8 Search Results for "Schulman, Rebecca"

Document
Research
DOI: 10.4230/TGDK.3.3.4
Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web

Authors: Florian Ruosch, Cristina Sarasua, and Abraham Bernstein

Published in: TGDK, Volume 3, Issue 3 (2025). Transactions on Graph Data and Knowledge, Volume 3, Issue 3

Abstract
In Argument Mining, predicting argumentative relations between texts (or spans) remains one of the most challenging aspects, even more so in the cross-document setting. This paper makes three key contributions to advance research in this domain. We first extend an existing dataset, the Sci-Arg corpus, by annotating it with explicit inter-document argumentative relations, thereby allowing arguments to be distributed over several documents forming an Argument Web; these new annotations are published using Semantic Web technologies (RDF, OWL). Second, we explore and evaluate three automated approaches for predicting these inter-document argumentative relations, establishing critical baselines on the new dataset. We find that a simple classifier based on discourse indicators with access to context outperforms neural methods. Third, we conduct a comparative analysis of these approaches for both intra- and inter-document settings, identifying statistically significant differences in results that indicate the necessity of distinguishing between these two scenarios. Our findings highlight significant challenges in this complex domain and open crucial avenues for future research on the Argument Web of Science, particularly for those interested in leveraging Semantic Web technologies and knowledge graphs to understand scholarly discourse. With this, we provide the first stepping stones in the form of a benchmark dataset, three baseline methods, and an initial analysis for a systematic exploration of this field relevant to the Web of Data and Science.
Cite as

Florian Ruosch, Cristina Sarasua, and Abraham Bernstein. Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web. In Transactions on Graph Data and Knowledge (TGDK), Volume 3, Issue 3, pp. 4:1-4:33, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{ruosch_et_al:TGDK.3.3.4,
  author =	{Ruosch, Florian and Sarasua, Cristina and Bernstein, Abraham},
  title =	{{Mining Inter-Document Argument Structures in Scientific Papers for an Argument Web}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{4:1--4:33},
  ISSN =	{2942-7517},
  year =	{2025},
  volume =	{3},
  number =	{3},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.3.3.4},
  URN =		{urn:nbn:de:0030-drops-252159},
  doi =		{10.4230/TGDK.3.3.4},
  annote =	{Keywords: Argument Mining, Large Language Models, Knowledge Graphs, Link Prediction}
}
Document
RANDOM
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2025.40
Sublinear Space Graph Algorithms in the Continual Release Model

Authors: Alessandro Epasto, Quanquan C. Liu, Tamalika Mukherjee, and Felix Zhou

Published in: LIPIcs, Volume 353, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2025)

Abstract
The graph continual release model of differential privacy seeks to produce differentially private solutions to graph problems under a stream of edge updates where new private solutions are released after each update. Thus far, previously known edge-differentially private algorithms for most graph problems including densest subgraph and matchings in the continual release setting only output real-value estimates (not vertex subset solutions) and do not use sublinear space. Instead, they rely on computing exact graph statistics on the input [Hendrik Fichtenberger et al., 2021; Shuang Song et al., 2018]. In this paper, we leverage sparsification to address the above shortcomings for edge-insertion streams. Our edge-differentially private algorithms use sublinear space with respect to the number of edges in the graph while some also achieve sublinear space in the number of vertices in the graph. In addition, for the densest subgraph problem, we also output edge-differentially private vertex subset solutions; no previous graph algorithms in the continual release model output such subsets. We make novel use of assorted sparsification techniques from the non-private streaming and static graph algorithms literature to achieve new results in the sublinear space, continual release setting. This includes algorithms for densest subgraph, maximum matching, as well as the first continual release k-core decomposition algorithm. We also develop a novel sparse level data structure for k-core decomposition that may be of independent interest. To complement our insertion-only algorithms, we conclude with polynomial additive error lower bounds for edge-privacy in the fully dynamic setting, where only logarithmic lower bounds were previously known.
Cite as

