Next-Generation Experimentation with Self-Driving Laboratories

Cited by (427)

Items 101–200 of 427

1. Brokering between tenants for an international materials acceleration platform
   Monika Vogler, Jonas Busk, Hamidreza Hajiyani, Peter Bjørn Jørgensen, Nehzat Safaei, Ivano E. Castelli, Francisco Ramirez, Johan Martin Carlsson, Giovanni Pizzi, Simon Clark et al. · 2023 · Matter
2. Superlative mechanical energy absorbing efficiency discovered through self-driving lab-human partnership
   Kelsey L. Snapp, Benjamin Verdier, Aldair E. Gongora, Samuel Silverman, Adedire D. Adesiji, Elise Feng‐i Morgan, Timothy J. Lawton, Emily Whiting, Keith A. Brown · 2024 · Nature Communications
3. Machine Learning for Accelerating Energy Materials Discovery: Bridging Quantum Accuracy with Computational Efficiency
   Kwang S. Kim · 2025 · Advanced Energy Materials
4. What is missing in autonomous discovery: open challenges for the community
   Phillip M. Maffettone, Pascal Friederich, Sterling G. Baird, Ben J. Blaiszik, Keith A. Brown, Stuart I. Campbell, Orion Cohen, Rebecca L. Davis, Ian T Foster, Navid Haghmoradi et al. · 2023 · Digital Discovery
5. Steering towards safe self-driving laboratories
   Shi Xuan Leong, Caleb E. Griesbach, Rui Zhang, Kourosh Darvish, Yuchi Zhao, Abhijoy Mandal, Yushi Zou, Han Hao, Varinia Bernales, Alán Aspuru‐Guzik · 2025 · Nature Reviews Chemistry
6. An Automated Scanning Transmission Electron Microscope Guided by Sparse Data Analytics
   Matthew J. Olszta, Derek Hopkins, Kevin R. Fiedler, Marjolein T. Oostrom, Sarah M. Akers, Steven R. Spurgeon · 2022 · Microscopy and Microanalysis
7. Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence
   Jonathan Zaslavsky, Pauric Bannigan, Christine J. Allen · 2023 · Expert Opinion on Drug Delivery
8. Role of AI in experimental materials science
   M. Abolhasani, Keith A. Brown, G. Editors · 2023 · MRS bulletin
9. Data-Driven Design and Autonomous Experimentation in Soft and Biological Materials Engineering
   Andrew L. Ferguson, Keith A. Brown · 2022 · Annual Review of Chemical and Biomolecular Engineering
10. Multi-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations
    Nickolas Gantzler, Aryan Deshwal, Janardhan Rao Doppa, Cory M. Simon · 2023 · Digital Discovery
11. Advances in materials informatics: a review
    Dawn Sivan, K. Satheesh Kumar, Aziman Abdullah, Veena Raj, Izan Izwan Misnon, Seeram Ramakrishna, Rajan Jose · 2024 · Journal of Materials Science
12. High-throughput discovery of chemical structure-polarity relationships combining automation and machine-learning techniques
    Hao Jun Xu, Jinglong Lin, Qianyi Liu, Yuntian Chen, Jianning Zhang, Yang Yang, Michael C. Young, Yan Xu, Dongxiao Zhang, Fanyang Mo · 2022 · Chem
13. 100th Anniversary of Macromolecular Science Viewpoint: Data-Driven Protein Design
    Andrew L. Ferguson, Rama Ranganathan · 2021 · ACS Macro Letters
14. When robotics met fluidics
    Junjie Zhong, Jason Riordon, Tony Wu, Harrison S. Edwards, Aaron R. Wheeler, Keith Pardee, Alán Aspuru‐Guzik, David A. Sinton · 2019 · Lab on a Chip
15. Scalable and automated CRISPR-based strain engineering using droplet microfluidics
    Kosuke Iwai, Maren Wehrs, Megan E. Garber, Jess M. Sustarich, Lauren Washburn, Zachary Costello, Peter W. Kim, David Ando, William R. Gaillard, Nathan J. Hillson et al. · 2022 · Microsystems & Nanoengineering
16. Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization
    Andrea Angulo, Lankun Yang, Eray S. Aydil, Miguel Antonio Modestino · 2021 · Digital Discovery
17. Unveiling dominant recombination loss in perovskite solar cells with a XGBoost-based machine learning approach
