The evolution of Materials Acceleration Platforms: toward the laboratory of the future with AMANDA
Source abstract
Abstract
Abstract The development of complex functional materials poses a multi-objective optimization problem in a large multi-dimensional parameter space. Solving it requires reproducible, user-independent laboratory work and intelligent preselection of experiments. However, experimental materials science is a field where manual routines are still predominant, although other domains like pharmacy or chemistry have long used robotics and automation. As the number of publications on Materials Acceleration Platforms (MAPs) increases steadily, we review selected systems and fit them into the stages of a general material development process to examine the evolution of MAPs. Subsequently, we present our approach to laboratory automation in materials science. We introduce AMANDA (Autonomous Materials and Device Application Platform - www.amanda-platform.com), a generic platform for distributed materials research comprising a self-developed software backbone and several MAPs. One of them, LineOne (L1), is specifically designed to produce and characterize solution-processed thin-film devices like organic solar cells (OSC). It is designed to perform precise closed-loop screenings of up to 272 device variations per day yet allows further upscaling. Each individual solar cell is fully characterized, and all process steps are comprehensively documented. We want to demonstrate the capabilities of AMANDA L1 with OSCs based on PM6:Y6 with 13.7% efficiency when processed in air. Further, we discuss challenges and opportunities of highly automated research platforms and elaborate on the future integration of additional techniques, methods and algorithms in order to advance to fully autonomous self-optimizing systems—a paradigm shift in functional materials development leading to the laboratory of the future.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Organic Electronics and Photovoltaics · Perovskite Materials and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (59)
Show source-provided references
1. Hitomi K (1994) Automation — its concept and a short history. Technovation 14:121–128. https://doi.org/10.1016/0166-4972(94)90101-510.1016/0166-4972(94)90101-5 ↗
2. Popkova EG, Ragulina YV, Bogoviz AV (2018) Industry 4.0: Industrial revolution of the 21st Century. Springer, Cham
3. Olsen K (2012) The first 110 years of laboratory automation: technologies, applications, and the creative scientist. J Lab Autom 17:469–480. https://doi.org/10.1177/221106821245563110.1177/2211068212455631 ↗
4. Felder RA (2006) The clinical chemist: masahide sasaki, MD, PhD (August 27, 1933–September 23, 2005). Clin Chem 52:791–792. https://doi.org/10.1373/clinchem.2006.06768610.1373/clinchem.2006.067686 ↗
5. Sasaki M (1993) A fully automated clinical laboratory. Chemom Intell Lab Syst 21:159–168. https://doi.org/10.1373/clinchem.2006.06768610.1373/clinchem.2006.067686 ↗
6. Hawker CD (2007) Laboratory automation: total and subtotal. Clin Lab Med 27:749–70. https://doi.org/10.1016/j.cll.2007.07.01010.1016/j.cll.2007.07.010 ↗
7. National Research Council (US) Office of Special Projects (2014) Issues for Science and engineering researchers in the digital age. National Academies Press, Washington. https://doi.org/10.17226/1010010.17226/10100 ↗
8. National Science And Technology (2011) Materials Genome Initiative for Global Competitiveness. Books LLC