Alessandro Epasto, Quanquan C. Liu, Tamalika Mukherjee, and Felix Zhou. Sublinear Space Graph Algorithms in the Continual Release Model. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 353, pp. 40:1-40:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{epasto_et_al:LIPIcs.APPROX/RANDOM.2025.40,
  author =	{Epasto, Alessandro and Liu, Quanquan C. and Mukherjee, Tamalika and Zhou, Felix},
  title =	{{Sublinear Space Graph Algorithms in the Continual Release Model}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2025)},
  pages =	{40:1--40:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-397-3},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{353},
  editor =	{Ene, Alina and Chattopadhyay, Eshan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2025.40},
  URN =		{urn:nbn:de:0030-drops-244064},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2025.40},
  annote =	{Keywords: Differential Privacy, Continual Release, Densest Subgraph, k-Core Decomposition, Maximum Matching}
}
Document
DOI: 10.4230/LIPIcs.DNA.31.4
Differentiable Programming of Indexed Chemical Reaction Networks and Reaction-Diffusion Systems

Authors: Inhoo Lee, Salvador Buse, and Erik Winfree

Published in: LIPIcs, Volume 347, 31st International Conference on DNA Computing and Molecular Programming (DNA 31) (2025)

Abstract
Many molecular systems are best understood in terms of prototypical species and reactions. The central dogma and related biochemistry are rife with examples: gene i is transcribed into RNA i, which is translated into protein i; kinase n phosphorylates substrate m; protein p dimerizes with protein q. Engineered nucleic acid systems also often have this form: oligonucleotide i hybridizes to complementary oligonucleotide j; signal strand n displaces the output of seesaw gate m; hairpin p triggers the opening of target q. When there are many variants of a small number of prototypes, it can be conceptually cleaner and computationally more efficient to represent the full system in terms of indexed species (e.g. for dimerization, M_p, D_pq) and indexed reactions (M_p + M_q → D_pq). Here, we formalize the Indexed Chemical Reaction Network (ICRN) model and describe a Python software package designed to simulate such systems in the well-mixed and reaction-diffusion settings, using a differentiable programming framework originally developed for large-scale neural network models, taking advantage of GPU acceleration when available. Notably, this framework makes it straightforward to train the models’ initial conditions and rate constants to optimize a target behavior, such as matching experimental data, performing a computation, or exhibiting spatial pattern formation. The natural map of indexed chemical reaction networks onto neural network formalisms provides a tangible yet general perspective for translating concepts and techniques from the theory and practice of neural computation into the design of biomolecular systems.
Cite as

Inhoo Lee, Salvador Buse, and Erik Winfree. Differentiable Programming of Indexed Chemical Reaction Networks and Reaction-Diffusion Systems. In 31st International Conference on DNA Computing and Molecular Programming (DNA 31). Leibniz International Proceedings in Informatics (LIPIcs), Volume 347, pp. 4:1-4:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{lee_et_al:LIPIcs.DNA.31.4,
  author =	{Lee, Inhoo and Buse, Salvador and Winfree, Erik},
  title =	{{Differentiable Programming of Indexed Chemical Reaction Networks and Reaction-Diffusion Systems}},
  booktitle =	{31st International Conference on DNA Computing and Molecular Programming (DNA 31)},
  pages =	{4:1--4:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-399-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{347},
  editor =	{Schaeffer, Josie and Zhang, Fei},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DNA.31.4},
  URN =		{urn:nbn:de:0030-drops-238534},
  doi =		{10.4230/LIPIcs.DNA.31.4},
  annote =	{Keywords: Differentiable Programming, Chemical Reaction Networks, Reaction-Diffusion Systems}
}
Document
DOI: 10.4230/LIPIcs.DNA.31.7
Tile Blockers as a Simple Motif to Control Self-Assembly: Kinetics and Thermodynamics

Authors: Constantine G. Evans, Angel Cervera Roldan, Trent Rogers, and Damien Woods

Published in: LIPIcs, Volume 347, 31st International Conference on DNA Computing and Molecular Programming (DNA 31) (2025)

