Computational Materials Science
Predicting Electronic Structures: Revolutionizing High Entropy Alloy Design
Our cutting-edge machine learning framework accurately predicts electron density across the composition space of concentrated alloys, dramatically accelerating materials discovery and design by minimizing the need for extensive traditional simulations.
Quantifiable Impact on Materials Discovery
Leveraging Bayesian Active Learning and novel descriptors, our approach delivers significant efficiency gains and predictive power for complex alloy systems.
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Training Data Reduction (Ternary Alloys)
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Training Data Reduction (Quaternary Alloys)
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Average Error in Electron Density
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Unlocking Material Properties with ML
Our research presents a robust machine learning framework capable of predicting the electron density field, a fundamental unknown in materials science, for medium and high entropy alloys. This breakthrough enables accelerated exploration of material properties and greatly reduces the computational burden associated with traditional DFT calculations. We demonstrate high accuracy and generalizability across diverse composition spaces.
Innovative Approach
The core of our methodology involves three key strategies: Bayesian Active Learning (AL) to minimize training data, novel body-attached-frame descriptors that scale efficiently with alloy complexity, and training an δρ ML model (difference between total and atomic densities) for higher resolution chemical bonding description. These innovations collectively achieve state-of-the-art predictive performance.
Broad Applicability
Our ML models exhibit strong generalization capabilities, extending beyond the training data to unseen compositions, systems with localized defects (mono-vacancies, di-vacancies), and handcrafted systems featuring species segregation. The errors in energy predictions consistently remain within chemical accuracy, highlighting the model's reliability for diverse material configurations.
Streamlined Data Generation with Bayesian Active Learning
Generate Initial Training Data
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Train Bayesian Neural Network
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Optimize Acquisition Function (UQ)
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Suggest Next Input to Label
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Perform Ab Initio Calculations
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Augment Training Data
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Case Study: CrFeCoNi and the δρ ML Advantage
For complex systems like quaternary CrFeCoNi, training on the difference between total and atomic densities (δρ) significantly enhanced prediction accuracy. This approach led to a two-fold reduction in maximum energy error compared to traditional ρ-based models, especially critical for elements with semi-core states. This refinement ensures higher resolution description of chemical bonding, crucial for accurate property prediction in HEAs.
Remarkable
Generalization Across Unseen Configurations & Defects
Calculate Your Potential ROI
See how much time and resources you could reclaim by integrating AI-driven electronic structure prediction into your materials research pipeline.
Projected Annual Savings
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Researcher Hours Reclaimed Annually
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Your AI Implementation Roadmap
A phased approach to integrating advanced AI into your materials R&D, ensuring seamless adoption and maximum impact.
Phase 1: Discovery & Assessment
Comprehensive analysis of your current R&D workflows, data infrastructure, and specific materials challenges to identify key opportunities for AI integration.
Phase 2: Custom Model Development
Tailored development of ML models, leveraging our framework, to predict electronic structures and properties specific to your target alloy systems and research goals.
Phase 3: Integration & Training
Seamless integration of the AI platform into your existing computational environment, coupled with hands-on training for your research teams.
Phase 4: Optimization & Scaling
Continuous monitoring, performance optimization, and scaling of the AI solution to support expanding research efforts and drive ongoing innovation.
Ready to Transform Your Materials Research?
Schedule a personalized consultation with our AI experts to explore how our electronic structure prediction capabilities can revolutionize your R&D and accelerate discovery.
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