AIMC Topic: Materials Science

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Computational Material Science Has a Data Problem.

Journal of chemical information and modeling Jun 4, 2025
We present an overdue questioning of the computational material science data: Is it suitable for training machine learning models? By examining the energy above the convex hull (), the electronic bandgap, and the formation energy data in the Material...
Machine Learning Density Functional Theory Materials Science
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Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design.

Journal of chemical information and modeling Nov 21, 2024
In this study, we introduced Matini-Net, which is a versatile framework for feature engineering and automated architecture design for materials informatics research using deep neural networks. Matini-Net provides the flexibility to design feature-bas...
Neural Networks, Computer Deep Learning Informatics Materials Science
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Machine-Learning-Assisted Materials Discovery from Electronic Band Structure.

Journal of chemical information and modeling Nov 3, 2024
Traditional methods of materials discovery, often relying on intuition and trial-and-error experimentation, are time-consuming and limited in their ability to explore the vast design space effectively. The emergence of machine learning (ML) as a powe...
Machine Learning Algorithms Cluster Analysis Electrons Materials Science
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ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT.

Journal of chemical information and modeling Jan 18, 2024
The pursuit of designing smart and functional materials is of paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, and numerous others. Consequently, researchers ar...
Humans Artificial Intelligence Machine Learning Language Electronics Materials Science
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Artificial Intelligence Agents for Materials Sciences.

Journal of chemical information and modeling Dec 12, 2023
The artificial intelligence (AI) tools based on large-language models may serve as a demonstration that we are reaching a groundbreaking new paradigm in which machines themselves will generate knowledge autonomously. This statement is based on the as...
Humans Artificial Intelligence Language Learning Research Personnel Materials Science
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Mateverse, the Future Materials Science Computation Platform Based on Metaverse.

The journal of physical chemistry letters Dec 29, 2022
Currently, computational materials science involves human-computer interaction through coding in software or neural networks. There is still no direct way for human intelligence endorsement. The digitalization of human intelligence should be the ulti...
Humans Neural Networks, Computer Artificial Intelligence Software Water Materials Science
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Functional Output Regression for Machine Learning in Materials Science.

Journal of chemical information and modeling Oct 10, 2022
In recent years, there has been a rapid growth in the use of machine learning in material science. Conventionally, a trained predictive model describes a scalar output variable, such as thermodynamic, electronic, or mechanical properties, as a functi...
Machine Learning Models, Statistical Polymers Materials Science
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Neural network training method for materials science based on multi-source databases.

Scientific reports Sep 12, 2022
The fourth paradigm of science has achieved great success in material discovery and it highlights the sharing and interoperability of data. However, most material data are scattered among various research institutions, and a big data transmission wil...
Humans Neural Networks, Computer Databases, Factual Materials Science
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A universal similarity based approach for predictive uncertainty quantification in materials science.

Scientific reports Sep 2, 2022
Immense effort has been exerted in the materials informatics community towards enhancing the accuracy of machine learning (ML) models; however, the uncertainty quantification (UQ) of state-of-the-art algorithms also demands further development. Most ...
Machine Learning Algorithms Cluster Analysis Uncertainty Materials Science
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Neural Network Potentials: A Concise Overview of Methods.

Annual review of physical chemistry Jan 4, 2022
In the past two decades, machine learning potentials (MLPs) have reached a level of maturity that now enables applications to large-scale atomistic simulations of a wide range of systems in chemistry, physics, and materials science. Different machine...
Neural Networks, Computer Machine Learning Algorithms Physics Materials Science
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