Polymer Informatics: Current Status and Critical Next Steps
- URL: http://arxiv.org/abs/2011.00508v1
- Date: Sun, 1 Nov 2020 14:17:22 GMT
- Title: Polymer Informatics: Current Status and Critical Next Steps
- Authors: Lihua Chen, Ghanshyam Pilania, Rohit Batra, Tran Doan Huan, Chiho Kim,
Christopher Kuenneth, Rampi Ramprasad
- Abstract summary: Surrogate models are trained on available polymer data for instant property prediction.
Data-driven strategies to tackle unique challenges resulting from the extraordinary chemical and physical diversity of polymers at small and large scales are being explored.
Methods to solve inverse problems, wherein polymer recommendations are made using advanced AI algorithms that meet application targets, are being investigated.
- Score: 1.3238373064156097
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Artificial intelligence (AI) based approaches are beginning to impact several
domains of human life, science and technology. Polymer informatics is one such
domain where AI and machine learning (ML) tools are being used in the efficient
development, design and discovery of polymers. Surrogate models are trained on
available polymer data for instant property prediction, allowing screening of
promising polymer candidates with specific target property requirements.
Questions regarding synthesizability, and potential (retro)synthesis steps to
create a target polymer, are being explored using statistical means.
Data-driven strategies to tackle unique challenges resulting from the
extraordinary chemical and physical diversity of polymers at small and large
scales are being explored. Other major hurdles for polymer informatics are the
lack of widespread availability of curated and organized data, and approaches
to create machine-readable representations that capture not just the structure
of complex polymeric situations but also synthesis and processing conditions.
Methods to solve inverse problems, wherein polymer recommendations are made
using advanced AI algorithms that meet application targets, are being
investigated. As various parts of the burgeoning polymer informatics ecosystem
mature and become integrated, efficiency improvements, accelerated discoveries
and increased productivity can result. Here, we review emergent components of
this polymer informatics ecosystem and discuss imminent challenges and
opportunities.
Related papers
- AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence [0.0]
The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of large language models (LLM)
Our results enable accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of advanced metallic alloys.
arXiv Detail & Related papers (2024-07-13T22:46:02Z) - CACTUS: Chemistry Agent Connecting Tool-Usage to Science [6.832077276041703]
Large language models (LLMs) have shown remarkable potential in various domains, but they often lack the ability to access and reason over domain-specific knowledge and tools.
We introduce CACTUS, an LLM-based agent that integrates cheminformatics tools to enable advanced reasoning and problem-solving in chemistry and molecular discovery.
We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama2-7b, and Mistral-7b, on a benchmark of thousands of chemistry questions.
arXiv Detail & Related papers (2024-05-02T03:20:08Z) - An Autonomous Large Language Model Agent for Chemical Literature Data
Mining [60.85177362167166]
We introduce an end-to-end AI agent framework capable of high-fidelity extraction from extensive chemical literature.
Our framework's efficacy is evaluated using accuracy, recall, and F1 score of reaction condition data.
arXiv Detail & Related papers (2024-02-20T13:21:46Z) - Transferring a molecular foundation model for polymer property
predictions [3.067983186439152]
Self-supervised pretraining of transformer models requires large-scale datasets.
We show that using transformers pretrained on small molecules and fine-tuned on polymer properties achieve comparable accuracy to those trained on augmented polymer datasets.
arXiv Detail & Related papers (2023-10-25T19:55:00Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge
Collaborative AutoML System [85.8338446357469]
We introduce OmniForce, a human-centered AutoML system that yields both human-assisted ML and ML-assisted human techniques.
We show how OmniForce can put an AutoML system into practice and build adaptive AI in open-environment scenarios.
arXiv Detail & Related papers (2023-03-01T13:35:22Z) - Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular
Property Prediction [53.06671763877109]
We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction.
Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations.
On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
arXiv Detail & Related papers (2023-02-04T01:32:40Z) - When does deep learning fail and how to tackle it? A critical analysis
on polymer sequence-property surrogate models [1.0152838128195467]
Deep learning models are gaining popularity and potency in predicting polymer properties.
These models can be built using pre-existing data and are useful for the rapid prediction of polymer properties.
However, the performance of a deep learning model is intricately connected to its topology and the volume of training data.
arXiv Detail & Related papers (2022-10-12T23:04:10Z) - Copolymer Informatics with Multi-Task Deep Neural Networks [0.0]
We address the property prediction challenge for copolymers, extending the polymer informatics framework beyond homopolymers.
A large data set containing over 18,000 data points of glass transition, melting, and degradation temperature of homopolymers and copolymers of up to two monomers is used.
The developed models are accurate, fast, flexible, and scalable to more copolymer properties when suitable data become available.
arXiv Detail & Related papers (2021-03-25T23:28:20Z) - Polymers for Extreme Conditions Designed Using Syntax-Directed
Variational Autoencoders [53.34780987686359]
Machine learning tools are now commonly employed to virtually screen material candidates with desired properties.
This approach is inefficient, and severely constrained by the candidates that human imagination can conceive.
We utilize syntax-directed variational autoencoders (VAE) in tandem with Gaussian process regression (GPR) models to discover polymers expected to be robust under three extreme conditions.
arXiv Detail & Related papers (2020-11-04T21:36:59Z) - Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
We review the existing research regarding the use of machine learning in nano-scale biomedical engineering.
The main challenges that can be formulated as ML problems are classified into the three main categories.
For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
arXiv Detail & Related papers (2020-08-05T15:45:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.