Abstract
A fundamental problem in crystallisation, and in molecular tile-based self-assembly in particular, is how to simultaneously control its two main constituent processes: seeded growth and spontaneous nucleation. Often, we desire out-of-equilibrium growth without spontaneous nucleation, which can be achieved through careful calibration of temperature, concentration and experimental time-scale a laborious and overly-sensitive approach. Another technique is to find alternative nucleation-resistant tile designs [Minev et al, 2001]. Rogers, Evans and Woods [In prep] propose blockers: short DNA strands designed to dynamically block DNA tile sides, altering self-assembly dynamics. Experiments showed independent and tunable control on nucleation and growth rates. Here, we provide a theoretical explanation for these surprising results. We formally define the kBlock model where blockers bind to tiles at thermodynamic equilibrium in solution and stochastic kinetics allow self-assembly of a tiled structure. In an intentionally simplified mathematical setting we show that blockers permit reasonable seeded growth rates, akin to a non-blocked tile system at lower tile concentration, crucially giving nucleation rates that are exponentially suppressed. We then implement the kBlock model in a stochastic simulator, with results showing remarkable alignment with oversimplified theory. We provide evidence of blocker-induced tile buffering, where a large reservoir of blocked tiles slowly feeds a small unblocked tile subpopulation which acts like a regular, non-blocked, low tile concentration system, yet is capable of long-term buffered assembly. Finally, and perhaps most satisfyingly, theory and simulations align remarkably well with DNA self-assembly experiments over a wide range of concentrations and temperatures, matching the size of growth temperature windows to within 12%. Blockers are a straightforward solution to the challenging problem of simultaneously and independently controlling growth and nucleation, using a motif compatible with many DNA tile systems.
Cite as

Constantine G. Evans, Angel Cervera Roldan, Trent Rogers, and Damien Woods. Tile Blockers as a Simple Motif to Control Self-Assembly: Kinetics and Thermodynamics. In 31st International Conference on DNA Computing and Molecular Programming (DNA 31). Leibniz International Proceedings in Informatics (LIPIcs), Volume 347, pp. 7:1-7:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{evans_et_al:LIPIcs.DNA.31.7,
  author =	{Evans, Constantine G. and Cervera Roldan, Angel and Rogers, Trent and Woods, Damien},
  title =	{{Tile Blockers as a Simple Motif to Control Self-Assembly: Kinetics and Thermodynamics}},
  booktitle =	{31st International Conference on DNA Computing and Molecular Programming (DNA 31)},
  pages =	{7:1--7:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-399-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{347},
  editor =	{Schaeffer, Josie and Zhang, Fei},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DNA.31.7},
  URN =		{urn:nbn:de:0030-drops-238564},
  doi =		{10.4230/LIPIcs.DNA.31.7},
  annote =	{Keywords: Self-assembly, kinetic model, kinetic simulation, thermodynamic prediction}
}
Document
DOI: 10.4230/LIPIcs.DNA.31.11
Leakless Polymerase-Dependent Strand Displacement Systems

Authors: Zoë Evelyn Mōhalakealoha Derauf and Chris Thachuk

Published in: LIPIcs, Volume 347, 31st International Conference on DNA Computing and Molecular Programming (DNA 31) (2025)

Abstract
A grand challenge facing molecular programmers is the rational development of fast, robust, and isothermal architectures akin to "chemical central processing units" that can sense (bio-)chemical signals from their environment, perform complex computation, and orchestrate a physical response in situ. DNA strand displacement systems (DSDs) remain a compelling candidate, but are hampered by spurious reaction pathways that lead to incorrect output. DSDs that utilize the systematic leakless motif can be made arbitrarily robust at the cost of increasing redundancy and network size (scaling), and thus a degradation of kinetic performance. Another class of architectures utilize DNA hybridization, extension, and signal production of entirely sequestered outputs via strand-displacing polymerases (SDPs) that have resulted in impressive demonstrations; however, they face similar challenges of aberrant behavior such as mis-priming by incorrect signals. Our work introduces a unified polymerase-dependent toehold-mediated strand displacement (PD-TMSD) architecture that integrates the programmed specificity of DSDs with the unique advantages of SDPs. This unification enables systems that can be made arbitrarily robust, at any concentration range, without increasing network size. We propose a number of gate designs and composition rules to compute arbitrary Boolean functions, emulate arbitrary chemical reaction networks, and explore time-bounded probabilistic computation made possible by certain classes of SDPs. Our theoretical exploration is backed by preliminary experimental demonstrations. This contribution was inspired by the belief that molecular programming can meet or exceed the complexity exhibited in biology if we embrace its best understood molecular machinery and couple it with systematic design principles built upon a strong theoretical foundation.
Cite as

