Software:Materials Square

From HandWiki
Materials Square
Developer(s)Virtual Lab
PlatformWeb
TypeMaterials Science, Computational Materials Science Simulation software
Websitewww.materialssquare.com

Materials Square is a web-based materials simulation platform developed by Virtual Lab[1], a company based in South Korea. The platform integrates a graphical user interface (GUI) for materials simulations with a cloud-based computing server. This platform provides various materials simulation methodologies such as Density Functional Theory (DFT), Molecular Dynamics (MD), and CALPHAD

Open Source Softwares

Materials Square leverages a suite of open-source software engines to facilitate simulation operations. These include Quantum ESPRESSO[2] for DFT Simulation with a Plane wave basis, GAMESS (US)[3] for DFT simulations using an atomic orbital basis, Open Calphad[4] for CALPHAD simulations, and LAMMPS[5] for Molecular Dynamics simulations.

Impacting Advanced Materials Research

Computational materials science and computational chemistry are rapidly advancing fields, requiring specialized knowledge in high-performance computing (HPC) scripting, command-line interfaces, and the Linux operating system. Further, adequate computational resources are crucial, which often implies investment in high-cost supercomputing facilities. Researchers design structures, apply methods such as Density Functional Theory (DFT) or Molecular Dynamics (MD), and then utilize software for data visualization. Software tools used in this process, such as VASP[6], Quantum ESPRESSO, and LAMMPS[5], need a proper setup and compilation time.

Materials Square offers a more streamlined approach, incorporating open-source codes with precompiled packages and libraries. This setup reduces the time and resources spent on setup and maintenance tasks, allowing researchers to focus on their investigations. The platform also illustrates the growing role of cloud computing in scientific research [7], providing the ability to perform atomistic simulations on cloud servers, removing the need for local computational resources and offering enhanced flexibility.

The origin of HPC and the shift to the cloud

HPC and cloud computing have significantly contributed to the evolution of technology, with the two fields being interrelated and having shaped each other significantly[8].

High-Performance Computing

HPC, which primarily focuses on using supercomputers and parallel processing techniques for solving complex computational problems, traces its origins back to the 20th century[9]. The genesis of high-performance computing can be attributed to the Control Data Corporation's CDC 6600, the first supercomputer, designed by Seymour Cray in 1964[10]. The CDC 6600, which cost roughly $8 million and had the ability to perform up to three million instructions per second, signaled the dawn of the HPC era.

Today, HPC technology spans from miniature supercomputers, like Nvidia's Jetson AGX Xavier[11], to colossal machines like Fugaku, developed by RIKEN and Fujitsu in Japan[12]. HPC has become an essential tool for scientific, engineering, and data analysis tasks demanding substantial computational power. In particular, the United States, China, and Japan are leading the world in building and utilizing HPC systems[13].

HPC in Material Science and Chemistry

HPC has been instrumental in areas of material science and chemistry, providing computational power to simulate molecular structures and reactions, thereby enabling the design of new materials and the understanding of complex chemical phenomena [14].

Cloud Computing

With the progression of the internet and virtualization technologies, cloud computing emerged in the 21st century. Amazon, through Amazon Web Services (AWS) launched in 2006, popularized the concept of providing computing resources over the internet on an on-demand basis[15]. This development revolutionized access to computing resources, enabling businesses and individuals to harness high computational power without making significant capital investments[16].

The primary suppliers of cloud services today include Amazon Web Services, Microsoft Azure, and Google Cloud. Cloud computing serves a multitude of applications, ranging from data storage and virtual computing environments to artificial intelligence (AI) services. Its primary advantage is providing potent computing resources on demand, offering scalability and a pay-as-you-go model, thus reducing the need for upfront capital investment in infrastructure [16].

Applications of Cloud Servers

Cloud servers are utilized extensively across various industries and sectors, with applications ranging from data storage and backup, application hosting, website and blog hosting, to software and application development and testing, data analysis, and email hosting[17].

Notable examples of systems and services harnessing the power of cloud servers include:

  • Data Storage: Services such as Google Drive and Dropbox offer vast storage space, enabling users to store and retrieve data as required, in addition to providing backup services to prevent data loss[18].
  • Data Analysis: Companies employ cloud servers for Big Data analysis. Uber, for instance, uses cloud computing to process data obtained from its app for route optimization and demand prediction [20].
  • Software Development and Testing: Platforms like GitHub and Bitbucket offer cloud-based resources for software development, version control, and collaborative work[21].

