Challenges and advances in computational chemistry and physics, Journal Year: 2025, Volume and Issue: unknown, P. 67 - 83
Published: Jan. 1, 2025
Language: Английский
Science Advances, Journal Year: 2024, Volume and Issue: 10(1)
Published: Jan. 5, 2024
Phase-separated biomolecular condensates exhibit a wide range of dynamic properties, which depend on the sequences constituent proteins and RNAs. However, it is unclear to what extent condensate dynamics can be tuned without also changing thermodynamic properties that govern phase separation. Using coarse-grained simulations intrinsically disordered proteins, we show thermodynamics homopolymer are strongly correlated, with increased stability being coincident low mobilities high viscosities. We then apply an “active learning” strategy identify heteropolymer break this correlation. This data-driven approach accompanying analysis reveal how heterogeneous amino acid compositions nonuniform sequence patterning map independently tunable condensates. Our results highlight key molecular determinants governing physical establish design rules for development stimuli-responsive biomaterials.
Language: Английский
Citations
22
SmartMat, Journal Year: 2025, Volume and Issue: 6(1)
Published: Jan. 9, 2025
ABSTRACT Machine learning (ML), material genome, and big data approaches are highly overlapped in their strategies, algorithms, models. They can target various definitions, distributions, correlations of concerned physical parameters given polymer systems, have expanding applications as a new paradigm indispensable to conventional ones. Their inherent advantages building quantitative multivariate largely enhanced the capability scientific understanding discoveries, thus facilitating mechanism exploration, prediction, high‐throughput screening, optimization, rational inverse designs. This article summarizes representative progress recent two decades focusing on design, preparation, application, sustainable development materials based exploration key composition–process–structure–property–performance relationship. The integration both data‐driven insights through ML deepen fundamental discover novel is categorically presented. Despite construction application robust models, strategies algorithms deal with variant tasks science still rapid growth. challenges prospects then We believe that innovation will thrive along approaches, from efficient design applications.
Language: Английский
Citations
4
Advanced Materials, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 9, 2025
Machine learning is increasingly being applied in polymer chemistry to link chemical structures macroscopic properties of polymers and identify patterns the that help improve specific properties. To facilitate this, a dataset needs be translated into machine readable descriptors. However, limited inadequately curated datasets, broad molecular weight distributions, irregular configurations pose significant challenges. Most off shelf mathematical models often need refinement for applications. Addressing these challenges demand close collaboration between chemists mathematicians as must formulate research questions terms while are required refine This review unites both disciplines address curation hurdles highlight advances synthesis modeling enhance data availability. It then surveys ML approaches used predict solid-state properties, solution behavior, composite performance, emerging applications such drug delivery polymer-biology interface. A perspective field concluded importance FAIR (findability, accessibility, interoperability, reusability) integration theory discussed, thoughts on machine-human interface shared.
Language: Английский
Citations
2
Macromolecular Symposia, Journal Year: 2025, Volume and Issue: 414(1)
Published: Feb. 1, 2025
Abstract Technology, health care, and transport are merely some of the industries that historically rely on polymer‐based materials. In past centuries, creation innovative polymer materials has been dependent upon extensive experiments error procedures require an number resources as well time. With objective to explore transformative potential machine learning (ML) artificial intelligence (AI) in material discovery, design, optimization, this paper explores integration ML AI research. Researchers able speed development new with improved properties functionalities by utilizing sophisticated algorithms computational models. The use research is examined, a focus how these technologies may stimulate innovation expand science
Language: Английский
Citations
2
Progress in Polymer Science, Journal Year: 2024, Volume and Issue: 153, P. 101828 - 101828
Published: May 3, 2024
Language: Английский
Citations
12
npj Computational Materials, Journal Year: 2024, Volume and Issue: 10(1)
Published: June 29, 2024
Abstract The complexity and diversity of polymer topologies, or chain architectures, present substantial challenges in predicting engineering properties. Although machine learning is increasingly used science, applications to address architecturally complex polymers are nascent. Here, we use a generative model based on variational autoencoders data generated from molecular dynamics simulations design topologies that exhibit target Following the construction dataset featuring 1342 with linear, cyclic, branch, comb, star, dendritic structures, employ multi-task framework effectively reconstructs classifies while their dilute-solution radii gyration. This enables generation size, which subsequently validated through simulation. These capabilities then exploited contrast rheological properties topologically distinct otherwise similar behavior. research opens avenues for more intricate tailored learning.
Language: Английский
Citations
12
Current Opinion in Structural Biology, Journal Year: 2025, Volume and Issue: 92, P. 103028 - 103028
Published: March 12, 2025
Language: Английский
Citations
1
ACS Polymers Au, Journal Year: 2023, Volume and Issue: 3(3), P. 284 - 294
Published: June 5, 2023
Single-chain nanoparticles (SCNPs) are intriguing materials inspired by proteins that consist of a single precursor polymer chain has collapsed into stable structure. In many prospective applications, such as catalysis, the utility single-chain nanoparticle will intricately depend on formation mostly specific structure or morphology. However, it is not generally well understood how to reliably control morphology nanoparticles. To address this knowledge gap, we simulate 7680 distinct from chains span wide range of, in principle, tunable patterning characteristics cross-linking moieties. Using combination molecular simulation and machine learning analyses, show overall fraction functionalization blockiness moieties biases certain local global morphological characteristics. Importantly, illustrate quantify dispersity morphologies arise due stochastic nature collapse well-defined sequence ensemble sequences correspond given specification parameters. Moreover, also examine efficacy precise achieving outcomes different regimes Overall, work critically assesses might be feasibly tailored achieve SCNP provides platform pursue future sequence-based design.
Language: Английский
Citations
17
Artificial Intelligence Chemistry, Journal Year: 2024, Volume and Issue: 2(2), P. 100075 - 100075
Published: July 27, 2024
The beginning and ripening of digital chemistry is analyzed focusing on the role artificial intelligence (AI) in an expected leap chemical sciences to bring this area next evolutionary level. analytic description selects highlights top 20 AI-based technologies 7 broader themes that are reshaping field. It underscores integration tools such as machine learning, big data, twins, Internet Things (IoT), robotic platforms, smart control processes, virtual reality blockchain, among many others, enhancing research methods, educational approaches, industrial practices chemistry. significance study lies its focused overview how these innovations foster a more efficient, sustainable, innovative future sciences. This article not only illustrates transformative impact but also draws new pathways chemistry, offering broad appeal researchers, educators, industry professionals embrace advancements for addressing contemporary challenges
Language: Английский
Citations
8
International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 98, P. 384 - 393
Published: Dec. 10, 2024
Language: Английский
Citations
6