    Basir Akbar, Hilal Tayara, Kil To Chong · 2024 · iScience
18. Golem: an algorithm for robust experiment and process optimization
    Matteo Aldeghi, Florian Häse, Riley J. Hickman, Isaac Tamblyn, Alán Aspuru‐Guzik · 2021 · Chemical Science
19. A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles
    Fan Gao, Huiji Li, Zhilong Chen, Yunai Yi, Shihao Nie, Zihao Cheng, Zeming Liu, Yuanfang Guo, Shumin Liu, Qizhen Qin et al. · 2025 · Nature Communications
20. Artificial intelligence for synthetic biology
    Mohammed Eslami, Aaron Adler, Rajmonda S. Caceres, Joshua G. Dunn, Nancy Kelley‐Loughnane, Vanessa A. Varaljay, Héctor García Martín · 2022 · Communications of the ACM
21. Equipping data-driven experiment planning for Self-driving Laboratories with semantic memory: case studies of transfer learning in chemical reaction optimization
    Riley J. Hickman, Jurğis Ruža, Hermann Tribukait, Loı̈c M. Roch, Alberto García-Durán · 2023 · Reaction Chemistry & Engineering
22. Advancing perovskite solar cell commercialization: Bridging materials, vacuum deposition, and AI-assisted automation
    Zhihao Xu, Sang‐Hyun Chin, Bo‐In Park, Yuan Meng, Sunok Kim, Sangmoon Han, Yimeng Li, Dong‐Hwan Kim, Beom-Soo Kim, Jin‐Wook Lee et al. · 2024 · Next Materials
23. Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning.
    Li-Tao Chen, Bingxu Wang, Wen-Tao Zhang, Shisheng Zheng, Zhe-Feng Chen, Ming-Zheng Zhang, Cheng Dong, Feng Pan, Shun-Ning Li · 2024 · Journal of the American Chemical Society
24. Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery
    Lina Tang, Puiki Leung, Qian Xu, Cristina Flox · 2024 · ChemElectroChem
25. Recent Advances in Molybdenum Disulfide (MoS 2 ) and MXene‐based Heterostructures for Photovoltaic and Water Splitting Applications: A Review
    Peter Chidiebere Obodo, Kingsley Onyebuchi Obodo, Uyiosa Osagie Aigbe, Victor Sunday Aigbodion · 2025 · ChemistrySelect
26. Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes
    Oliver Tobias Schilter, Daniel Pacheco Gutiérrez, Linnea M. Folkmann, Alessandro Castrogiovanni, Alberto García-Durán, Federico Zipoli, Loı̈c M. Roch, Teodoro Laino · 2024 · Chemical Science
27. Accelerated discovery of perovskite solid solutions through automated materials synthesis and characterization
    Mojan Omidvar, Hangfeng Zhang, Achintha Avin Ihalage, Theo Saunders, Henry Giddens, Derek Michael Forrester, Sajad Haq, Yang Hao · 2024 · Nature Communications
28. Automated stopped-flow library synthesis for rapid optimisation and machine learning directed experimentation
    Claudio Avila, Carlo Cassani, Thierry Kogej, Javier Mazuela, Sunil Sarda, Adam D. Clayton, Michael Kossenjans, Clive P. Green, Richard A. Bourne · 2022 · Chemical Science
29. Natural Language Processing Challenges and Issues: A Literature Review
    Abdul Ahad Abro, Mir Sajjad Hussain Talpur, Awais Khan Jumani · 2022 · GAZI UNIVERSITY JOURNAL OF SCIENCE
30. Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug
    Zeqing Bao, Fion Yung, Riley J. Hickman, Alán Aspuru‐Guzik, Pauric Bannigan, Christine J. Allen · 2023 · Drug Delivery and Translational Research
31. Chemspyd : an open-source python interface for Chemspeed robotic chemistry and materials platforms
    Martin T. Seifrid, Felix Strieth‐Kalthoff, Mohammad Haddadnia, Tony Wu, Emre Alca, Leticia Bodo, Sebastian Arellano-Rubach, Naruki Yoshikawa, Marta Skreta, Rachel Keunen et al. · 2024 · Digital Discovery
32. A Recommender System for Inverse Design of Polycarbonates and Polyesters
    Nathaniel H. Park, Dmitry Yu. Zubarev, James L. Hedrick, Vivien Kiyek, Christiaan H. W. A. Corbet, Simon Lottier · 2020 · Macromolecules