9. Aspuru-Guzik A, Tribukait-Vasconcelos H (2018) Materials acceleration platform accelerating advanced energy materials discovery by integrating high-throughput methods with artificial intelligence report of the clean energy materials innovation challenge Expert Workshop. Mission Innovation
10. Nikolaev P, Hooper D, Webber F et al (2016) Autonomy in materials research: a case study in carbon nanotube growth. Npj Comput Mater. https://doi.org/10.1038/npjcompumats.2016.3110.1038/npjcompumats.2016.31 ↗
11. Chan EM, Xu C, Mao AW et al (2010) Reproducible, high-throughput synthesis of colloidal nanocrystals for optimization in multidimensional parameter space. Nano Lett 10:1874–1885. https://doi.org/10.1021/nl100669s10.1021/nl100669s ↗
12. Walter H, Basset G, Beierlein T et al (2010) Combinatorial approach for fast screening of functional materials. J Polym Sci, Part B: Polym Phys 48:1587–1593. https://doi.org/10.1002/polb.2196110.1002/polb.21961 ↗
13. Burger B, Maffettone PM, Gusev VV et al (2020) A mobile robotic chemist. Nature 583:237–241. https://doi.org/10.1038/s41586-020-2442-210.1038/s41586-020-2442-2 ↗
14. Pendleton IM, Cattabriga G, Li Z et al (2019) Experiment specification, capture and laboratory automation technology (ESCALATE): a software pipeline for automated chemical experimentation and data management. MRS Commun 9:846–859. https://doi.org/10.1038/533452a10.1038/533452a ↗
15. MacLeod BP, Parlane FGL, Morrissey TD et al (2020) Self-driving laboratory for accelerated discovery of thin-film materials. Sci Adv 6:eaaz8867. https://doi.org/10.1126/sciadv.aaz886710.1126/sciadv.aaz8867 ↗
16. Zunger A (2018) Inverse design in search of materials with target functionalities. Nat Rev Chem. https://doi.org/10.1038/s41570-018-012110.1038/s41570-018-0121 ↗
17. Baker M (2016) 1,500 scientists lift the lid on reproducibility. Nature 533:452–454. https://doi.org/10.1038/533452a10.1038/533452a ↗
18. Paulsen F, Waschke J (2018) Sobotta tables of muscles, joints and nerves, english/Latin: Tables to 16th Ed. of the Sobotta Atlas. Fischer Gustav Verlag GmbH & Company KG
19. Sobotta J, Hersey Thomas W (2018) Atlas and text-book of human anatomy: bones, ligaments, joints, and muscles. Franklin Classics
20. Darling SB, You F (2013) The case for organic photovoltaics. RSC Adv 3:17633. https://doi.org/10.1039/c3ra42989j10.1039/c3ra42989j ↗
21. Yu G, Gao J, Hummelen JC et al (1995) Polymer photovoltaic cells: enhanced efficiencies via a network of internal donor-acceptor heterojunctions. Science 270:1789–1791. https://doi.org/10.1126/science.270.5243.178910.1126/science.270.5243.1789 ↗
22. Wadsworth A, Moser M, Marks A et al (2019) Critical review of the molecular design progress in non-fullerene electron acceptors towards commercially viable organic solar cells. Chem Soc Rev 48:1596–1625. https://doi.org/10.1039/C7CS00892A10.1039/C7CS00892A ↗
23. Zhang M, Guo X, Ma W et al (2015) A large-bandgap conjugated polymer for versatile photovoltaic applications with high performance. Adv Mater 27:4655–4660. https://doi.org/10.1002/adma.20150211010.1002/adma.201502110 ↗
24. Fan B, Zhang D, Li M et al (2019) Achieving over 16% efficiency for single-junction organic solar cells. Sci China Chem 62:746–752. https://doi.org/10.1007/s11426-019-9457-510.1007/s11426-019-9457-5 ↗
25. Yuan J, Zhang Y, Zhou L et al (2019) Single-junction organic solar cell with over 15% efficiency using fused-ring acceptor with electron-deficient core. Joule 3:1140–1151. https://doi.org/10.1016/j.joule.2019.01.00410.1016/j.joule.2019.01.004 ↗
