2025
|
Malica, Cristiano; Novoselov, Kostya S; Barnard, Amanda S; V, Sergei Kalinin; Spurgeon, Steven R; Reuter, Karsten; Alducin, Maite; Deringer, Volker L; Csanyi, Gabor; Marzari, Nicola; Huang, Shirong; Cuniberti, Gianaurelio; Deng, Qiushi; Ordejon, Pablo; Cole, Ivan; Choudhary, Kamal; Hippalgaonkar, Kedar; Zhu, Ruiming; von Lilienfeld, Anatole O; Hibat-Allah, Mohamed; Carrasquilla, Juan; Cisotto, Giulia; Zancanaro, Alberto; Wenzel, Wolfgang; Ferrari, Andrea C; Ustyuzhanin, Andrey; Roche, Stephan Artificial intelligence for advanced functional materials: exploring
current and future directions JOURNAL OF PHYSICS-MATERIALS, 8 (2), 2025, DOI: 10.1088/2515-7639/adc29d. Abstract | BibTeX | Endnote @article{WOS:001473720000001,
title = {Artificial intelligence for advanced functional materials: exploring
current and future directions},
author = {Cristiano Malica and Kostya S Novoselov and Amanda S Barnard and Sergei Kalinin V and Steven R Spurgeon and Karsten Reuter and Maite Alducin and Volker L Deringer and Gabor Csanyi and Nicola Marzari and Shirong Huang and Gianaurelio Cuniberti and Qiushi Deng and Pablo Ordejon and Ivan Cole and Kamal Choudhary and Kedar Hippalgaonkar and Ruiming Zhu and Anatole O von Lilienfeld and Mohamed Hibat-Allah and Juan Carrasquilla and Giulia Cisotto and Alberto Zancanaro and Wolfgang Wenzel and Andrea C Ferrari and Andrey Ustyuzhanin and Stephan Roche},
doi = {10.1088/2515-7639/adc29d},
times_cited = {2},
year = {2025},
date = {2025-04-01},
journal = {JOURNAL OF PHYSICS-MATERIALS},
volume = {8},
number = {2},
publisher = {IOP Publishing Ltd},
address = {No.2 The Distillery, Glassfields, Avon Street, Bristol, ENGLAND},
abstract = {This perspective addresses the topic of harnessing the tools of
artificial intelligence (AI) for boosting innovation in functional
materials design and engineering as well as discovering new materials
for targeted applications in energy storage, biomedicine, composites,
nanoelectronics or quantum technologies. It gives a current view of
experts in the field, insisting on challenges and opportunities provided
by the development of large materials databases, novel schemes for
implementing AI into materials production and characterization as well
as progress in the quest of simulating physical and chemical properties
of realistic atomic models reaching the trillion atoms scale and with
near ab initio accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This perspective addresses the topic of harnessing the tools of
artificial intelligence (AI) for boosting innovation in functional
materials design and engineering as well as discovering new materials
for targeted applications in energy storage, biomedicine, composites,
nanoelectronics or quantum technologies. It gives a current view of
experts in the field, insisting on challenges and opportunities provided
by the development of large materials databases, novel schemes for
implementing AI into materials production and characterization as well
as progress in the quest of simulating physical and chemical properties
of realistic atomic models reaching the trillion atoms scale and with
near ab initio accuracy. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AFCristiano Malica
Kostya S Novoselov
Amanda S Barnard
Sergei Kalinin V
Steven R Spurgeon
Karsten Reuter
Maite Alducin
Volker L Deringer
Gabor Csanyi
Nicola Marzari
Shirong Huang
Gianaurelio Cuniberti
Qiushi Deng
Pablo Ordejon
Ivan Cole
Kamal Choudhary
Kedar Hippalgaonkar
Ruiming Zhu
Anatole O von Lilienfeld
Mohamed Hibat-Allah
Juan Carrasquilla
Giulia Cisotto
Alberto Zancanaro
Wolfgang Wenzel
Andrea C Ferrari
Andrey Ustyuzhanin
Stephan Roche
- TIArtificial intelligence for advanced functional materials: exploring
current and future directions - SOJOURNAL OF PHYSICS-MATERIALS
- DTArticle
- ABThis perspective addresses the topic of harnessing the tools of
artificial intelligence (AI) for boosting innovation in functional