Cloud Computing in Material Science and Chemistry

In the domains of material science and chemistry, cloud computing has emerged as an indispensable tool. The ability of cloud-based HPC to conduct complex simulations and process extensive datasets has facilitated numerous advancements in these fields [22]. For instance, scientists use cloud computing to study the molecular structures of materials and to simulate chemical reactions, contributing to the development of innovative materials and medicines[23].

MaterialSquare Platform

Materials Square is a platform that incorporates cloud computing for the purpose of materials science research. It focuses on the development of advanced materials, providing computational resources and a range of tools necessary for the design, simulation, and testing of these materials in a cloud-based environment. It is envisaged as a tool that can capitalize on the advantages of cloud computing in materials science, potentially facilitating more efficient and innovative research processes.

The platform's Graphical User Interface (GUI)

Materials Square employs a graphical user interface that aims to be accessible for users across a broad spectrum of experience levels. The design of the platform is intended to streamline the process of atomic and molecular modeling, as well as data pre and post-processing. In an effort to further enhance the user experience in material simulations, the platform also provides several features and tutorials for users

Support and Training

To cater to the needs of its users, Materials Square provides a comprehensive support system. The platform is designed so that users can get their technical and scientific inquiries addressed by Virtual Lab's team of materials researchers. To further enhance the understanding of its functionality and usage, the platform offers a one-time, one-on-one training for its users.

Simulation Updates and Tips

Materials Square provides its users with simulation tips on a range of topics, which are applicable to numerous research fields. These topics are not limited to but include solubility, viscoelastic, dielectric, and mechanical properties of polymers, glass transition temperature in polymers, techniques to correct Exchange-Correlation Error, considering Relativistic Effect in DFT : Spin-Orbit Coupling, Formation Energy's role in indicating a product's thermodynamic stability, the effect of Electric Field on Materials' Electronic Structure, use of Pseudopotential for effective simulation, visual inspection of electron distribution using Electron Charge Density, determining a material's electronic structure using band structure, and obtaining Adsorption Energy and Surface Energy via Slab Structure.

While these tips are available on Materials Square's platform, external validations for these topics can be found in numerous academic research papers. These papers cover topics such as polymer solubility[24], glass transition temperature [25], correction of Exchange-Correlation Error [26][27][28], and many others. Thus, Materials Square uses current, accepted research as a foundation for the simulation tips provided.

In summary, Materials Square combines user-friendly interfaces, cloud-based computing services, and thorough support, offering researchers a practical and economical solution for conducting material simulations.

Research Applications Utilizing Materials Square

The integrated DFT in Materials Square's have simplified complex calculations, finding applications across diverse research fields. Numerous research papers attest to the platform's robust capabilities.

Solar Cell Development:

Materials Square's contributed to solar cell research, as they aid in the exploration and development of innovative photovoltaic materials. For instance, in a study by Lee et al. (2021) titled "Oxide Passivation of Halide Perovskite Resistive Memory Device: A Strategy for Overcoming Endurance Problem," Materials Square's simulation capabilities played a significant role in understanding the passivation process of Halide Perovskite, a key material for solar cell development [29][30].

Battery Technology:

In the field of energy storage, Materials Square has been used for developing and optimizing lithium, sodium, and potassium-ion batteries, demonstrating its adaptability in studying various types of battery technologies. For instance, in a study by Lee et al. (2022) titled "Amorphization of germanium selenide driven by chemical interaction with carbon and realization of reversible conversion-alloying reaction for superior K-ion storage," the platform was used to study the complex electrochemical reactions in novel battery materials[31].

Hydrogen Storage and Production:

Research facilitated by Materials Square has contributed to the progress of hydrogen-related technologies, particularly in optimizing the efficiency of hydrogen evolution reactions. Bang et al. (2022) in their study "Phase-engineering terraced structure of edge-rich α-Mo2C for efficient hydrogen evolution reaction," used the Materials Square platform to model and understand the hydrogen evolution reactions on the surface of modified Mo2C, a promising catalyst for hydrogen production [32].


Radiation Damage:

Research into radiation damage, which holds potential implications for nuclear energy, space exploration, and other high-energy environments, has also benefited from the use of Materials Square. This platform has been instrumental in studying the atomic properties of Tantalum (Ta) and Tungsten-Tantalum (T-W) alloys under various collision cascade conditions. With the integration of molecular dynamic simulations in the Materials Square platform, the evolution of defects, the formation of atomic clusters, and the behavior of interstitial dislocation loops in these alloys have been thoroughly investigated [33].[34][35].