33. Standardization and Control of Grignard Reactions in a Universal Chemical Synthesis Machine using online NMR
    Martin Bornemann‐Pfeiffer, Jakob B. Wolf, Klas Meyer, Simon Kern, Davide Angelone, Artem I. Leonov, Leroy Cronin, Franziska Emmerling · 2021 · Angewandte Chemie International Edition
34. Advancing Organic Chemistry Using High‐Throughput Experimentation
    Reem Nsouli, Gaurav Galiyan, Laura K. G. Ackerman · 2025 · Angewandte Chemie International Edition
35. Designing materials acceleration platforms for heterogeneous CO2 photo(thermal)catalysis
    Andrew Wang, Carlota Bozal‐Ginesta, Sai Govind Hari Kumar, Alán Aspuru‐Guzik, Geoffrey Alan Ozin · 2023 · Matter
36. A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis
    Faisal Al‐Akayleh, Ahmed S.A. Ali Agha, Rami A. Abdel‐Rahem, Mayyas M. Al-Remawi · 2024 · Tenside Surfactants Detergents
37. Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes
    Alexey O. Sanin, Jackson K. Flowers, Tobias Horst Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein · 2025 · Advanced Energy Materials
38. Re‐imagining Priorities for Chemistry: A Central Science for “Freedom from Fear and Want”
    Stephen A. Matlin, Alain Krief, Henning Hopf, Goverdhan Mehta · 2021 · Angewandte Chemie International Edition
39. Recent Developments in Reactor Automation for Multistep Chemical Synthesis
    Adam D. Clayton · 2023 · Chemistry - Methods
40. Systematic study of TiO 2 /ZnO mixed metal oxides for CO 2 photoreduction
    Warren Athol Thompson, Alberto Olivo, Danny Zanardo, Giuseppe Cruciani, Federica Menegazzo, Michela Signoretto, M. Mercedes Maroto‐Valer · 2019 · RSC Advances
41. DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space
    Rangsiman Ketkaew, Sandra Luber · 2022 · Journal of Chemical Information and Modeling
42. Bayesian optimization of single-atom alloys and other bimetallics: efficient screening for alkane transformations, CO 2 reduction, and hydrogen evolution
    Gbolade O. Kayode, Avery F. Hill, M. M. Montemore · 2023 · Journal of Materials Chemistry A
43. Autonomous model-based experimental design for rapid reaction development
    Sebastian Knoll, Clemens Emanuel Jusner, Peter Sagmeister, Jason D. Williams, Christopher A. Hone, Martin Horn, Christian Oliver Kappe · 2022 · Reaction Chemistry & Engineering
44. The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?
    Pauric Bannigan, Riley J. Hickman, Alán Aspuru‐Guzik, Christine J. Allen · 2024 · Advanced Healthcare Materials
45. Ethical Artificial Intelligence in Chemical Research and Development: A Dual Advantage for Sustainability
    Erik Hermann, G. Hermann, Jean-Christophe Tremblay · 2021 · Science and Engineering Ethics
46. Machine Learning Assisted Design of Experiments for Solid State Electrolyte Lithium Aluminum Titanium Phosphate
    Yinghan Zhao, Nikolas Schiffmann, Arnd Koeppe, Nico Brandt, Ethel C. Bucharsky, Karl G. Schell, Michael A. Selzer, Britta Nestler · 2022 · Frontiers in Materials
47. Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops
    Jonathan T. Carter, John Feddema, D.B. Kothe, Rob Neely, J Pruet, Rick L. Stevens, Prasanna Balaprakash, Pete Beckman, Ian T Foster, Kamil A. Iskra et al. · 2023
48. Active meta-learning for predicting and selecting perovskite crystallization experiments
    Shekar, Venkateswaran, Nicholas, Gareth, Najeeb, Mansoor Ani, Zeile, Margaret, Yu, Vincent, Wang, Xiaorong, Slack, Dylan, Li, Zhi, Nega, Philip W., Chan, Emory M. et al. · 2022 · The Journal of Chemical Physics
49. An affordable platform for automated synthesis and electrochemical characterization
    Sergio Pablo‐García, A. Garcı́a, Gun Deniz Akkoc, Malcolm Sim, Yang Cao, Maxine Somers, Chance Hattrick, Naruki Yoshikawa, Dominik Dworschak, Han Hao et al. · 2024 · Device
50. Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation
    Riley J. Hickman, Gary Tom, Yunheng Zou, Matteo Aldeghi, Alán Aspuru‐Guzik · 2025 · Digital Discovery
51. Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials
    Maria A. Butakova, Andrey V. Chernov, Oleg O. Kartashov, Alexander Vladimirovich Soldatov · 2021 · Nanomaterials
52. Chemically-informed data-driven optimization (ChIDDO): leveraging physical models and Bayesian learning to accelerate chemical research
    Daniel Frey, Ju Hee Shin, Christopher Musco, Miguel Antonio Modestino · 2022 · Reaction Chemistry & Engineering
53. An AI-accelerated pathway for reproducible and stable halide perovskites
    Abigail R. Hering, Carolin M. Sutter‐Fella, Marina S. Leite · 2025 · Chemical Society Reviews
54. Scaling up nanoformulation manufacturing: A multi–case study linking target product profiles, critical quality attributes, and quality by design
    Shriraj B. Patel, Dhanashree Prakash Sanap · 2026 · OpenNano
55. Data Science in Chemical Engineering: Applications to Molecular Science
    Chowdhury M. Ashraf, Nisarg Joshi, David A. C. Beck, Jim Pfaendtner · 2021 · Annual Review of Chemical and Biomolecular Engineering
56. A Data-Driven Workflow for Nanomedicine Optimization Using Active Learning and Automated Experimentation
    Zeqing Bao, Frantz Le Dévédec, Steven Huynh · 2025 · Molecular Pharmaceutics
57. A Steady-State Algorithm for Solving Expensive Multiobjective Optimization Problems With Nonparallelizable Evaluations
    Kamrul Hasan Rahi, Hemant Kumar Singh, Tapabrata Ray · 2022 · IEEE Transactions on Evolutionary Computation
58. Transforming research laboratories with connected digital twins
    Simon D. Rihm, Jiaru Bai, Aleksandar Kondinski, Sebastian Mosbach, Jethro Akroyd, Markus Kraft · 2024 · Nexus
59. Cost-informed Bayesian reaction optimization
    Alexandre A. Schoepfer, Jan Weinreich, Rubén Laplaza, Jérôme Waser, Clémence Corminbœuf · 2024 · Digital Discovery
60. Toward self-driving laboratory 2.0 for chemistry and materials discovery
    Heeseung Lee, Hyuk Jun Yoo, Hye Su Jang, Byeongho Park, Yang Jeong Park, Sang Soo Han · 2026 · Materials Horizons
61. Bayesian optimization of electrochemical devices for electrons-to-molecules conversions: the case of pulsed CO 2 electroreduction
    Daniel Frey, Kenneth Charles Neyerlin, Miguel Antonio Modestino · 2022 · Reaction Chemistry & Engineering
62. Improved decision making with similarity based machine learning: applications in chemistry
    Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld · 2023 · Machine Learning Science and Technology
63. Critical Mineral Separations: Opportunities for Membrane Materials and Processes to Advance Sustainable Economies and Secure Supplies
    Laurianne Lair, Jonathan Aubuchon Ouimet, Molly Dougher, Bryan W. Boudouris, Alexander William Dowling, William A. Phillip · 2024 · Annual Review of Chemical and Biomolecular Engineering
64. Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective
    You-Hyun Kim, Hayoung Doo, Daeun Shin, Seo Yoon Lee, Yugyeong Roh, Seong‐Eun Park, Heejin Song, Yujin Jung, Hyuk Jun Yoo, Sang Soo Han et al. · 2025 · Computers & Chemical Engineering
65. Decentring the discoverer: how AI helps us rethink scientific discovery
    Elinor Clark, Donal Khosrowi · 2022 · Synthese
66. Robotically automated 3D printing and testing of thermoplastic material specimens
    Miguel Hernández‐del‐Valle, Christina Schenk, Lucía Echevarría-Pastrana, Burcu Özdemir, Enrique Dios-Lázaro, Jorge Ilarraza-Zuazo, De‐Yi Wang, Maciej Harańczyk · 2023 · Digital Discovery