materials design and engineering as well as discovering new materials
for targeted applications in energy storage, biomedicine, composites,
nanoelectronics or quantum technologies. It gives a current view of
experts in the field, insisting on challenges and opportunities provided
by the development of large materials databases, novel schemes for
implementing AI into materials production and characterization as well
as progress in the quest of simulating physical and chemical properties
of realistic atomic models reaching the trillion atoms scale and with
near ab initio accuracy. - Z92
- PUIOP Publishing Ltd
- PANo.2 The Distillery, Glassfields, Avon Street, Bristol, ENGLAND
- VL8
- DI10.1088/2515-7639/adc29d
- UTWOS:001473720000001
- ER
- EF
|
Velasco, Pablo Quijano; Hippalgaonkar, Kedar; Ramalingam, Balamurugan Emerging trends in the optimization of organic synthesis through
high-throughput tools and machine learning BEILSTEIN JOURNAL OF ORGANIC CHEMISTRY, 21 , pp. 10-38, 2025, DOI: 10.3762/bjoc.21.3. Abstract | BibTeX | Endnote @article{WOS:001390361600001,
title = {Emerging trends in the optimization of organic synthesis through
high-throughput tools and machine learning},
author = {Pablo Quijano Velasco and Kedar Hippalgaonkar and Balamurugan Ramalingam},
doi = {10.3762/bjoc.21.3},
times_cited = {6},
issn = {1860-5397},
year = {2025},
date = {2025-01-01},
journal = {BEILSTEIN JOURNAL OF ORGANIC CHEMISTRY},
volume = {21},
pages = {10-38},
publisher = {BEILSTEIN-INSTITUT},
address = {TRAKEHNER STRASSE 7-9, FRANKFURT AM MAIN, 60487, GERMANY},
abstract = {The discovery of the optimal conditions for chemical reactions is a
labor-intensive, time-consuming task that requires exploring a
high-dimensional parametric space. Historically, the optimization of
chemical reactions has been performed by manual experimentation guided
by human intuition and through the design of experiments where reaction
variables are modified one at a time to find the optimal conditions for
a specific reaction outcome. Recently, a paradigm change in chemical
reaction optimization has been enabled by advances in lab automation and
the introduction of machine learning algorithms. Therein, multiple
reaction variables can be synchronously optimized to obtain the optimal
reaction conditions, requiring a shorter experimentation time and
minimal human intervention. Herein, we review the currently used
state-of-the-art high-throughput automated chemical reaction platforms
and machine learning algorithms that drive the optimization of chemical
reactions, highlighting the limitations and future opportunities of this
new field of research.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The discovery of the optimal conditions for chemical reactions is a
labor-intensive, time-consuming task that requires exploring a
high-dimensional parametric space. Historically, the optimization of
chemical reactions has been performed by manual experimentation guided
by human intuition and through the design of experiments where reaction
variables are modified one at a time to find the optimal conditions for
a specific reaction outcome. Recently, a paradigm change in chemical
reaction optimization has been enabled by advances in lab automation and
the introduction of machine learning algorithms. Therein, multiple
reaction variables can be synchronously optimized to obtain the optimal
reaction conditions, requiring a shorter experimentation time and
minimal human intervention. Herein, we review the currently used
state-of-the-art high-throughput automated chemical reaction platforms
and machine learning algorithms that drive the optimization of chemical
reactions, highlighting the limitations and future opportunities of this
new field of research. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AFPablo Quijano Velasco
Kedar Hippalgaonkar
Balamurugan Ramalingam
- TIEmerging trends in the optimization of organic synthesis through
high-throughput tools and machine learning - SOBEILSTEIN JOURNAL OF ORGANIC CHEMISTRY