In addition, the platform has been utilized in diverse areas, including two-dimensional materials[36] [37] , dye degradation properties of co-doped ZnO[38], and electrolysis research[39].

References

  1. ↑ "Virtual Lab. Inc." (in en). 2023-06-30. https://www.nature.com/nature-index/institution-outputs/south-korea/virtual-lab-inc/5a153aced9d4a3004b57fac4. 
  2. ↑ Paolo Giannozzi; Stefano Baroni; Nicola Bonini; Matteo Calandra; Roberto Car; Carlo Cavazzoni; Davide Ceresoli; Guido L Chiarotti; Matteo Cococcioni; Ismaila Dabo; Andrea Dal Corso; Stefano de Gironcoli; Stefano Fabris; Guido Fratesi; Ralph Gebauer; Uwe Gerstmann; Christos Gougoussis; Anton Kokalj; Michele Lazzeri; Layla Martin-Samos; Nicola Marzari; Francesco Mauri; Riccardo Mazzarello; Stefano Paolini; Alfredo Pasquarello; Lorenzo Paulatto; Carlo Sbraccia; Sandro Scandolo; Gabriele Sclauzero; Ari P Seitsonen; Alexander Smogunov; Paolo Umari & Renata M Wentzcovitch (2009). "QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials". Journal of Physics: Condensed Matter. 21 (39): 395502. arXiv:0906.2569
  3. ↑ Young, David C. (2001). "Appendix A. A.2.3 GAMESS". Computational Chemistry: A Practical Guide for Applying Techniques to Real World Problems. Wiley-Interscience. p. 335
  4. ↑ Sundman Bo (2021). "Algorithms useful for calculating multi-component equilibria, phase diagrams and other kinds of diagrams". Calphad. 75: 102330. doi:10.1016/j.calphad.2021.102330.
  5. ↑ 5.0 5.1 "LAMMPS Molecular Dynamics Simulator". Sandia National Laboratories. Retrieved 2022-07-13.
  6. ↑ Kresse, G.; Furthmüller, J. (1996-10-15). "Efficient iterative schemes forab initiototal-energy calculations using a plane-wave basis set". Physical Review B 54 (16): 11169–11186. doi:10.1103/physrevb.54.11169. ISSN 0163-1829. http://dx.doi.org/10.1103/physrevb.54.11169. 
  7. ↑ Yang, Xiaoyu; Wallom, David; Waddington, Simon; Wang, Jianwu; Shaon, Arif; Matthews, Brian; Wilson, Michael; Guo, Yike et al. (2014-10-01). "Cloud computing in e-Science: research challenges and opportunities" (in en). The Journal of Supercomputing 70 (1): 408–464. doi:10.1007/s11227-014-1251-5. ISSN 1573-0484. https://doi.org/10.1007/s11227-014-1251-5. 
  8. ↑ Hochstein, Lorin (2011-08-01). High Performance Computing (HPC) Innovation Service Portal Pilots Cloud Computing (HPC-ISP Pilot Cloud Computing) (Report). Fort Belvoir, VA: Defense Technical Information Center. http://dx.doi.org/10.21236/ada549202. 
  9. ↑ Mezmaz, M.; Melab, N.; Talbi, E.-G. (2006). "A parallel exact hybrid approach for solving multi-objective problems on the computational grid". Proceedings 20th IEEE International Parallel & Distributed Processing Symposium (IEEE). doi:10.1109/ipdps.2006.1639525. http://dx.doi.org/10.1109/ipdps.2006.1639525. 
  10. ↑ Casanova, Henri; Dongarra, Jack (September 1997). "Netsolve: a Network-Enabled Server for Solving Computational Science Problems". The International Journal of Supercomputer Applications and High Performance Computing 11 (3): 212–223. doi:10.1177/109434209701100304. ISSN 1078-3482. http://dx.doi.org/10.1177/109434209701100304. 
  11. ↑ "Deploy AI-Powered Autonomous Machines at Scale" (in en-us). https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-agx-xavier/. 
  12. ↑ "June 2020 | TOP500". https://www.top500.org/lists/top500/2020/06/. 
  13. ↑ "List Statistics | TOP500". https://www.top500.org/statistics/list/. 
  14. ↑ Woodley, Scott M.; Catlow, C. Richard A. (2011). "High-performance computing in the chemistry and physics of materials". Proceedings: Mathematical, Physical and Engineering Sciences 467 (2131): 1880–1884. ISSN 1364-5021. https://www.jstor.org/stable/29792829. 
