Journal of Multiscale Materials Informatics
Journal of Multiscale Materials Informatics (JIMAT) is a peer-reviewed, open-access, free of APC (until December 2025), and published 2 times (April and October) in one year. JIMAT is an interdisciplinary journal emphasis on cutting-edge research situated at the intersection of materials science and engineering with data science. The journal aims to establish a unified platform catering to researchers utilizing and advancing data-driven methodologies, machine learning (ML), and artificial intelligence (AI) techniques for the analysis and prediction of material properties, behavior, and performance. Our overarching mission is to propel and distribute innovative research that expedites the progress of materials research and discovery through the utilization of data-centric approaches. The journal publishes papers in the areas of, but not limited to: a. Interdisciplinary research integrating physics, chemistry, biology, mathematics, mechanics, engineering, materials science, and computer science. b. Materials informatics, physics informatics, bioinformatics, chemoinformatics, medical informatics, agri informatics, geoinformatics, astroinformatics, etc. c. Quantum computing, quantum information, quantum simulation, quantum error correction, and quantum sensors and metrology. d. Artificial intelligence, machine learning, and statistical learning to analyze materials data. e. Data mining, big data, and database construction of materials data. f. Data-driven discovery, design, and development of materials. g. Development of software, codes, and algorithms for materials computation and simulation. h. Synergistic approaches combining theory, experiment, computation, and artificial intelligence in materials research. i. Theoretical modeling, numerical analysis, and domain knowledge approaches of materials structure-activity-property relationship.
Articles
32 Documents
<![CDATA[Implementation of a Finite State Machine for Controlling NPC Customer Behavior in the 2D Game "Cooking Chaos" ]]>
<![CDATA[Syakira Tasya]]>;
<![CDATA[Diny Syarifah Sany]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 3 No. 2 (2026): October (In Progress) ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v3i2.17570
<![CDATA[Non-Player Characters (NPC) are components in games that can be used to create interactions and gameplay dynamics. NPC behavior control is required to enable characters to respond appropriately to conditions occurring during gameplay. This study aims to implement a Finite State Machine (FSM) to control customer NPC behavior in the Unity-based 2D game Cooking Chaos. The implemented FSM consists of four states: Waiting, Angry, Chasing, and Attack. State transitions are influenced by the customer’s patience system and interaction conditions with the player. When the patience time expires, the customer changes behavior from waiting to becoming angry, chasing, and potentially attacking the player. The implementation was evaluated using Black Box Testing, FSM Transition Testing, and Multi-Customer Testing. The Black Box Testing results showed that all 12 functional scenarios were valid. FSM Transition Testing on seven scenarios, each tested 10 times, resulted in 70 successful trials out of 70. Multi-Customer Testing demonstrated that the order, patience, and FSM mechanisms operated independently with up to three active customers simultaneously. The results indicate that the implemented FSM consistently controls customer NPC behavior transitions under the defined test scenarios and integrates the service mechanism with direct gameplay consequences for the player.]]>
<![CDATA[Resource-Constrained Perception: Deploying Quantized Deep Learning Models on Microcontrollers for Real-Time Robotic Vision ]]>
<![CDATA[Enjoy Bhodra]]>;
<![CDATA[Md Nahiduzzaman Hridoy]]>;
<![CDATA[M A Shahriar]]>;
<![CDATA[Iffat Salim Toaha]]>
<![CDATA[ Journal of Multiscale Materials Informatics Vol. 3 No. 2 (2026): October (In Progress) ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro ]]>
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DOI: 10.62411/jimat.v3i2.17734
<![CDATA[Robotic vision enables machines to "see" and interpret the world around them. It can help with tasks such as obstacle avoidance, navigation, and object detection. This kind of work is well suited to modern deep learning but requires a lot of memory, power, and processing speed. Most of the small robots do not have a powerful computer. However, they have a low-cost microcontroller. This is a smaller computer. These devices are very resource-constrained. It becomes difficult to run regular deep learning models. This research work proposes to address the above issue with model quantization. When a model number is not a nice integer. It is quantized, i.e., rounded to a simpler integer with 8 bits. Such a reduction reduces memory consumption and increases the computing speed. This work is a comparison of two popular approaches. They are Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT). Researchers construct a small and simple ConvNet that has only 28,069 parameters. It is examined on two popular microcontrollers: ARM Cortex M7 and ESP32. Both methods have been found to be effective. By reducing the use of the flash memory by approximately 71% and RAM by 60%, quantization cuts down memory usage. It doubles the processing speed and reduces power consumption by 35 to 40 percent. QAT retains 99% of the original model accuracy. The advantages of PTQ are that it is faster to set up, but with slightly less accuracy. The model performs well in real time on both platforms, with no assistance from the clouds. This work shows deep learning can run well on low-power devices. It helps make smart, low-cost robots more widely available.]]>