- DTArticle
- ABThe discovery of the optimal conditions for chemical reactions is a
labor-intensive, time-consuming task that requires exploring a
high-dimensional parametric space. Historically, the optimization of
chemical reactions has been performed by manual experimentation guided
by human intuition and through the design of experiments where reaction
variables are modified one at a time to find the optimal conditions for
a specific reaction outcome. Recently, a paradigm change in chemical
reaction optimization has been enabled by advances in lab automation and
the introduction of machine learning algorithms. Therein, multiple
reaction variables can be synchronously optimized to obtain the optimal
reaction conditions, requiring a shorter experimentation time and
minimal human intervention. Herein, we review the currently used
state-of-the-art high-throughput automated chemical reaction platforms
and machine learning algorithms that drive the optimization of chemical
reactions, highlighting the limitations and future opportunities of this
new field of research. - Z96
- PUBEILSTEIN-INSTITUT
- PATRAKEHNER STRASSE 7-9, FRANKFURT AM MAIN, 60487, GERMANY
- SN1860-5397
- VL21
- BP10
- EP38
- DI10.3762/bjoc.21.3
- UTWOS:001390361600001
- ER
- EF
|
2024
|
Tan, Jin Da; Low, Andre K Y; Ying, Shannon Thoi Rui; Tan, Sze Yu; Zhao, Wenguang; Lim, Yee-Fun; Li, Qianxiao; Khan, Saif A; Ramalingam, Balamurugan; Hippalgaonkar, Kedar Multi-objective synthesis optimization and kinetics of a sustainable
terpolymer DIGITAL DISCOVERY, 3 (12), pp. 2628-2636, 2024, DOI: 10.1039/d4dd00233d. Abstract | BibTeX | Endnote @article{WOS:001349870400001,
title = {Multi-objective synthesis optimization and kinetics of a sustainable
terpolymer},
author = {Jin Da Tan and Andre K Y Low and Shannon Thoi Rui Ying and Sze Yu Tan and Wenguang Zhao and Yee-Fun Lim and Qianxiao Li and Saif A Khan and Balamurugan Ramalingam and Kedar Hippalgaonkar},
doi = {10.1039/d4dd00233d},
times_cited = {2},
year = {2024},
date = {2024-12-01},
journal = {DIGITAL DISCOVERY},
volume = {3},
number = {12},
pages = {2628-2636},
publisher = {ROYAL SOC CHEMISTRY},
address = {THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
ENGLAND},
abstract = {The properties of polymers are primarily influenced by their monomer
constituents, functional groups, and their mode of linkages. Copolymers,
synthesized from multiple monomers, offer unique material properties
compared to their homopolymers. Optimizing the synthesis of terpolymers
is a complex and labor-intensive task due to variations in monomer
reactivity and their compositional shifts throughout the polymerization
process. The present work focuses on synthesizing a new terpolymer from
styrene, myrcene, and dibutyl itaconate (DBI) monomers with the goal of
achieving a high glass transition temperature (Tg) in the resulting
terpolymer. While the copolymerization of pairwise combinations of
styrene, myrcene, and DBI have been previously investigated, the
terpolymerization of all three at once remains unexplored. Terpolymers
with monomers like styrene would provide high glass transition
temperatures as the resultant polymers exhibit a rigid glassy state at
ambient temperatures. Conversely, minimizing styrene incorporation also
reduces reliance on petrochemical-derived monomer sources for terpolymer
synthesis, thus enhancing the sustainability of terpolymer usage. To
balance the objectives of maximizing Tg while minimizing styrene
incorporation, we employ multi-objective Bayesian optimization to
efficiently sample in a design space comprising 5 experimental
parameters. We perform two iterations of optimization for a total of 89
terpolymers, reporting terpolymers with a Tg above ambient temperature
while retaining less than 50% styrene incorporation. This underscores
the potential for exploring and utilizing renewable monomers such as
myrcene and DBI, to foster sustainability in polymer synthesis.
