Inferring energy-composition relationships with Bayesian optimization enhances exploration of inorganic materials

2023/02/01 by Andrij Vasylenko, Benjamin M. Asher, Vasylenko, Andrij +11 · 1 citation
Chemical Engineering · Materials Science · #Catalysis and Oxidation Reactions #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #X-ray Diffraction in Crystallography

paper · pdf · doi:10.48550/arxiv.2302.00710

openalex publication_date 2023/02/01 · openalex created_date 2023/02/04 · openalex updated_date 2026/07/28

Abstract

Computational exploration of the compositional spaces of materials can provide guidance for synthetic research and thus accelerate the discovery of novel materials. Most approaches employ high-throughput sampling and focus on reducing the time for energy evaluation for individual compositions, often at the cost of accuracy. Here, we present an alternative approach focusing on effective sampling of the compositional space. The learning algorithm PhaseBO optimizes the stoichiometry of the potential target material while improving the probability of and accelerating its discovery without compromising the accuracy of energy evaluation.

Cited by

• Introducing physics-informed generative models for targeting structural novelty in the exploration of chemical space

Related

• Electronic structure prediction of medium and high entropy alloys across composition space
• Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material Design
• Element selection for functional materials discovery by integrated machine learning of elemental contributions to properties
• Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation
• Leveraging Composition-Based Material Descriptors for Machine Learning Optimization