About the Artificial Intelligence, Machine Learning and Materials Informatics Session
This session explores the use of artificial intelligence, machine learning and data science in materials research and engineering. It covers materials informatics, predictive modelling, property estimation, high-throughput screening, automated experimentation, digital twins, database development and intelligent manufacturing.
Abstract Scope and Participation
Materials scientists, data scientists, engineers and interdisciplinary teams are invited to present their work. Abstracts may address algorithm development, dataset quality, model validation, explainable AI, integration with experiments or simulations and data-driven approaches that accelerate materials discovery, process optimization and industrial decision-making.
Topics Covered in Artificial Intelligence, Machine Learning and Materials Informatics
Topic 01
Machine Learning for Materials Discovery
Topic 02
Materials Informatics and Data-Driven Research
Topic 03
Artificial Intelligence in Materials Design
Topic 04
Predictive Modelling of Material Properties
Topic 05
Automated Experimentation and High-Throughput Screening
Topic 06
Digital Twins, Databases and Intelligent Manufacturing
Featured Speakers — Artificial Intelligence, Machine Learning and Materials Informatics 2027
Recent Research Presented at Artificial Intelligence, Machine Learning and Materials Informatics Sessions
Interpretable AI-enabled electrochemical biosensors: Dendritic Fe₂O₃/graphene nanocomposites for next-generation clinical diagnostics
The synergetic combination of advanced nanomaterials and artificial intelligence (AI) is revolutionising the electrochemical biosensing field to achieve highly sensitive, selective and data-driven…
Calculation and data-driven strategies for accelerated MOFs design and synthesis
Metal-organic framework (MOF) is a new class of nanoporous material that is widely used in catalytic field due to their large specific surface area, high porosity and tunable pore size. Its excellent…
Machine learning assisted prediction of solid solubility limits in binary metallic alloys through Hume-Rothery approach
The solid solubility of metallic alloys is a critical factor in designing the materials with tailored properties. Hume-Rothery rules qualitatively predict the solid solubility limits (SSL) in the…
The role of machine learning in organic solar cell research
Machine learning (ML) and artificial intelligence (AI) methods are emerging as promising technologies for enhancing the performance of low-cost photovoltaic (PV) cells in miniaturized electronic…
The merging of artificial Intelligence and biomaterials: The future of personalized health solutions
Artificial intelligence (AI) has revolutionized many fields by identifying patterns and optimizing processes. Despite the use of AI across numerous industries, its use in biomaterials is relatively…
Related Scientific Sessions at Materials 2027
Why Attend the Artificial Intelligence, Machine Learning and Materials Informatics Conference 2027
Earn CPD Credit
Sessions are CPD-accredited; certificates issued to every registered delegate within two weeks of the conference.
Present Your Research
Oral and poster slots for original Artificial Intelligence, Machine Learning and Materials Informatics work, reviewed by the scientific committee.
Network Globally
Meet materials researchers, engineers, and academics from around the world across three days.
Publish & Get Indexed
Accepted abstracts appear in the indexed conference proceedings with a citable DOI.
Learn from Keynotes
Plenary lectures from leading voices shaping Artificial Intelligence, Machine Learning and Materials Informatics research and practice.
Hybrid Flexibility
Attend in person in Singapore or join virtually — same programme, same certificate.
Join Materials 2027 for the Artificial Intelligence, Machine Learning and Materials Informatics track
Oral and poster slots for your work — in person in Singapore or online. Not presenting? Attend as a delegate to learn from the field.
Artificial Intelligence, Machine Learning and Materials Informatics Conference 2027 — FAQs
What is the Artificial Intelligence, Machine Learning and Materials Informatics track at Materials 2027?
The Artificial Intelligence, Machine Learning and Materials Informatics track is a dedicated stream within Materials 2027 covering the latest research, methods, and applications in Artificial Intelligence, Machine Learning and Materials Informatics. It brings together researchers, engineers, and academics for oral presentations, posters, and discussion.
Who should attend the Artificial Intelligence, Machine Learning and Materials Informatics sessions?
Materials researchers, engineers, educators, doctoral students, and R&D leaders with an interest in Artificial Intelligence, Machine Learning and Materials Informatics are all welcome — whether presenting or attending.
Can I present my research in the Artificial Intelligence, Machine Learning and Materials Informatics track?
Yes. Submit an abstract for oral or poster presentation. All submissions are peer-reviewed by the scientific committee; accepted abstracts appear in the indexed proceedings.
Is virtual attendance available for Artificial Intelligence, Machine Learning and Materials Informatics sessions?
Yes. Materials 2027 is a hybrid conference — attend the Artificial Intelligence, Machine Learning and Materials Informatics track in person in Singapore or join live online, with on-demand access afterward.
Join the Materials 2027 delegation
Singapore · March 18–20, 2027· Hybrid · in-person & virtual
Register as Delegate →
Attend without presenting.