Zoë Evelyn Mōhalakealoha Derauf and Chris Thachuk. Leakless Polymerase-Dependent Strand Displacement Systems. In 31st International Conference on DNA Computing and Molecular Programming (DNA 31). Leibniz International Proceedings in Informatics (LIPIcs), Volume 347, pp. 11:1-11:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{derauf_et_al:LIPIcs.DNA.31.11,
  author =	{Derauf, Zo\"{e} Evelyn M\={o}halakealoha and Thachuk, Chris},
  title =	{{Leakless Polymerase-Dependent Strand Displacement Systems}},
  booktitle =	{31st International Conference on DNA Computing and Molecular Programming (DNA 31)},
  pages =	{11:1--11:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-399-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{347},
  editor =	{Schaeffer, Josie and Zhang, Fei},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DNA.31.11},
  URN =		{urn:nbn:de:0030-drops-238608},
  doi =		{10.4230/LIPIcs.DNA.31.11},
  annote =	{Keywords: DNA strand displacement, strand-displacing polymerases, molecular computation, energy barriers, kinetics}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.130
An Efficient Algorithm to Compute the Minimum Free Energy of Interacting Nucleic Acid Strands

Authors: Ahmed Shalaby and Damien Woods

Published in: LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)

Abstract
The information-encoding molecules RNA and DNA bind via base pairing to form an exponentially large set of secondary structures. Practitioners need algorithms to predict the most favoured structures, called minimum free energy (MFE) structures, or to compute a partition function that allows assigning a probability to any structure. MFE prediction is NP-hard in the presence pseudoknots - base pairings that violate a restricted planarity condition. However, for single-stranded unpseudoknotted structures, there are polynomial time dynamic programming algorithms. For multiple strands, the problem is significantly more complicated: Codon, Hajiaghayi and Thachuk [DNA27, 2021] proved it NP-hard for N bases and 𝒪(N) strands. Dirks, Bois, Schaeffer, Winfree and Pierce [SIAM Review, 2007] gave a polynomial time partition function algorithm for multiple (𝒪(1)) strands, now widely-used, however their technique did not generalise to MFE which they left open. We give an 𝒪(N⁴) time algorithm for unpseudoknotted multiple (𝒪(1)) strand MFE prediction, answering the open problem from Dirks et al. The challenge lies in considering the rotational symmetry of secondary structures, a global feature not immediately amenable to local subproblem decomposition used in dynamic programming. Our proof has two main technical contributions: First, a characterisation of symmetric secondary structures implying only quadratically many need to be considered when computing the rotational symmetry penalty. Second, that bound is leveraged by a backtracking algorithm to efficiently find the MFE in an exponential space of contenders.
Cite as

Ahmed Shalaby and Damien Woods. An Efficient Algorithm to Compute the Minimum Free Energy of Interacting Nucleic Acid Strands. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 130:1-130:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{shalaby_et_al:LIPIcs.ICALP.2025.130,
  author =	{Shalaby, Ahmed and Woods, Damien},
  title =	{{An Efficient Algorithm to Compute the Minimum Free Energy of Interacting Nucleic Acid Strands}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{130:1--130:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-372-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{334},
  editor =	{Censor-Hillel, Keren and Grandoni, Fabrizio and Ouaknine, Jo\"{e}l and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.130},
  URN =		{urn:nbn:de:0030-drops-235071},
  doi =		{10.4230/LIPIcs.ICALP.2025.130},
  annote =	{Keywords: Minimum free energy, MFE, partition function, nucleic acid, DNA, RNA, secondary structure, computational complexity, algorithm analysis and design, dynamic programming}
}
Document
DOI: 10.4230/LIPIcs.STACS.2025.5
Parameterized Saga of First-Fit and Last-Fit Coloring

Authors: Akanksha Agrawal, Daniel Lokshtanov, Fahad Panolan, Saket Saurabh, and Shaily Verma

Published in: LIPIcs, Volume 327, 42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025)

Abstract
The classic greedy coloring algorithm considers the vertices of an input graph G in a given order and assigns the first available color to each vertex v in G. In the Grundy Coloring problem, the task is to find an ordering of the vertices that will force the greedy algorithm to use as many colors as possible. In the Partial Grundy Coloring, the task is also to color the graph using as many colors as possible. This time, however, we may select both the ordering in which the vertices are considered and which color to assign the vertex. The only constraint is that the color assigned to a vertex v is a color previously used for another vertex if such a color is available. Whether Grundy Coloring and Partial Grundy Coloring admit fixed-parameter tractable (FPT) algorithms, algorithms with running time f(k)n^O(1), where k is the number of colors, was posed as an open problem by Zaker and by Effantin et al., respectively. Recently, Aboulker et al. (STACS 2020 and Algorithmica 2022) resolved the question for Grundy Coloring in the negative by showing that the problem is W[1]-hard. For Partial Grundy Coloring, they obtain an FPT algorithm on graphs that do not contain K_{i,j} as a subgraph (a.k.a. K_{i,j}-free graphs). Aboulker et al. re-iterate the question of whether there exists an FPT algorithm for Partial Grundy Coloring on general graphs and also asks whether Grundy Coloring admits an FPT algorithm on K_{i,j}-free graphs. We give FPT algorithms for Partial Grundy Coloring on general graphs and for Grundy Coloring on K_{i,j}-free graphs, resolving both the questions in the affirmative. We believe that our new structural theorems for partial Grundy coloring and "representative-family" like sets for K_{i,j}-free graphs that we use in obtaining our results may have wider algorithmic applications.
Cite as

