The enormous leap in computational approaches combined with advances in experimental techniques over the past three decades, demonstrates vividly that this synergy can play a key role in solving challenging problems in Materials Science and Engineering. Quantum Mechanical based calculations complemented by Machine Learning (QM/ML) are ideal to guide experiments in focussed discovery of novel materials with technological potential. The objective of the international conference is to provide a forum for recent advances in experiment, theory, and data science to discover new materials and to understand their fundamental properties. The current status of the field and its future plans and prospects will be highlighted, covering all the recent developments in First-Principles calculations, experiment, materials database and Machine Learning. The objective of this meeting is to promote collaboration not only among theorists but also with experimentalists through creation, sharing and mining of Data.

Sub-themes:

• Machine Learning for materials
• High-throughput Computation
• Oxygen Evolution Reaction, Oxygen Reduction Reaction, Hydrogen Evolution Reaction
• CO₂ conversion to value-added products, CO Oxidation
• Nitrogen reduction
• Storage materials – Batteries
• Materials for Solar Cell Applications
• HEA (high entropy alloys)
• Materials for solid state hydrogen storage
• Computation aided discovery of functional oxides
• Polar Magnetic Materials
• Thermoelectric Materials