izza Sohail

Development of an automated system for ceramic powder process

Daijiro Hashimoto; Woosuck Shin

Dynamic Parameter Scanning: a Novel Method for Mechanistic Elucidation

Dylan Pyle

MaiML: A Common Data Format for Measurement and Analysis and Its Application to an Autonomous Experimental System

Akira Aiba

Robotic-arm-based self-driving laboratories powered by NIMO and IvoryOS

Naruki Yoshikawa

Affordable 3D Printed Automation for Self-Driving Laboratory: Expanding Access to High-Throughput Experimentation

Sayan Doloi

Autonomous Workflows and SHAP Interpretation of Deposition-Rates in Bipolar HiPIMS

Manuel Kober-Czerny

Automation of thermal ageing and tensile tests for adhesively bonded single lap joints

Kazumasa Shimamoto

On-the-fly reaction monitoring using correlated spectroscopies

Jan GüNZL

Electrochemical Feedback for Closed-Loop Facet Control in Pd Nanoparticles

Shao Rong Choo

ATLAS: accelerating materials discovery at Imperial College London

Lana Lee

Autonomous Search for Grinding Conditions using Robots and Bayesian Optimization

Yuto Yotsumoto

RAISE: A self-driving laboratory for interfacial property formulation discovery

Aaron Clasky

MicroMAP: A Material Acceleration Platform for the Synthesis of Organic Nanoparticle Dispersions

Alexander Colsmann

An automated gel screening platform

Aaron Clasky

Accelerating small organic molecules scale up with data-rich experimentation

Ekaterina Trushina

Challenging to formulation through autonomous experiments and consideration of digital twin framework application

Kesaaki Minemura

Towards Optimal Mixing: Enabling Autonomous Experimentation via Quantitative Emulsion Assessment

Hitoha Sato

Physical model-informed automated reaction optimization within a digital platform

Shuyuan Zhang

An Automated, Calibration-Free Platform for High-Throughput Reaction Screening and Data Analysis

Manh Hung Tran

DIY, Modular Automation Platform Built from Affordable Components for Directed Evolution and Molecular Biology, Toward Closed-Loop Workflows

Takanori Uzawa

Scalable Low-Cost Laboratory Automation: A Digital Twin-Integrated Robotic Platform for Autonomous Liquid Handling

Ali Shayesteh

Robotic Mechanochemical Synthesis of HKUST-1 with Process Monitoring

Kazuki Ishizaki

Bayesian optimization for self-driving liquid coating laboratory

Jisoo Song

A Provenance-First, AI-Ready Data Platform for Large-Scale Memristor Experiments

Lai Gan

Autonomous Discovery of Pareto-Optimal Gas-Diffusion Electrodes

Zahra Azimi Dijvejin

A low-cost, high-throughput solid dosing platform for self-driving laboratories

Rui Zhang

Robot-Enabled Biofabrication of Tissue Cultures with Micrometer-Scale Precision

Daniel Hocevar

Automated and Interpretable Experimental Platform for Process-Structure-Property Analysis via Raman Spectroscopy

Takuya Miyajima

Digitalizing Slurry Mixing for an Electrode Fabrication SDLs

Jaehyun Oh

Robotic Kneading for Organic Reactions: A Deterministic Platform for Data-Driven Synthesis in Semi-Solid Media

Yuki Nishihori

HELIOS - Holistic Experiment Learning Intelligent Orchestration System

Sissi Feng

Membrane Optimization for Electrochemical CO₂ Storage

Jordan Rumscheidt

Development of a Scalable High-Throughput Platform for Plasmid Transfection in 3D Mammalian Cell Culture

Rosanna Jiang

Reinforcement Learning to Increase Nuclear Fusion Efficiency

Joseph Koh

Accelerated Discovery and Characterization of Nanoscale-Covalent Organic Frameworks for Photocatalytic Water Splitting

Ziheng Xiao

Bayesian Optimization-Guided Experimental Design for Graphene-Reinforced Cementitious Composites

Abhimanyu Goel

LabDroid “Maholo”-Centered Automation and Autonomization of Life Science Experiments

Akari Kato

Audited Causal Discovery Agents for Brain Resilience and Alzheimer's Reversal

David Scott Lewis

PD-NCA AutoLab: Self-Organizing Neural Intelligence as a Testbed for Autonomous Science

David Scott Lewis

AI-Guided Optimization of EIS Measurements: Minimizing Low-Frequency Sampling for Data-Efficient Electrochemical Characterization

Yonatan Kurniawan

Transferable Impedance-Grounded Learning for Interfacial Degradation Across Energy Systems