67. Machine vision-based detections of transparent chemical vessels toward the safe automation of material synthesis
    Leslie Ching Ow Tiong, Hyuk Jun Yoo, Nayeon Kim, Chansoo Kim, Kwan‐Young Lee, Sang Soo Han, Dong-Hun Kim · 2024 · npj Computational Materials
68. From Absorption Spectra to Charge Transfer in Nanoaggregates of Oligomers with Machine Learning
    Loı̈c M. Roch, Semion K. Saikin, Florian Häse, Pascal Friederich, Randall H. Goldsmith, Salvador León, Alán Aspuru‐Guzik · 2020 · ACS Nano
69. Optimized Machine Learning for Autonomous Enzymatic Reaction Intensification in a Self‐Driving Lab
    Sebastian Putz, Niklas Teetz, Michael Abt, Pascal Jerono, Thomas Meurer, Matthias Franzreb · 2025 · Biotechnology and Bioengineering
70. Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery
    Rohan Chand Sahu, Sanchit Arora, Dinesh K. Kumar, Ashish Kumar Agrawal · 2025 · Med Research
71. MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research
    Keith A. Brown · 2022 · npj Computational Materials
72. AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics
    Lauren Takahashi, Micke Kuwahara, KEISUKE G. TAKAHASHI · 2025 · Chemical Science
73. A self-driving physical vapor deposition system making sample-specific decisions on the fly
    Yu Zheng, Connor Blake, Layla Mravac, Fengxue Zhang, Yuxin Chen, Shuolong Yang · 2025 · npj Computational Materials
74. Perspectives for artificial intelligence in bioprocess automation
    Laura M. Helleckes, Sebastian Putz, Kshitiz Gupta, Matthias Franzreb, Hector Garcia Martin · 2025 · Current Opinion in Biotechnology
75. How to Accelerate R&D and Optimize Experiment Planning with Machine Learning and Data Science
    Daniel Pacheco Gutiérrez, Linnea M. Folkmann, Hermann Tribukait, Loı̈c M. Roch · 2023 · CHIMIA International Journal for Chemistry
76. Advancing Rare-Earth (4 f ) and Actinide (5 f ) Separation through Machine Learning and Automated High-Throughput Experiments
    Logan J. Augustine, Yufei Wang, Sara L. Adelman, Enrique R. Batista, Stosh A. Kozimor, Danny Pérez, Joshua Schrier, Ping Xiong Yang · 2024 · ACS Sustainable Chemistry & Engineering
77. Quality by Computational Design: Harnessing AI to Advance Rational Drug Development
    Nannan Wang, Hao Zhong, Ping Xiong, Jinying Zhu, Defang Ouyang · 2025 · Advanced Drug Delivery Reviews
78. AI-Driven Polymeric Coatings: Strategies for Material Selection and Performance Evaluation in Structural Applications
    Min Ook Kim · 2025 · Polymers
79. Digital chemistry: navigating the confluence of computation and experimentation – definition, status quo, and future perspective
    Stefan Bräse · 2024 · Digital Discovery
80. Harnessing synthetic biology for sustainable industrial innovation: Advances, challenges, and future direction
    Emmanuel Chimeh Ezeako, Barine Innocent Nwiloh, Malachy Chigozie Odo, Vincent Eric Ozougwu · 2025 · Biochemical Engineering Journal
81. Artificial Intelligence Applied to the Prediction of Organic Materials
    Steven Bennett, Andrew Tarzia, Martijn A. Zwijnenburg, Kim E. Jelfs · 2020 · Royal Society of Chemistry eBooks
82. Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells
    Mohammad J. Eslamibidgoli, Fabian P. Tipp, Jenia Jitsev, Jasna Janković, Michael Hermann Eikerling, Kourosh Malek · 2021 · RSC Advances
83. An Autonomous Chemical Robot Discovers the Rules of Inorganic Coordination Chemistry without Prior Knowledge
    Luzian Porwol, Daniel J. Kowalski, Alon Henson, De‐Liang Long, Nicola L. Bell, Leroy Cronin · 2020 · Angewandte Chemie
84. Bias Free Multiobjective Active Learning for Materials Design and Discovery
    Kevin Maik Jablonka, Giriprasad Melpatti Jothiappan, Shefang Wang, Berend Smit, Brian Yoo · 2020 · ChemRxiv
