AppliedAI 4 Materials

Services

Material Design

Leverage AI to design materials with tailored properties. The AppliedAI4All approach includes:

• Property Prediction
• Inverse Design
• Structure Optimization
• Generative AI for novel material generation

Specialization is in overcoming scarce data challenges with advanced techniques like transfer learning, active learning, and physics-informed AI.

Material Discovery

Accelerate the discovery of novel materials with the AI-powered platform developed under this initiative. Capabilities include:

• High-Throughput Screening
• Novel Material Suggestion
• Data Mining and Analysis

The methods efficiently combine AI with experimental data to navigate vast chemical spaces even with limited data.

Technology

AppliedAI4All utilizes cutting-edge AI techniques to address material science challenges:

• Machine Learning Models
  • Supervised & Unsupervised Learning
  • Deep Learning (CNNs, GNNs)
  • Scientific ML (SciML)
  • Transformer & LM-based Models
  • Gaussian Processes
• Scarce Data Techniques
  • Transfer Learning
  • Active Learning
  • Physics-Informed AI
  • Data Augmentation
  • Few-shot Learning
• Generative AI
  • Variational Autoencoders (VAEs)
  • Generative Adversarial Networks (GANs)
  • Diffusion Models
• Optimization
  • Local Optimization
  • Global Optimization
  • Bayesian Optimization
• Explainable AI (XAI)

  Priority is given to transparency and interpretability in the AI models developed.

Impact

This initiative has a strong track record of publishing impactful research in leading scientific journals, demonstrating the effectiveness of the AI-driven approaches to material science developed through AppliedAI4All.