Results 261 to 270 of about 17,106 (295)

SigmaFormer: Augmenting transformer encoders with COSMO sigma profiles for pure component property prediction

AIChE Journal, EarlyView.
Abstract Transformer‐based molecular models pretrained on SMILES strings demonstrate strong performance in property prediction. However, these model often lack explicit integration of molecular surface charge distributions that govern intermolecular interactions such as hydrogen bonding and polarity.
Tae Hyun Kim, Silabrata Pahari, Joseph Sang‐Il Kwon   +2 more
wiley   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

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Scaled Symbolic Regression

Genetic Programming and Evolvable Machines, 2004
Performing a linear regression on the outputs of arbitrary symbolic expressions has empirically been found to provide great benefits. Here some basic theoretical results of linear regression are reviewed on their applicability for use in symbolic regression.
Maarten Keijzer
exaly   +2 more sources

Recent Advances in Symbolic Regression

ACM Computing Surveys
Symbolic regression (SR) is an optimization problem that identifies the most suitable mathematical expression or model to fit the observed dataset. Over the past decade, SR has experienced rapid development due to its interpretability and broad applicability, leading to numerous algorithms for addressing SR problems and a steady increase in practical ...
Jinghui Zhong
exaly   +2 more sources

Parse-matrix evolution for symbolic regression

Engineering Applications of Artificial Intelligence, 2012
Data-driven model is highly desirable for industrial data analysis in case the experimental model structure is unknown or wrong, or the concerned system has changed.
Changtong Luo, Shao-Liang Zhang
exaly   +2 more sources

Simulated annealing for symbolic regression

Proceedings of the Genetic and Evolutionary Computation Conference, 2021
Symbolic regression aims to hypothesize a functional relationship involving explanatory variables and one or more dependent variables, based on examples of the desired input-output behavior. Genetic programming is a meta-heuristic commonly used in the literature to achieve this goal.
Daniel Kantor, Fernando J. Von Zuben, Fabrício Olivetti de França   +2 more
openaire   +1 more source

Explaining Symbolic Regression Predictions

2020 IEEE Congress on Evolutionary Computation (CEC), 2020
The outgrowing application of machine learning methods has raised a discussion in the artificial intelligence community on model transparency. In the center of this discussion is the question of model explanation and interpretability. The genetic programming (GP) community has systematically pointed out as one of the major advantages of GP the fact ...
Renato Miranda Filho, Anísio Lacerda, Gisele L. Pappa   +2 more
openaire   +1 more source

A GRASP approach for Symbolic Regression

2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019
In this paper a metaheuristic approach is proposed for solving the problem of symbolic regression for function approximation. The focus is on developing a method that is easy to implement and can be used to generate initial populations for more advanced metaheuristics.
Raka Jovanovic, Sahel Ashhab
openaire   +1 more source