Additionally, the dataset enables the calculation of ternary reactivity
ratios using a system of ordinary differential equations based on the
terminal model, providing valuable insights into the reactivity of
monomers in complex ternary systems compared to binary copolymer
systems. This approach reveals the nuanced kinetics of
terpolymerization, further informing the synthesis of polymers with
desired properties.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The properties of polymers are primarily influenced by their monomer
constituents, functional groups, and their mode of linkages. Copolymers,
synthesized from multiple monomers, offer unique material properties
compared to their homopolymers. Optimizing the synthesis of terpolymers
is a complex and labor-intensive task due to variations in monomer
reactivity and their compositional shifts throughout the polymerization
process. The present work focuses on synthesizing a new terpolymer from
styrene, myrcene, and dibutyl itaconate (DBI) monomers with the goal of
achieving a high glass transition temperature (Tg) in the resulting
terpolymer. While the copolymerization of pairwise combinations of
styrene, myrcene, and DBI have been previously investigated, the
terpolymerization of all three at once remains unexplored. Terpolymers
with monomers like styrene would provide high glass transition
temperatures as the resultant polymers exhibit a rigid glassy state at
ambient temperatures. Conversely, minimizing styrene incorporation also
reduces reliance on petrochemical-derived monomer sources for terpolymer
synthesis, thus enhancing the sustainability of terpolymer usage. To
balance the objectives of maximizing Tg while minimizing styrene
incorporation, we employ multi-objective Bayesian optimization to
efficiently sample in a design space comprising 5 experimental
parameters. We perform two iterations of optimization for a total of 89
terpolymers, reporting terpolymers with a Tg above ambient temperature
while retaining less than 50% styrene incorporation. This underscores
the potential for exploring and utilizing renewable monomers such as
myrcene and DBI, to foster sustainability in polymer synthesis.
Additionally, the dataset enables the calculation of ternary reactivity
ratios using a system of ordinary differential equations based on the
terminal model, providing valuable insights into the reactivity of
monomers in complex ternary systems compared to binary copolymer
systems. This approach reveals the nuanced kinetics of
terpolymerization, further informing the synthesis of polymers with
desired properties. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AFJin Da Tan
Andre K Y Low
Shannon Thoi Rui Ying
Sze Yu Tan
Wenguang Zhao
Yee-Fun Lim
Qianxiao Li
Saif A Khan
Balamurugan Ramalingam
Kedar Hippalgaonkar
- TIMulti-objective synthesis optimization and kinetics of a sustainable
terpolymer - SODIGITAL DISCOVERY
- DTArticle
- ABThe properties of polymers are primarily influenced by their monomer
constituents, functional groups, and their mode of linkages. Copolymers,
synthesized from multiple monomers, offer unique material properties
compared to their homopolymers. Optimizing the synthesis of terpolymers
is a complex and labor-intensive task due to variations in monomer
reactivity and their compositional shifts throughout the polymerization
process. The present work focuses on synthesizing a new terpolymer from
styrene, myrcene, and dibutyl itaconate (DBI) monomers with the goal of
achieving a high glass transition temperature (Tg) in the resulting
terpolymer. While the copolymerization of pairwise combinations of
styrene, myrcene, and DBI have been previously investigated, the
terpolymerization of all three at once remains unexplored. Terpolymers
with monomers like styrene would provide high glass transition
temperatures as the resultant polymers exhibit a rigid glassy state at
ambient temperatures. Conversely, minimizing styrene incorporation also
reduces reliance on petrochemical-derived monomer sources for terpolymer
synthesis, thus enhancing the sustainability of terpolymer usage. To
balance the objectives of maximizing Tg while minimizing styrene
incorporation, we employ multi-objective Bayesian optimization to
efficiently sample in a design space comprising 5 experimental
parameters. We perform two iterations of optimization for a total of 89
terpolymers, reporting terpolymers with a Tg above ambient temperature
while retaining less than 50% styrene incorporation. This underscores
the potential for exploring and utilizing renewable monomers such as
myrcene and DBI, to foster sustainability in polymer synthesis.
