一般財団法人材料科学技術振興財団　MST Product Lineup

From very small and simple systems such as single molecules and clusters to relatively complex systems such as interfaces, defects, and external fields (electric fields, pressure, etc.), we can handle a wide range of scenarios. ■ Effective Situations - When you want to verify hypotheses established at the atomic or molecular level regarding the phenomenon of interest - When you want to perform detailed attribution and interpretation of various spectroscopic spectra ■ Features of MST - We can provide a one-stop service from the proposal of computational models to analysis - We have our own computational environment, allowing us to handle highly confidential data - Simulations leveraging the expertise of analysis and AI specialists are possible ■ Supported Computational Methods - Quantum chemical calculations - First-principles calculations - Molecular dynamics calculations

- AI Model Development Based on collected data that includes the target object and publicly available open data, we will develop an AI model suitable for the conditions. By utilizing this AI model, it is expected that daily operations can be replaced by AI, promoting automation and efficiency in business processes and stabilizing accuracy. - Image/Data Analysis We analyze and evaluate images and other data. The evaluation content will be discussed with the client, and proposals will be made in line with the challenges. If the target is a product or material, analysis data can be obtained using MST.

MOFs (Metal-Organic Frameworks) are attracting attention as nano-porous materials with potential applications in separation processes, catalysis, and gas storage. The combinations of MOF topology, linkers, and metal nodes are vast, and the types of synthesizable MOFs are virtually limitless. Therefore, prior screening and understanding of the structure, function, and properties are crucial for the development of new MOF materials. In this case study, we predicted the CO2 adsorption amounts for various MOFs using the GCMC (Grand Canonical Monte Carlo) method and examined the factors affecting the adsorption characteristics.

ALD (Atomic Layer Deposition) is a method that allows for uniform film formation at the atomic layer level, even for high aspect ratio structures, and is being focused on as a useful technique for the miniaturization, integration, and fine wiring of semiconductors. Since it is a vapor-phase deposition method that utilizes the "adsorption saturation of precursors on the growth surface," high vapor pressure precursors are required to improve the coverage rate of precursors on the substrate and the deposition rate. This document predicts the vapor pressures of various Ga-containing precursors through molecular dynamics calculations and compares them with experimental values.

In area-selective atomic layer deposition (AS-ALD), small molecule inhibitors (SMIs) control the surface selectivity of precursor adsorption on the substrate surface. SMIs prevent unwanted deposition on the non-growth surfaces of the substrate and play a crucial role in thin film formation during the deposition process. In this case study, we used the Grand Canonical Monte Carlo (GCMC) method to predict the competitive adsorption amounts of SMIs (aniline, pyridine) and TMA (trimethylaluminum) as single and double molecular species on the Cu(111) surface. This analysis is effective for evaluating adsorption characteristics, including the steric effects of the molecules.

Measurement Method: Computational Science, AI, Data Analysis Product Field: Secondary Batteries Analysis Purpose: Thermal Property Evaluation