PhaseXplorer Creates High-Dimensional Phase Diagrams with Closed-Loop Active Learning Article Swipe
Stef A. H. Jansen
,
Lasse Sander Dreyer
,
Jule van Basten
,
Yihan Yao
,
Daniel E. Otzen
,
Tom F. A. de Greef
,
Tuomas P. J. Knowles
,
E. W. Meijer
,
Nadia A. Erkamp
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acsnano.5c07268
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acsnano.5c07268
Phase separation fundamentally governs material properties and cellular function on multiple organizational scales. Conventional approaches to studying this nonlinear phenomenon necessitate resource-intensive experiments. As such, investigations were limited to low-dimensional space. We present PhaseXplorer, a platform that combines microfluidics, microscopy, and machine learning to efficiently study phase separation systems. PhaseXplorer autonomously designs, generates, and analyzes samples in a closed-loop active learning workflow until an accurate phase diagram is obtained. Using an acquisition function that balances exploration and exploitation, all the phase boundaries are located with minimal sampling. A convolutional neural network executes real-time image recognition to identify microfluidic droplets and phase separation within them in less than 1 ms per droplet. PhaseXplorer standardizes analysis across experiments and does not require calibration nor extensive postexperiment analysis. We demonstrate PhaseXplorer's capabilities using a poly rA model system by creating a four-dimensional phase diagram 100 times faster than traditional methods while simultaneously consuming 10,000 times less material.
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2025Year of publication
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2025-11-03Full publication date if available
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Stef A. H. Jansen, Lasse Sander Dreyer, Jule van Basten, Yihan Yao, Daniel E. Otzen, Tom F. A. de Greef, Tuomas P. J. Knowles, E. W. Meijer, Nadia A. ErkampList of authors in order
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hybridOpen access status per OpenAlex
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| referenced_works_count | 31 |
| abstract_inverted_index.1 | 107 |
| abstract_inverted_index.A | 87 |
| abstract_inverted_index.a | 34, 57, 130, 137 |
| abstract_inverted_index.As | 23 |
| abstract_inverted_index.We | 31, 125 |
| abstract_inverted_index.an | 63, 70 |
| abstract_inverted_index.by | 135 |
| abstract_inverted_index.in | 56, 104 |
| abstract_inverted_index.is | 67 |
| abstract_inverted_index.ms | 108 |
| abstract_inverted_index.on | 9 |
| abstract_inverted_index.rA | 132 |
| abstract_inverted_index.to | 15, 28, 43, 95 |
| abstract_inverted_index.100 | 141 |
| abstract_inverted_index.all | 78 |
| abstract_inverted_index.and | 6, 40, 53, 76, 99, 116 |
| abstract_inverted_index.are | 82 |
| abstract_inverted_index.nor | 121 |
| abstract_inverted_index.not | 118 |
| abstract_inverted_index.per | 109 |
| abstract_inverted_index.the | 79 |
| abstract_inverted_index.does | 117 |
| abstract_inverted_index.less | 105, 152 |
| abstract_inverted_index.poly | 131 |
| abstract_inverted_index.than | 106, 144 |
| abstract_inverted_index.that | 36, 73 |
| abstract_inverted_index.them | 103 |
| abstract_inverted_index.this | 17 |
| abstract_inverted_index.were | 26 |
| abstract_inverted_index.with | 84 |
| abstract_inverted_index.Phase | 0 |
| abstract_inverted_index.Using | 69 |
| abstract_inverted_index.image | 93 |
| abstract_inverted_index.model | 133 |
| abstract_inverted_index.phase | 46, 65, 80, 100, 139 |
| abstract_inverted_index.study | 45 |
| abstract_inverted_index.such, | 24 |
| abstract_inverted_index.times | 142, 151 |
| abstract_inverted_index.until | 62 |
| abstract_inverted_index.using | 129 |
| abstract_inverted_index.while | 147 |