Additionally, the dataset enables the calculation of ternary reactivity
ratios using a system of ordinary differential equations based on the
terminal model, providing valuable insights into the reactivity of
monomers in complex ternary systems compared to binary copolymer
systems. This approach reveals the nuanced kinetics of
terpolymerization, further informing the synthesis of polymers with
desired properties. - Z92
- PUROYAL SOC CHEMISTRY
- PATHOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
ENGLAND - VL3
- BP2628
- EP2636
- DI10.1039/d4dd00233d
- UTWOS:001349870400001
- ER
- EF
|
Yoon, Ji Wei; Kumar, Adithya; Kumar, Pawan; Hippalgaonkar, Kedar; Senthilnath, J; Chellappan, Vijila Explainable machine learning to enable high-throughput electrical
conductivity optimization and discovery of doped conjugated polymers KNOWLEDGE-BASED SYSTEMS, 295 , 2024, DOI: 10.1016/j.knosys.2024.111812. Abstract | BibTeX | Endnote @article{WOS:001237223200001,
title = {Explainable machine learning to enable high-throughput electrical
conductivity optimization and discovery of doped conjugated polymers},
author = {Ji Wei Yoon and Adithya Kumar and Pawan Kumar and Kedar Hippalgaonkar and J Senthilnath and Vijila Chellappan},
doi = {10.1016/j.knosys.2024.111812},
times_cited = {10},
issn = {0950-7051},
year = {2024},
date = {2024-07-01},
journal = {KNOWLEDGE-BASED SYSTEMS},
volume = {295},
publisher = {ELSEVIER},
address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
abstract = {The combination of high-throughput experimentation techniques and
machine learning (ML) has recently ushered in a new era of accelerated
material discovery, enabling the identification of materials with
cutting-edge properties. However, the measurement of certain physical
quantities remains challenging to automate. Specifically, meticulous
process control, experimentation and laborious measurements are required
to achieve optimal electrical conductivity in doped polymer materials.
We propose a ML approach, which relies on readily measured absorbance
spectra, to accelerate the workflow associated with measuring electrical
conductivity. The classification model accurately classifies samples
with a conductivity >similar to 25 to 100 S/cm, achieving a maximum of
100 % accuracy rate. For the subset of highly conductive samples, we
employed a regression model to predict their conductivities, yielding an
impressive test R-2 value of 0.984. We tested the models with samples of
the two highest conductivities (498 and 506 S/cm) and showed that they
were able to correctly classify and predict the two extrapolative
conductivities at satisfactory levels of errors. The proposed
ML-assisted workflow results in an improvement in the efficiency of the
conductivity measurements by 89 % of the maximum achievable using our
experimental techniques. Furthermore, our approach addressed the common
challenge of the lack of explainability in ML models by exploiting
bespoke mathematical properties of the descriptors and ML model,
allowing us to gain corroborated insights into the spectral influences
on conductivity. Through this study, we offer an accelerated pathway for
optimizing the properties of doped polymer materials while showcasing
the valuable insights that can be derived from purposeful utilization of
ML in experimental science.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The combination of high-throughput experimentation techniques and
machine learning (ML) has recently ushered in a new era of accelerated
material discovery, enabling the identification of materials with
cutting-edge properties. However, the measurement of certain physical
quantities remains challenging to automate. Specifically, meticulous
process control, experimentation and laborious measurements are required
to achieve optimal electrical conductivity in doped polymer materials.
We propose a ML approach, which relies on readily measured absorbance
spectra, to accelerate the workflow associated with measuring electrical
conductivity. The classification model accurately classifies samples
with a conductivity >similar to 25 to 100 S/cm, achieving a maximum of
100 % accuracy rate. For the subset of highly conductive samples, we
employed a regression model to predict their conductivities, yielding an
impressive test R-2 value of 0.984. We tested the models with samples of
the two highest conductivities (498 and 506 S/cm) and showed that they
were able to correctly classify and predict the two extrapolative
conductivities at satisfactory levels of errors. The proposed
ML-assisted workflow results in an improvement in the efficiency of the
conductivity measurements by 89 % of the maximum achievable using our
experimental techniques. Furthermore, our approach addressed the common
challenge of the lack of explainability in ML models by exploiting
bespoke mathematical properties of the descriptors and ML model,
allowing us to gain corroborated insights into the spectral influences
on conductivity. Through this study, we offer an accelerated pathway for
optimizing the properties of doped polymer materials while showcasing
the valuable insights that can be derived from purposeful utilization of
ML in experimental science. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AFJi Wei Yoon
Adithya Kumar
Pawan Kumar
Kedar Hippalgaonkar
J Senthilnath
Vijila Chellappan
- TIExplainable machine learning to enable high-throughput electrical
conductivity optimization and discovery of doped conjugated polymers - SOKNOWLEDGE-BASED SYSTEMS
- DTArticle
- ABThe combination of high-throughput experimentation techniques and
machine learning (ML) has recently ushered in a new era of accelerated
material discovery, enabling the identification of materials with
cutting-edge properties. However, the measurement of certain physical
quantities remains challenging to automate. Specifically, meticulous
process control, experimentation and laborious measurements are required
to achieve optimal electrical conductivity in doped polymer materials.
