AI4MAT-ICLR-2026: ICLR 2026 Workshop on AI for Accelerated Materials Design

Santiago Miret, Defne Circi, N M Anoop Krishnan, Emily Jin, Seyed Mohamad Moosavi, Stefano Martiniani

Keywords: materials science, multi-modal data, representation learning, reinforcement learning

TL;DR: AI4Mat-ICLR-2025 explores automating materials discovery via: 1. AI-Guided Design; 2. Automated Chemical Synthesis; 3. Automated Material Characterization inviting diverse of leading-edge research at the intersection of AI and materials science.

Abstract: AI4Mat-ICLR-2026 explores the automated discovery of advanced materials through three interconnected pillars: 1. AI-Guided Design; 2. Automated Synthesis; 3. Automated Characterization. By bringing together leading researchers at the intersection of machine learning and materials science, the workshop fosters discussion of cutting-edge advances while building a cohesive, multidisciplinary community tackling some of the field's most pressing challenges. To that end AI4Mat-ICLR-2026's program highlights two leading topics to foster scientific dialogue in relevant subject areas, each featuring carefully curated invited speakers: 1. Reinforcement Learning & Beyond: The Role of Feedback in AI for Materials Science; 2. Cross-Modal, Unified Materials Representations – From Structure to Properties to Performance. In addition to invited talks and technical discussions, AI4Mat-ICLR-2026 continues its commitment to community development through established initiatives, including a Tiny Papers track for early-stage work, travel grants to support broad and inclusive researcher participation, and a dedicated journal venue for high-quality submissions.

Submission Number: 85