# Machine Learning in Materials Science

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/machine-learning-in-materials-science/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the application of materials informatics, machine learning, and high-throughput computational techniques to accelerate materials innovation. It encompasses topics such as property predictions, crystal structures, molecular dynamics, and data mining in the context of materials science and engineering. |
| Domain | Physical Sciences |
| Field | Materials Science |
| OpenAlex ID | t11948 |
| Works | 27 |

## Topic papers all

Showing 15 of 27.

- [Scientific discovery in the age of artificial intelligence](https://scholariq.org/papers/scientific-discovery-in-the-age-of-artificial-intelligence/)
- [Applications of Deep Learning and Reinforcement Learning to Biological Data](https://scholariq.org/papers/applications-of-deep-learning-and-reinforcement-learning-to-biological-data/)
- [Low-Energy Electron Diffraction](https://scholariq.org/papers/low-energy-electron-diffraction/)
- [A literature review on the state-of-the-art in patent analysis](https://scholariq.org/papers/a-literature-review-on-the-state-of-the-art-in-patent-analysis/)
- [Deep learning in drug discovery: an integrative review and future challenges](https://scholariq.org/papers/deep-learning-in-drug-discovery-an-integrative-review-and-future-challenges/)
- [Metal–support frontier orbital interactions in single-atom catalysis](https://scholariq.org/papers/metal-support-frontier-orbital-interactions-in-single-atom-catalysis/)
- [An effective self-supervised framework for learning expressive molecular global representations to drug discovery](https://scholariq.org/papers/an-effective-self-supervised-framework-for-learning-expressive-molecular-global/)
- [Big Data Software Engineering: Analysis of Knowledge Domains and Skill Sets Using LDA-Based Topic Modeling](https://scholariq.org/papers/big-data-software-engineering-analysis-of-knowledge-domains-and-skill-sets-using/)
- [Tracing the evolution of AI in the past decade and forecasting the emerging trends](https://scholariq.org/papers/tracing-the-evolution-of-ai-in-the-past-decade-and-forecasting-the-emerging/)
- [Inverse Design of Materials by Machine Learning](https://scholariq.org/papers/inverse-design-of-materials-by-machine-learning/)
- [AI-driven robotic chemist for autonomous synthesis of organic molecules](https://scholariq.org/papers/ai-driven-robotic-chemist-for-autonomous-synthesis-of-organic-molecules/)
- [Monte Carlo Simulations of Au <sub>38</sub> (SCH <sub>3</sub> ) <sub>24</sub> Nanocluster Using Distance-Based Machine Learning Methods](https://scholariq.org/papers/monte-carlo-simulations-of-au-sub-38-sub-sch-sub-3-sub-sub-24-sub-nanocluster/)
- [Predicting the Structure–Activity Relationship of Hydroxyapatite-Binding Peptides by Enhanced-Sampling Molecular Simulation](https://scholariq.org/papers/predicting-the-structure-activity-relationship-of-hydroxyapatite-binding/)
- [Molecular generation targeting desired electronic properties <i>via</i> deep generative models](https://scholariq.org/papers/molecular-generation-targeting-desired-electronic-properties-i-via-i-deep/)
- [Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials](https://scholariq.org/papers/deep-learning-models-for-predicting-gas-adsorption-capacity-of-nanomaterials/)

## Topic primary papers

- [Inverse Design of Materials by Machine Learning](https://scholariq.org/papers/inverse-design-of-materials-by-machine-learning/)
- [Molecular generation targeting desired electronic properties <i>via</i> deep generative models](https://scholariq.org/papers/molecular-generation-targeting-desired-electronic-properties-i-via-i-deep/)
- [Artificial intelligence and machine learning-driven design of self-healing biomedical composites](https://scholariq.org/papers/artificial-intelligence-and-machine-learning-driven-design-of-self-healing/)
- [A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A <sub>2</sub> B <sup>+</sup> B <sup>3+</sup> X <sub>6</sub>](https://scholariq.org/papers/a-domain-knowledge-enhanced-machine-learning-method-to-predict-the-properties-of/)
- [Accelerated Structural Optimization for the Supported Metal System Based on Hybrid Approach Combining Bayesian Optimization with Local Search](https://scholariq.org/papers/accelerated-structural-optimization-for-the-supported-metal-system-based-on/)
- [Machine learning framework for derivation and optimization of Cz-YAG crystal growth recipe](https://scholariq.org/papers/machine-learning-framework-for-derivation-and-optimization-of-cz-yag-crystal/)
- [Constraint-Aware Neurosymbolic Uncertainty Quantification with Bayesian Deep Learning for Scientific Discovery](https://scholariq.org/papers/constraint-aware-neurosymbolic-uncertainty-quantification-with-bayesian-deep/)
- [Constraint-Aware Neurosymbolic Uncertainty Quantification with Bayesian Deep Learning for Scientific Discovery](https://scholariq.org/papers/constraint-aware-neurosymbolic-uncertainty-quantification-with-bayesian-deep-2/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.