We propose a ML approach, which relies on readily measured absorbance
spectra, to accelerate the workflow associated with measuring electrical
conductivity. The classification model accurately classifies samples
with a conductivity >similar to 25 to 100 S/cm, achieving a maximum of
100 % accuracy rate. For the subset of highly conductive samples, we
employed a regression model to predict their conductivities, yielding an
impressive test R-2 value of 0.984. We tested the models with samples of
the two highest conductivities (498 and 506 S/cm) and showed that they
were able to correctly classify and predict the two extrapolative
conductivities at satisfactory levels of errors. The proposed
ML-assisted workflow results in an improvement in the efficiency of the
conductivity measurements by 89 % of the maximum achievable using our
experimental techniques. Furthermore, our approach addressed the common
challenge of the lack of explainability in ML models by exploiting
bespoke mathematical properties of the descriptors and ML model,
allowing us to gain corroborated insights into the spectral influences
on conductivity. Through this study, we offer an accelerated pathway for
optimizing the properties of doped polymer materials while showcasing
the valuable insights that can be derived from purposeful utilization of
ML in experimental science. - Z910
- PUELSEVIER
- PARADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
- SN0950-7051
- VL295
- DI10.1016/j.knosys.2024.111812
- UTWOS:001237223200001
- ER
- EF
|
Low, Andre K Y; Mekki-Berrada, Flore; Gupta, Abhishek; Ostudin, Aleksandr; Xie, Jiaxun; Vissol-Gaudin, Eleonore; Lim, Yee-Fun; Li, Qianxiao; Ong, Yew Soon; Khan, Saif A; Hippalgaonkar, Kedar Evolution-guided Bayesian optimization for constrained multi-objective
optimization in self-driving labs 33 NPJ COMPUTATIONAL MATERIALS, 10 (1), 2024, DOI: 10.1038/s41524-024-01274-x. Abstract | BibTeX | Endnote @article{WOS:001221677000002,
title = {Evolution-guided Bayesian optimization for constrained multi-objective
optimization in self-driving labs},
author = {Andre K Y Low and Flore Mekki-Berrada and Abhishek Gupta and Aleksandr Ostudin and Jiaxun Xie and Eleonore Vissol-Gaudin and Yee-Fun Lim and Qianxiao Li and Yew Soon Ong and Saif A Khan and Kedar Hippalgaonkar},
doi = {10.1038/s41524-024-01274-x},
times_cited = {33},
year = {2024},
date = {2024-05-01},
journal = {NPJ COMPUTATIONAL MATERIALS},
volume = {10},
number = {1},
publisher = {NATURE PORTFOLIO},
address = {HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY},
abstract = {The development of automated high-throughput experimental platforms has
enabled fast sampling of high-dimensional decision spaces. To reach
target properties efficiently, these platforms are increasingly paired
with intelligent experimental design. However, current optimizers show
limitations in maintaining sufficient exploration/exploitation balance
for problems dealing with multiple conflicting objectives and complex
constraints. Here, we devise an Evolution-Guided Bayesian Optimization
(EGBO) algorithm that integrates selection pressure in parallel with a
q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not
only solves for the Pareto Front (PF) efficiently but also achieves
better coverage of the PF while limiting sampling in the infeasible
space. The algorithm is developed together with a custom self-driving
lab for seed-mediated silver nanoparticle synthesis, targeting 3
objectives (1) optical properties, (2) fast reaction, and (3) minimal
seed usage alongside complex constraints. We demonstrate that, with
appropriate constraint handling, EGBO performance improves upon
state-of-the-art qNEHVI. Furthermore, across various synthetic
multi-objective problems, EGBO shows significative hypervolume
improvement, revealing the synergy between selection pressure and the
qNEHVI optimizer. We also demonstrate EGBO's good coverage of the PF as