Akanksha Agrawal, Daniel Lokshtanov, Fahad Panolan, Saket Saurabh, and Shaily Verma. Parameterized Saga of First-Fit and Last-Fit Coloring. In 42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 327, pp. 5:1-5:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{agrawal_et_al:LIPIcs.STACS.2025.5,
  author =	{Agrawal, Akanksha and Lokshtanov, Daniel and Panolan, Fahad and Saurabh, Saket and Verma, Shaily},
  title =	{{Parameterized Saga of First-Fit and Last-Fit Coloring}},
  booktitle =	{42nd International Symposium on Theoretical Aspects of Computer Science (STACS 2025)},
  pages =	{5:1--5:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-365-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{327},
  editor =	{Beyersdorff, Olaf and Pilipczuk, Micha{\l} and Pimentel, Elaine and Thắng, Nguy\~{ê}n Kim},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.STACS.2025.5},
  URN =		{urn:nbn:de:0030-drops-228304},
  doi =		{10.4230/LIPIcs.STACS.2025.5},
  annote =	{Keywords: Grundy Coloring, Partial Grundy Coloring, FPT Algorithm, K\underline\{i,j\}-free graphs}
}
Document
DOI: 10.4230/LIPIcs.DNA.28.4
Exploring Material Design Space with a Deep-Learning Guided Genetic Algorithm

Authors: Kuan-Lin Chen and Rebecca Schulman

Published in: LIPIcs, Volume 238, 28th International Conference on DNA Computing and Molecular Programming (DNA 28) (2022)

Abstract
Designing complex, dynamic yet multi-functional materials and devices is challenging because the design spaces for these materials have numerous interdependent and often conflicting constraints. Taking inspiration from advances in artificial intelligence and their applications in material discovery, we propose a computational method for designing metamorphic DNA-co-polymerized hydrogel structures. The method consists of a coarse-grained simulation and a deep learning-guided optimization system for exploring the immense design space of these structures. Here, we develop a simple numeric simulation of DNA-co-polymerized hydrogel shape change and seek to find designs for structured hydrogels that can fold into the shapes of different Arabic numerals in different actuation states. We train a convolutional neural network to classify and score the geometric outputs of the coarse-grained simulation to provide autonomous feedback for design optimization. We then construct a genetic algorithm that generates and selects large batches of material designs that compete with one another to evolve and converge on optimal objective-matching designs. We show that we are able to explore the large design space and learn important parameters and traits. We identify vital relationships between the material scale size and the range of shape change that can be achieved by individual domains and we elucidate trade-offs between different design parameters. Finally, we discover material designs capable of transforming into multiple different digits in different actuation states.
Cite as

Kuan-Lin Chen and Rebecca Schulman. Exploring Material Design Space with a Deep-Learning Guided Genetic Algorithm. In 28th International Conference on DNA Computing and Molecular Programming (DNA 28). Leibniz International Proceedings in Informatics (LIPIcs), Volume 238, pp. 4:1-4:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{chen_et_al:LIPIcs.DNA.28.4,
  author =	{Chen, Kuan-Lin and Schulman, Rebecca},
  title =	{{Exploring Material Design Space with a Deep-Learning Guided Genetic Algorithm}},
  booktitle =	{28th International Conference on DNA Computing and Molecular Programming (DNA 28)},
  pages =	{4:1--4:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-253-2},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{238},
  editor =	{Ouldridge, Thomas E. and Wickham, Shelley F. J.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.DNA.28.4},
  URN =		{urn:nbn:de:0030-drops-167899},
  doi =		{10.4230/LIPIcs.DNA.28.4},
  annote =	{Keywords: Machine Learning, Deep Learning, Computational Material Design, Multi-Objective Optimization, DNA Nanotechnology}
}
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