Hithesh Rai Purushothama

Reinforcement Learning-Enabled Control of Single DNA Molecule Manipulation in Microfluidics

Fuhai Zhou

Accelerating Electrolyte Discovery via a Closed-Loop Physical AI Platform

Vanesa Munoz Perales

The Medicinal Chemistry Self-Driving Lab - Accelerating Hit Optimisation with a Direct to Biology Approach

Stuart Green

A Multi-Agent LLM Framework with Hierarchical Citation Graph for Automated Survey Generation

Duy Dung Le

Beyond Statelessness: Engineering Subjective Cognition via Hierarchical Memory Consolidation and Tunable Agency

Malik Saif

A Multi-agent Framework for Physical Laws Discovery

Bo Hu

MolViBench: Evaluating LLMs on Molecular Vibe Coding

Jiatong Li

Black-box Membership Inference Attacks on Synthetic Text via N-gram Overlap

Yidan Sun

Quality-Diversity LLM for Generative Design

Ariq Koh Boon Xiong

PERELMAN: Pipeline for scientific literature meta-analysis

Daniil Sherki

When Designs Explain Themselves: Report Cards for Evolutionary LLMs

Alex Siek

IFC-QA: A Benchmark for Evaluating LLM Reliability over Industrial Building Information Model

Anastasiia Volkova

The Geometry of Reasoning Failure: Predicting Agent Errors from Semantic Scale Trajectories

Andrey Ustyuzhanin

From Visual Vocabulary to Grammar: An Interpretable Paradigm for Scientific Discovery in Spatiotemporal Data

Zhen Yuan Yeo

Spectral Analysis of the Weighted Frobenius Objective

Vladislav Trifonov

Sparse Global Mixing via Compact-Support Graph Kernels

Himanshu Singh

A Modular and Interpretable Pipeline for Unsupervised Learning on Scientific Spatiotemporal Imaging Datasets

Timothee Levilly

Electronic Circuit Optimization With Graph and Sheaf Neural Networks

Eva Maria Hita Sogorb

Scaling Multilingual E-Commerce with GenAI: Content Generation and Sentiment Analysis for Low-Resource Languages

Kathakali Mitra / Harsh Khanna

Conditional Diffusion for Storage Performance Modeling

Aziz Temirkhanov

Chiropto-Neuromorphic Devices Based on a Photocatalytic Dye/Polymer Semiconductor Bulk Heterojunction for Circularly Polarized Light Detection and Memorization

Boesung Kwon

Enhancing Synaptic Plasticity and Multistate Retention of Organic Neuromorphic Devices Using Anion-Excessive Gel Electrolyte

Jeongwoo Lee

Identifiable learning of dissipative dynamics

Aiqing Zhu

Learning to Image in Slepian-Pollack Basis – Achieving Super-resolution of Nanostructures

Eng Aik Chan

Divergence-Constrained Physics-Informed Neural Networks for Time-Domain Maxwell's Equations

Zaifeng Yang

AI-Enhanced Zero Noise Extrapolation using Residual Neural Networks for Quantum Error Mitigation

Feras Shita

OnsagerFlow: Learning mesoscopic dynamics from microscopic simulation

Shiqi Wu

Multi-Modal Classification of Extended 1D Cellular Automata

Zhaoyun Chen

Data-Driven Detection of Nonlinear Mode Interactions Using Post-Hoc Interpretable Deep Learning

Bayan Abusalameh

Partial-Physics Pretraining for Transfer-Learning in Computational Aerodynamics

Benjamin Y. J. Wong

Application of Automatic Differentiation and Optimization with PDE Constraints to Forward and Inverse Problems

Alexander Ryabov

Toward a Global SAR Foundation Model for SAR-to-Spectral-Indices Mapping

Ziyi Song

Physics-informed local-global underground fluid flow modeling with multiple sinks

Alexander Ryabov

A Field-Deployable IoT-Edge-AI Framework for Landslide Prediction and Early Warning

Amrita Joshi

AI stretch-grid model for kilometre-scale weather prediction over deep tropics

Ka Wing Chui

AI in Life Cycle Assessment

Alexei Lapkin

Improving Value Chain Transparency in Bioprocess Life Cycle Assessment Using Surrogate Models

Michail Vousvoukis

Dyna-STTN: Distilling Physical Dynamics into Spatio-Temporal Transformer Network for Weather Forecasting

Jiafeiyang Dong

Data-driven uncertainty-aware forecasting of sea ice conditions in the gulf of Ob based on satellite radar imagery

Alexander Ryabov