| abstract_inverted_index.10,000 | 150 |
| abstract_inverted_index.across | 114 |
| abstract_inverted_index.active | 59 |
| abstract_inverted_index.faster | 143 |
| abstract_inverted_index.neural | 89 |
| abstract_inverted_index.space. | 30 |
| abstract_inverted_index.system | 134 |
| abstract_inverted_index.within | 102 |
| abstract_inverted_index.diagram | 66, 140 |
| abstract_inverted_index.governs | 3 |
| abstract_inverted_index.limited | 27 |
| abstract_inverted_index.located | 83 |
| abstract_inverted_index.machine | 41 |
| abstract_inverted_index.methods | 146 |
| abstract_inverted_index.minimal | 85 |
| abstract_inverted_index.network | 90 |
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| abstract_inverted_index.accurate | 64 |
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| abstract_inverted_index.balances | 74 |
| abstract_inverted_index.cellular | 7 |
| abstract_inverted_index.combines | 37 |
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| abstract_inverted_index.designs, | 51 |
| abstract_inverted_index.droplet. | 110 |
| abstract_inverted_index.droplets | 98 |
| abstract_inverted_index.executes | 91 |
| abstract_inverted_index.function | 8, 72 |
| abstract_inverted_index.identify | 96 |
| abstract_inverted_index.learning | 42, 60 |
| abstract_inverted_index.material | 4 |
| abstract_inverted_index.multiple | 10 |
| abstract_inverted_index.platform | 35 |
| abstract_inverted_index.studying | 16 |
| abstract_inverted_index.systems. | 48 |
| abstract_inverted_index.workflow | 61 |
| abstract_inverted_index.analysis. | 124 |
| abstract_inverted_index.consuming | 149 |
| abstract_inverted_index.extensive | 122 |
| abstract_inverted_index.material. | 153 |
| abstract_inverted_index.nonlinear | 18 |
| abstract_inverted_index.obtained. | 68 |
| abstract_inverted_index.real-time | 92 |
| abstract_inverted_index.sampling. | 86 |
| abstract_inverted_index.approaches | 14 |
| abstract_inverted_index.boundaries | 81 |
| abstract_inverted_index.generates, | 52 |
| abstract_inverted_index.phenomenon | 19 |
| abstract_inverted_index.properties | 5 |
| abstract_inverted_index.separation | 1, 47, 101 |
| abstract_inverted_index.acquisition | 71 |
| abstract_inverted_index.calibration | 120 |
| abstract_inverted_index.closed-loop | 58 |
| abstract_inverted_index.demonstrate | 126 |
| abstract_inverted_index.efficiently | 44 |
| abstract_inverted_index.experiments | 115 |
| abstract_inverted_index.exploration | 75 |
| abstract_inverted_index.microscopy, | 39 |
| abstract_inverted_index.necessitate | 20 |
| abstract_inverted_index.recognition | 94 |
| abstract_inverted_index.traditional | 145 |
| abstract_inverted_index.Conventional | 13 |
| abstract_inverted_index.PhaseXplorer | 49, 111 |
| abstract_inverted_index.autonomously | 50 |
| abstract_inverted_index.capabilities | 128 |
| abstract_inverted_index.experiments. | 22 |
| abstract_inverted_index.microfluidic | 97 |
| abstract_inverted_index.standardizes | 112 |
| abstract_inverted_index.PhaseXplorer, | 33 |
| abstract_inverted_index.convolutional | 88 |
| abstract_inverted_index.exploitation, | 77 |
| abstract_inverted_index.fundamentally | 2 |
| abstract_inverted_index.PhaseXplorer's | 127 |
| abstract_inverted_index.investigations | 25 |
| abstract_inverted_index.microfluidics, | 38 |
| abstract_inverted_index.organizational | 11 |
| abstract_inverted_index.postexperiment | 123 |
| abstract_inverted_index.simultaneously | 148 |
| abstract_inverted_index.low-dimensional | 29 |
| abstract_inverted_index.four-dimensional | 138 |
| abstract_inverted_index.resource-intensive | 21 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5022142431 |
| countries_distinct_count | 5 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I241749, https://openalex.org/I4210139501, https://openalex.org/I83019370 |
| citation_normalized_percentile |