85. Artificial Intelligence in Accelerating Materials Discovery: Opportunities and Challenges
    Avadhesh Kumar Yadav · 2026 · ChemistrySelect
86. Socialni odnosi, animizem, antropomorfizem in interakcija z UI
    Nenad Čuš Babić · 2022
87. Reaction Space Charting as a Tool in Organic Chemistry Research and Development
    Eloy Lozano Baró, Pau Nadal Rodríguez, Jordi Juárez‐Jiménez, Ouldouz Ghashghaei, Rodolfo Lavilla · 2024 · Advanced Synthesis & Catalysis
88. On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform
    Zhehong Ai, Longhan Zhang, Yangguan Chen, Yifan Long, Boyuan Li, Qingyu Dong, Yueming Wang, Jing Jiang · 2024 · ACS Sensors
89. The ADePT framework for assessing autonomous laboratory robotics
    Pablo José Salazar Villacis, Brahim Benyahia · 2026 · Communications Chemistry
90. A Bag of Tricks for Automated De Novo Design of Molecules with the Desired Properties: Application to EGFR Inhibitor Discovery
    Maria V. Korshunova, Niles Huang, Stephen J. Capuzzi, Dmytro S. Radchenko, Olena Savych, Yuriy S. Moroz, Carrow I. Wells, Timothy Mark Willson, Alexander Tropsha, Olexandr Isayev · 2021 · ChemRxiv
91. Potential of Machine Learning Algorithms in Material Science: Predictions in Design, Properties, and Applications of Novel Functional Materials
    Purvi Bhatt, Neha Singh, Sumit Chaudhary · 2023 · Springer eBooks
92. Benchmarking Strategies of Sustainable Process Chemistry Development: Human-Based, Machine Learning, and Quantum Mechanics
    A. Filipa Almeida, Sofia Branco, Luísa C. R. Carvalho, André Raposo Moreira Dias, Emília P. T. Leitão, Rui M. S. Loureiro, Susana Dias Lucas, Ricardo F. Mendonça, Rudi Oliveira, Inês L. D. Rocha et al. · 2024 · Organic Process Research & Development
93. Next generation fatigue crack growth experiments of aerospace materials
    Tobias Strohmann, David Melching, Florian Paysan, Eric Dietrich, Guillermo Carlos Requena, Eric Breitbarth · 2024 · Scientific Reports
94. Artificial intelligence driven laser parameter search: Inverse design of photonic surfaces using greedy surrogate-based optimization
    Luka Grbčić, Minok Park, Juliane Müller, Vassilia Zorba, Wibe Albert de Jong · 2025 · Engineering Applications of Artificial Intelligence
95. Materials discovery in combinatorial and high-throughput synthesis and processing: A new Frontier for SPM
    Boris N. Slautin, Yongtao Liu, Kamyar Barakati, Yu Liu, Reece Emery, Seungbum Hong, Astita Dubey, Vladimir V. Shvartsman, Doru Constantin Lupascu, Sheryl L. Sanchez et al. · 2025 · Applied Physics Reviews
96. Artificial intelligence in materials science and chemistry: Past, present and future trajectories
    Etinosa Osaro, Nell Karpinski, Sonia Alornyo, Joshua O. Ighalo · 2025 · Materials Today Chemistry
97. Beyond Combinatorial Materials Science: The 100 Prisoners Problem
    James Elliott Fowler, Matthew A. Kottwitz, Nathaniel Trask, Rémi Dingreville · 2024 · Integrating materials and manufacturing innovation
98. Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing
    Haochen Pan, Ryan Chard, Sicheng Zhou, Alok Kamatar, Rafael Vescovi, Valérie Hayot‐Sasson, André Bauer, Maxime Gonthier, Kyle Chard, Ian T Foster · 2024 · International Conference for High Performance Computing, Networking, Storage and Analysis (SC)
99. An Integrated Method of Bayesian Optimization and D-Optimal Design for Chemical Experiment Optimization
    Xinye Huang, Shuyuan Zhang, Haoran Li, Tong Qiu · 2022 · Processes
100. Olympus, enhanced: benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science
     Riley J. Hickman, Priyansh Parakh, Austin Cheng, Qianxiang Ai, Joshua Schrier, Matteo Aldeghi, Alán Aspuru‐Guzik · 2023 · ChemRxiv