Preferred Computational Chemistry, Inc.

🎉 We are thrilled to announce that our very own Dr. Takamoto has been honored as one of the recipients of the "Nice Step Researcher Award" for 2024 by the National Institute of Science and Technology Policy (NISTEP) in Japan. His pioneering work in the fusion of machine learning and materials science, particularly through the development of Matlantis, has been pivotal in advancing atomic simulations. 🔬 In celebration of this award, Dr. Takamoto used Matlantis to simulate a "Nicestep" material composed of a mixture of five elements: Ni, Ce, S, Te, and P. Matlantis supports all elements found in nature and has led to the simulation of novel materials, showcasing the vast potential for future scientific discoveries and technological advancements that his groundbreaking research holds. Details (Japanese site): https://lnkd.in/gVdimE6p

🔬 We are excited to share a recent publication titled "Experimental study on Na⁺ conductivity in NaAlBr₄ and atomic-scale investigation of Na⁺ conduction" by one of our Matlantis users, shedding light on the ionic conduction properties of NaAlBr₄. This study marks a significant advancement in the exploration of Na⁺ conductivity in alkali Al bromides, an area previously underexplored compared to other metal halides. 📊 The researchers discovered that NaAlBr₄ shows enhanced Na⁺ conductivity at room temperature, notably outperforming its isostructural counterpart, NaAlCl₄. This remarkable conductivity was achieved despite the high energy barriers for creating vacancies and interstitials, suggesting a complex conduction mechanism. 🧩 Using Matlantis's Neural Network Potential (NNP-MD) simulations, the team investigated the atomic-scale pathways of Na⁺ conduction. These simulations suggested that transient defects, introduced through ball milling, play a crucial role in facilitating the Na⁺ movement across the material, providing deeper insights into how Na⁺ conduction may be achieved in NaAlBr₄. 🔍 This study highlights the potential of NaAlBr₄ as a viable solid electrolyte, contributing to the development of safer and more efficient Na-ion batteries. The findings underscore the importance of computational tools in material science, and suggest that further optimization could lead to even greater enhancements in Na⁺ conductivity. For more details, visit the full publication here. https://lnkd.in/g2ncvF7c #MaterialScience #SolidStateBatteries #NeuralNetwork

🚀 We are thrilled to announce our participation in the 24th Annual Advanced Automotive Battery Conference (AABC), where we will be presenting a poster on cutting-edge research. The AABC is a key event for automotive battery technology, bringing together industry leaders and researchers to discuss groundbreaking advancements and trends. 📅 Conference Information: Date: December 9 – 12, 2024 Location: Mandalay Bay Resort & Casino, Las Vegas, NV 💡 The poster will showcase research utilizing Matlantis, a high-performance atomistic simulation platform with a universal neural network model, specifically applied to the field of battery technology. Key highlights of the research include: - Revealing the instability of layered LiMnO2 nanostructures at high temperatures due to MnO6 to MnO4 transitions. - Identifying the crucial role of PS3 species in stabilizing interfaces in sodium all-solid-state batteries. - Examining the effect of nickel content on electrolyte adsorption on LiNixMnyCozO2 cathode surfaces. These studies demonstrate the platform's capability to provide atomic-level insights into complex material systems, advancing energy storage research. We invite you to visit our poster to learn more about these findings and discuss how these applications can contribute to the future of automotive battery technology. https://lnkd.in/gfnzpHwx #AABC2024 #MaterialsScience #MachineLearning

📢 Join our free webinar: "Exploring New Frontiers in Materials Discovery: Insights into Amorphous Materials through Atomistic-Level Simulation with Matlantis" 🗓 Date & Time: USA: Tuesday, January 14, 2025 at 6:00 – 7:00 pm ET | 3:00 – 4:00 pm PT 🔍 Discover how advanced atomistic simulations are revolutionizing the study of amorphous dielectric materials. Learn about: - High-speed, accurate modeling of complex atomic interactions - Breakthroughs in understanding the composition-structure-property relations - Applications to elastic properties of SiOC and SiON systems

🔬 We are excited to highlight a recent study by Matlantis users on "Stress-Induced Martensitic Transformation in Na3YCl6". This research explores how stress can trigger martensitic transformation in nonoxide materials. 📊 The study demonstrates that anisotropic crystallographic transformation from monoclinic to rhombohedral Na3YCl6 occurs exclusively under uniaxial pressure, with no effect under hydrostatic pressure conditions. This finding was supported by in situ synchrotron X-ray diffraction and atomistic simulations using Matlantis. 🙌 Congratulations to the researchers for these impactful findings! Explore the full details in the abstract and research paper. https://lnkd.in/gtFq82CM #MaterialsScience

🌟 Exciting News! 🌟 PFCC will be presenting and exhibiting at the 2024 MRS Fall Meeting & Exhibit. We have an impressive lineup with a total of 13 presentations focusing on Matlantis, highlighting its impact and innovations in materials discovery. 📊 From PFCC, we have 3 poster presentations that will delve into various aspects: - The Surface Configurations and Their Impact on Pd-Based Alloy Membranes for Hydrogen Separation—An Application of a Universal Neural Network Potential - Evaluation of a Universal Neural Network Potential for Predicting Finite Temperature Properties Using Quasi-Harmonic Approximation - Wet Hydrofluoric Acid Etching Reaction Mechanism Analysis of Silicon Oxide Using GRRM with Universal Neural Network Potential 🤝 We are also thrilled to announce contributions from esteemed external collaborators: ENEOS Corporation, Massachusetts Institute of Technology, NARA Institute of Science and Technology, Preferred Networks, inc., and Waseda University, who will present 3 posters and 7 oral presentations. 🌐 We are also exhibiting at the event. Come visit us and explore our innovations! 📅 Exhibition Dates and Hours: December 3 - 5, 2024 📍 Location: Booth 419, Hynes Convention Center, Boston, Massachusetts Join us to discover the latest advancements and research insights. We look forward to seeing you there! For more details about the conference, please visit our webisite: https://lnkd.in/gJMtB2uh #MRS2024 #MaterialsScience #NeuralNetwork