  15. ↑ "What’s New at AWS – Cloud Innovation & News" (in en-US). https://aws.amazon.com/about-aws/whats-new/2006/. 
  16. ↑ 16.0 16.1 "What is Cloud Computing & Why is it Important? | Accenture" (in en-US). https://www.accenture.com/us-en/cloud/insights/cloud-computing-index. 
  17. ↑ "Topics | IBM" (in en-us). https://www.ibm.com/topics. 
  18. ↑ Armbrust, Michael; Fox, Armando; Griffith, Rean; Joseph, Anthony D.; Katz, Randy; Konwinski, Andy; Lee, Gunho; Patterson, David et al. (2010-04-01). "A view of cloud computing". Communications of the ACM 53 (4): 50–58. doi:10.1145/1721654.1721672. ISSN 0001-0782. https://dl.acm.org/doi/10.1145/1721654.1721672. 
  19. ↑ "AWS Innovator: Netflix | Case Studies, Videos and Customer Stories" (in en-US). https://aws.amazon.com/solutions/case-studies/innovators/netflix/. 
  20. ↑ Song, Qi (2022-11-23). "Improving the swimming route optimization algorithm based on big data cloud computing and directional distance sequence processing". http://dx.doi.org/10.21203/rs.3.rs-2295520/v1. 
  21. ↑ Dabbish, Laura; Stuart, Colleen; Tsay, Jason; Herbsleb, Jim (2012-02-11). "Social coding in GitHub". Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (New York, NY, USA: ACM). doi:10.1145/2145204.2145396. http://dx.doi.org/10.1145/2145204.2145396. 
  22. ↑ "What Cloud Computing Has to Offer", Cloud Computing Strategies (CRC Press): pp. 43–70, 2010-07-27, ISBN 978-0-429-15133-0, http://dx.doi.org/10.1201/9781439834541-6, retrieved 2023-07-28 
  23. ↑ Guigourès, Romain; Gay, Dominique; Boullé, Marc; Clérot, Fabrice; Rossi, Fabrice (2015). Bifet, Albert; May, Michael; Zadrozny, Bianca et al.. eds. "Country-Scale Exploratory Analysis of Call Detail Records Through the Lens of Data Grid Models" (in en). Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science (Cham: Springer International Publishing): 37–52. doi:10.1007/978-3-319-23461-8_3. ISBN 978-3-319-23461-8. https://link.springer.com/chapter/10.1007/978-3-319-23461-8_3. 
  24. ↑ "Hildebrand solubility parameter" (in en), Wikipedia, 2023-06-24, https://en.wikipedia.org/w/index.php?title=Hildebrand_solubility_parameter&oldid=1161761981, retrieved 2023-08-01 
  25. ↑ "Glass Transition Temperature of Polymer". https://www.arsdcollege.ac.in/wp-content/uploads/2020/04/Glass-transition-Temperature-and-factors-affecting-1.pdf. 
  26. ↑ Martin, Richard M. (2004-04-08). Electronic Structure. Cambridge University Press. ISBN 978-0-521-53440-6. http://dx.doi.org/10.1017/cbo9780511805769. 
  27. ↑ Paier, J.; Marsman, M.; Hummer, K.; Kresse, G.; Gerber, I. C.; Ángyán, J. G. (2006-12-22). "Erratum: “Screened hybrid density functionals applied to solids” [J. Chem. Phys. 124, 154709 (2006)"]. The Journal of Chemical Physics 125 (24). doi:10.1063/1.2403866. ISSN 0021-9606. http://dx.doi.org/10.1063/1.2403866. 
  28. ↑ Jain, Manish; Chelikowsky, James R.; Louie, Steven G. (2011-11-18). "Reliability of Hybrid Functionals in Predicting Band Gaps". Physical Review Letters 107 (21). doi:10.1103/physrevlett.107.216806. ISSN 0031-9007. http://dx.doi.org/10.1103/physrevlett.107.216806. 
  29. ↑ Lee, SangMyeong; Kim, Won Bin; Lee, Jae Myeong; Kim, Hee Jung; Choi, Jin Hyuk; Jung, Hyun Suk (2021-09-08). "Oxide Passivation of Halide Perovskite Resistive Memory Device: A Strategy for Overcoming Endurance Problem". ACS Applied Materials & Interfaces 13 (37): 44577–44584. doi:10.1021/acsami.1c13210. ISSN 1944-8244. http://dx.doi.org/10.1021/acsami.1c13210. 