26. Sun C, Pan F, Qiu B et al (2020) D-A copolymer donor based on bithienyl benzodithiophene d-unit and monoalkoxy bifluoroquinoxaline a-unit for high-performance polymer solar cells. Chem Mater 32:3254–3261. https://doi.org/10.1021/acs.chemmater.0c0045910.1021/acs.chemmater.0c00459 ↗
27. Ma R, Liu T, Luo Z et al (2020) Improving open-circuit voltage by a chlorinated polymer donor endows binary organic solar cells efficiencies over 17%. Sci China Chem 63:325–330. https://doi.org/10.1007/s11426-019-9669-310.1007/s11426-019-9669-3 ↗
28. Cai F, Zhu C, Yuan J et al (2020) Efficient organic solar cells based on a new “Y-series” non-fullerene acceptor with an asymmetric electron-deficient-core. Chem Commun 56:4340–4343. https://doi.org/10.1039/C9CC10076H10.1039/C9CC10076H ↗
29. Liu Q, Jiang Y, Jin K et al (2020) 18% Efficiency organic solar cells. Sci Bull 65:272–275. https://doi.org/10.1016/j.scib.2020.01.00110.1016/j.scib.2020.01.001 ↗
30. Lin Y, Nugraha MI, Firdaus Y et al (2020) A simple n-dopant derived from diquat boosts the efficiency of organic solar cells to 18.3%. ACS Energy Lett 5:3663–3671. https://doi.org/10.1021/acsenergylett.0c0194910.1021/acsenergylett.0c01949 ↗
31. Meredith P, Li W, Armin A (2020) Nonfullerene acceptors: a renaissance in organic photovoltaics? Adv Energy Mater 10:2001788. https://doi.org/10.1002/aenm.20200178810.1002/aenm.202001788 ↗
32. Pan M-A, Lau T-K, Tang Y et al (2019) 16.7%-efficiency ternary blended organic photovoltaic cells with PCBM as the acceptor additive to increase the open-circuit voltage and phase purity. J Mater Chem A 7:20713–20722. https://doi.org/10.1039/C9TA06929A10.1039/C9TA06929A ↗
33. Yan T, Ge J, Lei T et al (2019) 16.55% efficiency ternary organic solar cells enabled by incorporating a small molecular donor. J Mater Chem A 7:25894–25899. https://doi.org/10.1039/C9TA10145D10.1039/C9TA10145D ↗
34. Yang T, Ma R, Cheng H et al (2020) A compatible polymer acceptor enables efficient and stable organic solar cells as a solid additive. J Mater Chem A 8:17706–17712. https://doi.org/10.1039/D0TA06146H10.1039/D0TA06146H ↗
35. Gasparini N, Paleti SHK, Bertrandie J et al (2020) Exploiting ternary blends for improved photostability in high-efficiency organic solar cells. ACS Energy Lett 5:1371–1379. https://doi.org/10.1021/acsenergylett.0c0060410.1021/acsenergylett.0c00604 ↗
36. Inganäs O (2018) Organic photovoltaics over three decades. Adv Mater 30:e1800388. https://doi.org/10.1002/adma.20180038810.1002/adma.201800388 ↗
37. Sun R, Wu Q, Guo J et al (2020) A layer-by-layer architecture for printable organic solar cells overcoming the scaling lag of module efficiency. Joule 4:407–419. https://doi.org/10.1016/j.joule.2019.12.00410.1016/j.joule.2019.12.004 ↗
38. Bohacek RS, McMartin C, Guida WC (1996) The art and practice of structure-based drug design: a molecular modeling perspective. Med Res Rev 16:3–50. https://doi.org/10.1002/(SICI)1098-1128(199601)16:1%3c3::AID-MED1%3e3.0.CO;2-610.1002/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6 ↗
39. Yener KA, Aslihan Yener K, Vandiver PB (1993) Tin processing at göltepe, an early bronze age site in Anatolia. Am J Archaeol 97:207. https://doi.org/10.2307/50565710.2307/505657 ↗
40. W Zulehner (1989) Czochralski Growth of Silicon. Semiconductor Silicon, Springer, Heidelberg, https://doi.org/10.1007/978-3-642-74723-6_110.1007/978-3-642-74723-6_1 ↗
41. Merrifield RB, Stewart JM, Jernberg N (1966) Instrument for automated synthesis of peptides. Anal Chem 38:1905–1914. https://doi.org/10.1021/ac50155a05710.1021/ac50155a057 ↗
42. Lindsey JS (1992) A retrospective on the automation of laboratory synthetic chemistry. Chemom Intell Lab Syst 17:15–45. https://doi.org/10.1016/0169-7439(92)90025-B10.1016/0169-7439(92)90025-B ↗