🔍 New Case Study on Catalyst Screening for Ammonia Synthesis Using Matlantis 🌿 🌱 Ammonia is a critical component in industrial processes, essential for fertilizers, pharmaceuticals, and many other chemicals. Efficient ammonia synthesis is a key for realizing a sustainable society, leading to extensive research in this area. 🔧 The development of catalysts, especially heterogeneous catalysts, is crucial for efficient ammonia synthesis. These catalysts play a pivotal role not only in ammonia production but also in various other applications such as fuel production and pollution remediation. In this study, Matlantis was used for virtual screening of heterogeneous catalysts for ammonia synthesis. 🌟 Matlantis’s high speed and accuracy have drastically improved the efficiency of catalyst screening, previously a time-consuming task especially for transition state calculations. This method highlights the potential for future catalyst exploration and represents a significant advance towards industrial optimization and sustainability. For a detailed understanding of the methodologies and results, I encourage you to explore the full study: https://lnkd.in/gYkYD_PJ #Catalysis #Sustainability #MaterialScience

🌟 Exciting News! 🌟 We are delighted to share a significant new research paper authored by a Matlantis user: "Effect of very slow O diffusion at high temperature on very fast H diffusion in the hydride ion conductor LaH₂.₇₅O₀.₁₂₅". 📚 In this study, the author used advanced molecular dynamics (MD) simulations powered by neural network potentials (NNP) to examine the diffusion behavior of LaH₂.₇₅O₀.₁₂₅. By varying the atomic masses of the elements and analyzing diffusion at both high and low temperatures, he sought to understand the conducting mechanism of a remarkable hydride conductor LaH₂.₇₅O₀.₁₂₅. 🔍 A critical finding of this research is the identification of the origin of the bending in the Arrhenius plot around 550 K (referred to as Tc). Below this temperature, oxygen atoms exhibit very slow diffusion, while above this temperature, oxygen diffusion significantly impacts the hydrogen diffusion process. This dual behavior was confirmed using innovative approaches, including the variance of atom positions and selective mass alteration of elements in simulations. 🚀 These results not only advance our understanding of the diffusion mechanisms in hydride ion conductors but also open up new possibilities for improving hydrogen-based technologies. This groundbreaking study demonstrates the powerful capabilities of Matlantis tools in advancing materials science research. For a deeper understanding of these findings, I encourage you to read the paper: https://lnkd.in/gM4_bDVJ #HydrogenConductors #MaterialScience #MachineLearningPotential

🔬 Exciting news from the Matlantis community! Our valued user has recently published a fascinating study in a Japanese journal titled "Adsorption of Phosphorus-Type Anti-Wear Agents for Refrigerator Nonpolar-Oil Additives," shedding light on future perspectives in lubricant formulations. 💡 The research characterizes the adsorption behavior of several phosphorus-type anti-wear agents on iron oxide surfaces in hexane. Using a quartz crystal microbalance with dissipation monitoring technique, the study found that the adsorption amounts are significantly influenced by the chemical reactivity and polarity of the agents. Additionally, the results indicated that the presence of benzene rings in their structures also play some roles. 🧪 Matlantis's atomistic level simulator helped estimate the adsorption energy, corroborating the experimental findings. Four-ball tests further demonstrated that the adsorbed films on iron oxide surfaces effectively enhance wear resistance. This provides foundational knowledge for improving lubricant formulations. We are proud to see how Matlantis contributed to these significant discoveries, supporting both theoretical and experimental approaches. 🌐 Read more about this innovative study: https://lnkd.in/grU_d3bu Visit our website to learn more about Matlantis: https://matlantis.com/ #MaterialsScience #Lubricant #NeuralNetwork

🔬 Recently, one of our valued Matlantis users published an intriguing paper on metal-CO2 batteries, an emerging technology with the potential to revolutionize energy storage solutions. This research is particularly relevant for industries such as electric vehicles and renewable energy storage. The primary challenge addressed in the study is the high voltage gap during the charging and discharging cycles of metal-CO2 batteries, which lowers their efficiency and hinders practical applications. By conducting a series of controlled experiments and advanced simulations, the researchers have shed light on this issue. Their in situ ambient pressure x-ray photoelectron spectroscopy (APXPS) study revealed that in a pure CO2 environment, the reduction activity is low, primarily producing CO as a discharge product. However, when oxygen (O2) or water (H2O) is introduced alongside CO2, the formation of Csp2 species, akin to graphite, occurs. This discovery is significant as these Csp2 structures emerged due to the decomposition of ionic liquid electrolytes, thus enhancing the efficiency of the battery. 🔍 The simulations, supported by Matlantis' neural network potential, played a crucial role in corroborating the experimental data, providing a theoretical foundation for these observations. Read more: https://lnkd.in/gZP-Nz7g Visit our website and learn more about Matlantis: https://matlantis.com/ #EnergyStorage #CO2Batteries #NeuralNetwork #MaterialScience