  30. ↑ Johnson, Austin; Gbaorun, F.; Ikyo, B.A. (2021-09-03). "First-Principles Study of (CsMA)NaSbX6 (MA= Methylammonium; X=Cl, Br, I) Organic-Inorganic Hybrid Double Perovskites For Optoelectronic Applications". http://dx.doi.org/10.21203/rs.3.rs-842623/v1. 
  31. ↑ Lee, Geon-Woo; Jun Choi, Yeon; Hwan Kim, Young; Hun Park, Byung; Choi, Song-Gue; Nazarian-Samani, Masoud; Kim, Kwang-Bum (February 2022). "Amorphization of germanium selenide driven by chemical interaction with carbon and realization of reversible conversion-alloying reaction for superior K-ion storage". Chemical Engineering Journal 430: 132995. doi:10.1016/j.cej.2021.132995. ISSN 1385-8947. http://dx.doi.org/10.1016/j.cej.2021.132995. 
  32. ↑ Bang, Jangwon; Moon, In Kyu; Choi, Keorock; Oh, Jungwoo (June 2022). "Phase-engineering terraced structure of edge-rich α-Mo2C for efficient hydrogen evolution reaction". Materials Today Energy 26: 100981. doi:10.1016/j.mtener.2022.100981. ISSN 2468-6069. http://dx.doi.org/10.1016/j.mtener.2022.100981. 
  33. ↑ Salman, Mohammad Bany; Park, Minkyu; Banisalman, Mosab Jaser (2023-05-29). "Revealing the Effects of Strain and Alloying on Primary Irradiation Defects Evolution in Tantalum Through Atomistic Simulations" (in en). Metals and Materials International. doi:10.1007/s12540-023-01459-x. ISSN 2005-4149. https://doi.org/10.1007/s12540-023-01459-x. 
  34. ↑ Bany Salman, Mohammad; Park, Minkyu; Banisalman, Mosab Jaser (January 2023). "Atomistic Study for the Tantalum and Tantalum–Tungsten Alloy Threshold Displacement Energy under Local Strain" (in en). International Journal of Molecular Sciences 24 (4): 3289. doi:10.3390/ijms24043289. ISSN 1422-0067. https://www.mdpi.com/1422-0067/24/4/3289. 
  35. ↑ Bany Salman, Mohammad; Park, Minkyu; Banisalman, Mosab Jaser (June 2022). "A Molecular Dynamics Study of Tungsten’s Interstitial Dislocation Loops Formation Induced by Irradiation under Local Strain" (in en). Solids 3 (2): 219–230. doi:10.3390/solids3020015. ISSN 2673-6497. https://www.mdpi.com/2673-6497/3/2/15. 
  36. ↑ Sengupta, A. (April 2022). "First principles design of 2 dimensional Nickel dichalcogenide Janus materials NiXY (X,Y = S,Se,Te)". Computational Materials Science 206: 111278. doi:10.1016/j.commatsci.2022.111278. ISSN 0927-0256. http://dx.doi.org/10.1016/j.commatsci.2022.111278. 
  37. ↑ Sengupta, Amretashis (2021-05-19). "An ab-initio study of 2 dimensional metal (Cu, Ag) - 1T’ ReS2 van der Waals heterostructure". 2021 Devices for Integrated Circuit (DevIC) (IEEE). doi:10.1109/devic50843.2021.9455899. http://dx.doi.org/10.1109/devic50843.2021.9455899. 
  38. ↑ Mishra, Prashant Kumar; Viji, Priya; Dobhal, Rachit; Sengupta, Amretashis; Rini, E.G.; Sen, Somaditya (February 2022). "Defects assisted photosensing and dye degradation of Ni/Ga co-doped ZnO: A theory added experimental investigation". Journal of Alloys and Compounds 893: 162229. doi:10.1016/j.jallcom.2021.162229. ISSN 0925-8388. http://dx.doi.org/10.1016/j.jallcom.2021.162229. 
  39. ↑ Enkhtuvshin, Enkhbayar; Kim, Kang Min; Kim, Young-Kwang; Mihn, Sungwook; Kim, So Jung; Jung, Sun Young; Thu Thao, Nguyen Thi; Ali, Ghulam et al. (2021). "Stabilizing oxygen intermediates on redox-flexible active sites in multimetallic Ni–Fe–Al–Co layered double hydroxide anodes for excellent alkaline and seawater electrolysis". Journal of Materials Chemistry A 9 (48): 27332–27346. doi:10.1039/d1ta07126b. ISSN 2050-7488. http://dx.doi.org/10.1039/d1ta07126b. 
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