43. King RD, Rowland J, Oliver SG et al (2009) The automation of science. Science 324:85–89. https://doi.org/10.1126/science.116562010.1126/science.1165620 ↗
44. Sparkes A, Aubrey W, Byrne E et al (2010) Towards robot scientists for autonomous scientific discovery. Autom Exp 2:1. https://doi.org/10.1186/1759-4499-2-110.1186/1759-4499-2-1 ↗
45. Rao R, Pierce N, Liptak D et al (2013) Revealing the impact of catalyst phase transition on carbon nanotube growth by in situ Raman spectroscopy. ACS Nano 7:1100–1107. https://doi.org/10.1021/nn304064u10.1021/nn304064u ↗
46. Nikolaev P, Hooper D, Perea-López N et al (2014) Discovery of wall-selective carbon nanotube growth conditions via automated experimentation. ACS Nano 8:10214–10222. https://doi.org/10.1021/nn503347a10.1021/nn503347a ↗
47. Li J, Ballmer SG, Gillis EP et al (2015) Synthesis of many different types of organic small molecules using one automated process. Science 347:1221–1226. https://doi.org/10.1126/science.aaa541410.1126/science.aaa5414 ↗
48. Steiner S, Wolf J, Glatzel S et al (2019) Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363:eaav2211. https://doi.org/10.1126/science.aav221110.1126/science.aav2211 ↗
49. Chen S, Hou Y, Chen H et al (2018) Exploring the stability of novel wide bandgap perovskites by a robot based high throughput approach. Adv Energy Mater 8:1701543. https://doi.org/10.1002/aenm.20170154310.1002/aenm.201701543 ↗
50. Xie C, Tang X, Berlinghof M et al (2018) Robot-based high-throughput engineering of alcoholic polymer: fullerene nanoparticle inks for an eco-friendly processing of organic solar cells. ACS Appl Mater Interfaces 10:23225–23234. https://doi.org/10.1021/acsami.8b0362110.1021/acsami.8b03621 ↗
51. Roch LM, Häse F, Kreisbeck C et al (2018) ChemOS: orchestrating autonomous experimentation. Science Robotics 3:eaat5559. https://doi.org/10.1126/scirobotics.aat555910.1126/scirobotics.aat5559 ↗
52. Langner S, Häse F, Perea JD et al (2020) Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems. Adv Mater 32:e190780110.1002/adma.201907801 ↗
53. González-R PL, Framinan JM, Pierreval H (2012) Token-based pull production control systems: an introductory overview. J Intell Manuf 23:5–22. https://doi.org/10.1007/s10845-011-0534-410.1007/s10845-011-0534-4 ↗
54. Wilkinson MD, Dumontier M, Jan Aalbersberg I et al (2019) Addendum: The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 6:6. https://doi.org/10.1038/s41597-019-0009-610.1038/s41597-019-0009-6 ↗
55. American National Standards Institute (2004) ANSI/SBS 1–2004: Footprint Dimensions
56. WS Cleveland E Grosse WM Shyu (2017) Local Regression Models. Statistical Models in S, Routledge, New York, 309 376, https://doi.org/10.1201/9780203738535-810.1201/9780203738535-8 ↗
57. Zhu L, Zhang M, Zhou G et al (2020) Non-fullerene acceptors: efficient organic solar cell with 16 88% efficiency enabled by refined acceptor crystallization and morphology with improved charge Transfer and Transport Properties (Adv. Energy Mater. 18/2020). Adv Energy Mater 10:2070083. https://doi.org/10.1002/aenm.20207008310.1002/aenm.202070083 ↗
58. Du X, Lüer L, Heumüller T et al (2021) Elucidating the full potential of OPV materials utilizing a high - throughput robot - based platform and machine learning. Joule 5:495–506. https://doi.org/10.1016/j.joule.2020.12.01310.1016/j.joule.2020.12.013 ↗
59. Bär H, Hochstrasser R, Papenfub B (2012) SiLA: Basic standards for rapid integration in laboratory automation. J Lab Autom 17:86–95. https://doi.org/10.1177/221106821142455010.1177/2211068211424550 ↗