well as comparatively better ability to propose feasible solutions. We
thus propose EGBO as a general framework for efficiently solving
constrained multi-objective problems in high-throughput experimentation
platforms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The development of automated high-throughput experimental platforms has
enabled fast sampling of high-dimensional decision spaces. To reach
target properties efficiently, these platforms are increasingly paired
with intelligent experimental design. However, current optimizers show
limitations in maintaining sufficient exploration/exploitation balance
for problems dealing with multiple conflicting objectives and complex
constraints. Here, we devise an Evolution-Guided Bayesian Optimization
(EGBO) algorithm that integrates selection pressure in parallel with a
q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not
only solves for the Pareto Front (PF) efficiently but also achieves
better coverage of the PF while limiting sampling in the infeasible
space. The algorithm is developed together with a custom self-driving
lab for seed-mediated silver nanoparticle synthesis, targeting 3
objectives (1) optical properties, (2) fast reaction, and (3) minimal
seed usage alongside complex constraints. We demonstrate that, with
appropriate constraint handling, EGBO performance improves upon
state-of-the-art qNEHVI. Furthermore, across various synthetic
multi-objective problems, EGBO shows significative hypervolume
improvement, revealing the synergy between selection pressure and the
qNEHVI optimizer. We also demonstrate EGBO's good coverage of the PF as
well as comparatively better ability to propose feasible solutions. We
thus propose EGBO as a general framework for efficiently solving
constrained multi-objective problems in high-throughput experimentation
platforms. - FNClarivate Analytics Web of Science
- VR1.0
- PTJ
- AFAndre K Y Low
Flore Mekki-Berrada
Abhishek Gupta
Aleksandr Ostudin
Jiaxun Xie
Eleonore Vissol-Gaudin
Yee-Fun Lim
Qianxiao Li
Yew Soon Ong
Saif A Khan
Kedar Hippalgaonkar
- TIEvolution-guided Bayesian optimization for constrained multi-objective
optimization in self-driving labs - SONPJ COMPUTATIONAL MATERIALS
- DTArticle
- ABThe development of automated high-throughput experimental platforms has
enabled fast sampling of high-dimensional decision spaces. To reach
target properties efficiently, these platforms are increasingly paired
with intelligent experimental design. However, current optimizers show
limitations in maintaining sufficient exploration/exploitation balance
for problems dealing with multiple conflicting objectives and complex
constraints. Here, we devise an Evolution-Guided Bayesian Optimization
(EGBO) algorithm that integrates selection pressure in parallel with a
q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not
only solves for the Pareto Front (PF) efficiently but also achieves
better coverage of the PF while limiting sampling in the infeasible
space. The algorithm is developed together with a custom self-driving
lab for seed-mediated silver nanoparticle synthesis, targeting 3
objectives (1) optical properties, (2) fast reaction, and (3) minimal
seed usage alongside complex constraints. We demonstrate that, with
appropriate constraint handling, EGBO performance improves upon
state-of-the-art qNEHVI. Furthermore, across various synthetic
multi-objective problems, EGBO shows significative hypervolume
improvement, revealing the synergy between selection pressure and the
qNEHVI optimizer. We also demonstrate EGBO's good coverage of the PF as
well as comparatively better ability to propose feasible solutions. We
thus propose EGBO as a general framework for efficiently solving
constrained multi-objective problems in high-throughput experimentation
platforms. - Z933
- PUNATURE PORTFOLIO
- PAHEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY
- VL10
- DI10.1038/s41524-024-01274-x
- UTWOS:001221677000002
- ER
- EF
|
