Welcome to ASID 2026
We are pleased to announce the 2026 International Symposium on AI for Science and Interdisciplinary Discovery (ASID 2026), which will be held in Los Angeles, USA, from February 25 to 26, 2026.
ASID 2026 aims to bring together researchers, scientists, and practitioners from artificial intelligence, natural sciences, engineering, and interdisciplinary research domains to explore how AI technologies are transforming scientific discovery. The symposium provides an international platform for exchanging ideas on AI-driven scientific methodologies, data-intensive research, and cross-disciplinary collaboration.
Topics of interest include, but are not limited to, machine learning for scientific modeling, AI-assisted discovery in physics, chemistry, biology, and materials science, data-driven simulation and optimization, scientific knowledge representation, and autonomous experimentation systems. The symposium also welcomes research on interdisciplinary AI frameworks, large-scale scientific data analysis, and the integration of AI with high-performance computing and experimental sciences.
All submissions will undergo a rapid peer review process (2–3 working days). Accepted papers will be indexed by Google Scholar, CrossRef, and Scilit. We cordially invite researchers and professionals worldwide to contribute their latest findings and participate in this forward-looking symposium in the vibrant city of Los Angeles.
TIME FOR SUBMISSION
CALL FOR PAPER
Topics of interest include but are not limited to
1. Foundations and Architectures of Artificial Intelligence
- Adaptive learning models and self-organizing neural architectures
- Symbolic reasoning and hybrid intelligence frameworks
- Explainable and interpretable machine learning systems
- Human-centered and cognitive-inspired AI design
2. AI for Scientific Discovery and Modeling
- Machine learning for scientific modeling and hypothesis generation
- AI-assisted discovery in physics, chemistry, biology, and materials science
- Data-driven simulation, surrogate modeling, and uncertainty quantification
- Integration of AI with experimental and theoretical scientific workflows
3. Interdisciplinary AI and Data-Driven Research
- AI-enabled interdisciplinary research methodologies
- Large-scale scientific data analysis and knowledge extraction
- Multimodal data fusion across scientific domains
- Cross-domain representation learning and transfer learning
4. Autonomous Systems and Intelligent Scientific Instruments
- Autonomous experimentation and self-driving laboratories
- Intelligent sensing, measurement, and control systems
- Robotics and automation for scientific research
- Closed-loop optimization in experimental and computational science
5. High-Performance Computing and AI Integration
- AI acceleration for high-performance and exascale computing
- Hybrid AI–HPC architectures and workflow optimization
- Distributed, cloud, and edge computing for scientific applications
- Scalable AI algorithms for large-scale scientific problems
6. Trustworthy, Ethical, and Responsible AI for Science
- Reliability, robustness, and validation of scientific AI models
- Explainability and transparency in AI-driven scientific results
- Ethical considerations in AI-enabled scientific research
- Reproducibility, data governance, and responsible AI practices
PUBLICATION & INDEXING
Pinnacle Academic Proceedings Series
eISSN: 3079-8760
pISSN: 3079-8752
Indexing: Google Scholar, Dimensions, Crossref, Scilit, Semantic Scholar, Yubetsu Shibata, ResearchGate
Note: Selected titles will be submitted for evaluation in CPCI (part of Clarivate’s Web of Science), and where applicable, they are also submitted to Ei Compendex and Scopus(Subject to acceptance).
