[00838] Perspectives in Artificial Intelligence and Machine Learning in Materials Chemistry, 2nd edition
Session Date & Time :
00838 (1/2) : 5B (Aug.25, 10:40-12:20)
00838 (2/2) : 5C (Aug.25, 13:20-15:00)
Type : Proposal of Minisymposium
Abstract : Artificial Intelligence has led to a paradigm shift in investigation in Materials Chemistry, with Machine Learning allowing informatics-based systematic calculations, predictions and discovery based on material databases pushing beyond the intrinsic limitations of first-principles calculations. The successful application requires development of novel methodologies inspired by the frontends of materials development in close synergy between Mathematics and Information Technology, areas where interdisciplinary collaborations are crucial and yet to date in their early phases.
Yoh-ichi Mototake (The Institute of Statistical Mathematics)
Aleksandar Staykov (I2CNER)
Kato Koichiro (Kyushu university)
Shigenori Fujikawa (I2CNER)
Kulbir Ghuman (INRS)
David Rivera (Hiroshima University)
Talks in Minisymposium :
[01335] Understanding the role of defects and disorder in polycrystalline materials
Author(s) :
Kulbir K Ghuman (Institut national de la recherche scientifique)
Abstract : The functionality of the materials used for energy applications is critically determined by the physical properties of small active regions such as dopants, dislocations, interfaces, grain boundaries, etc. The capability to manipulate and utilize the inevitable disorder in materials, whether due to the finite-dimensional defects (such as vacancies, dopants, grain boundaries) or due to the complete atomic randomness (as in amorphous materials), can bring innovation in designing energy materials. With the increase in computational material science capabilities, it is now possible to understand the complexity present in materials due to various defects resulting in pathways required for optimizing their efficiencies. In this talk, I will provide a critical overview of such computational advancements specifically for designing realistic materials with various types of defects. I will discuss the traditional approaches (implemented via tools such as density functional theory, and molecular dynamics) as well as modern approaches such as machine learning that exist for understanding the impact of defects and disorder present in polycrystalline materials, thereby identifying future opportunities for energy materials design and discovery.
[01431] Discovery of New Materials by Quantum Calculations and Artificial Intelligence
Author(s) :
David Samuel Rivera Rocabado (Hiroshima University)
Mika Aizawa (Hiroshima University)
Takayoshi Ishimoto (Hiroshima University)
Abstract : Integrating artificial intelligence into real system first-principles calculations has the potential to transform the field of materials science and, ultimately, the world in which we live. In this presentation, the modeling and the prediction of the CO adsorption/activation on Ru nanoparticles exemplify that our new approach can be universally applied to predict the catalytic properties of any existing material and be used for the discovery of more functionalized materials.
[01449] Topological descriptor of thermal conductivity in amorphous Si
Author(s) :
Emi Minamitani (Osaka University)
Takuma Shiga (AIST)
Makoto Kashiwagi (Aoyama Gakuin University)
Ippei Obayashi (Okayama University)
Abstract : In this talk, we analyze the relationship between the atomic configuration of amorphous Si and the thermal conductivity of the material. A topological descriptor constructed by persistent homology successfully predicts the thermal conductivity using machine learning, and from the machine learning model, we can extract medium-range order structures related to thermal conductivity using inverse analysis of persistent homology. https://doi.org/10.1063/5.0093441
[01688] Toward Functional Polymer Informatics
Author(s) :
Koichiro Kato (Kyushu University)
Abstract : Although materials informatics has made remarkable progress in recent years, its application to functional polymers remains unexplored. Reasons for this include the lack of established methods for incorporating the higher-order structure of functional polymers as a feature in machine learning and the lack of data. In this talk, I will present our recent results on building models for predicting properties from published data and extracting features of functional polymers using coarse-grained simulation.
[01724] A Trial for the Realization of Material Pattern Informatics Using Interpretable AI
Author(s) :
Yoh-ichi Mototake (Hitotsubashi University)
Abstract : It has been recently reported that highly accurate classification, regression, and generation can be achieved by interpolative modeling of complex scientific data using machine learning models with high expressive power, such as deep neural networks (DNNs). However, many of the machine learning models used there are nonlinear functions with a large number of parameters, making the interpretation of the training results very difficult. In thermodynamics, Gibbs extended the theory of thermodynamics, which was the theory of heat engines, to chemical reactions, which was a great development in science. This shows that science has been greatly advanced by the scientific insight of human beings, who derive general principles beyond mere interpolation models and boldly extrapolate them. On the other hand, it is sometimes difficult to apply such insights to systems with complex non-periodic structures, such as those found in nonlinear and nonequilibrium phenomena. To address this situation, we believe that it is important to collaborate between machine learning, which is good at building interpolation models for complex data, and humans, who can make bold extrapolations based on scientific insights, and are developing methods for interpreting machine learning training results to bridge the gap between the two.
In this presentation, we will discuss our recent research on machine learning frameworks that collaborate with scientists trying to reveal complex pattern dynamics in materials and their applications.
[05452] Causal analysis of materials functionality by combining topological data analysis and physical model
Author(s) :
Masato Kotsugi (Tokyo University of Science)
Abstract : Microscopic image data is key to developing low-power, high-speed electronic devices. However, the complex interactions in nanoscale magnetic materials are difficult to understand. We developed a new functional design theory called “extended Landau free energy model” that combines persistent homology and machine learning with free energy to automate the interpretation of the microscopic image. This model illustrates the physical mechanism and critical location of magnetization reversal and proposes a device structure with low energy consumption.
