syn-pdfQA/01.1_Input_Files_Non_PDF/research articles/2510.22503v1.tex

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% \title{LEMMA: LLM-Guided Evolution for Multi-Objective Materials Synthesis}
\title{Accelerating Materials Design via LLM-Guided Evolutionary Search}
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% \author{
% Nikhil Abhyankar$^{1}$, 
% Sanchit Kabra$^{1}$, 
% Saaketh Desai$^{2}$, 
% Chandan K. Reddy$^{1}$ \\
% $^{1}$Department of Computer Science, Virginia Tech \\
% $^{2}$Sandia National Laboratories \\
% \texttt{\{nikhilsa, sanchit23, reddy\}@vt.edu}, \texttt{saadesa@sandia.gov}
% }

\author{
Nikhil Abhyankar$^{1}$\thanks{Equal contribution. Correspondence: \texttt{nikhilsa@vt.edu, sanchit23@vt.edu}.} \quad
Sanchit Kabra$^{1}$\footnotemark[1] \quad
Saaketh Desai$^{2}$ \quad
Chandan K. Reddy$^{1}$ \\
$^{1}$Department of Computer Science, Virginia Tech \\
$^{2}$Center of Integrated Nanotechnologies, Sandia National Laboratories}
% \texttt{\{nikhilsa, sanchit23, reddy\}@vt.edu}, \texttt{saadesa@sandia.gov} \\}


% Antiquus S.~Hippocampus, Natalia Cerebro \& Amelie P. Amygdale \thanks{ Use footnote for providing further information
% about author (webpage, alternative address)---\emph{not} for acknowledging
% funding agencies.  Funding acknowledgements go at the end of the paper.} \\
% Department of Computer Science\\
% Cranberry-Lemon University\\
% Pittsburgh, PA 15213, USA \\
% \texttt{\{hippo,brain,jen\}@cs.cranberry-lemon.edu} \\
% \And
% Ji Q. Ren \& Yevgeny LeNet \\
% Department of Computational Neuroscience \\
% University of the Witwatersrand \\
% Joburg, South Africa \\
% \texttt{\{robot,net\}@wits.ac.za} \\
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% \newcommand{\fix}{\marginpar{FIX}}
\newcommand*{\AlgName}{\text{LLEMA}\@\xspace}
\newcommand{\saaketh}[1]{\textbf{\textcolor{orange}{#1}}}
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\begin{document}


\maketitle

\begin{abstract}
Materials discovery requires navigating vast chemical and structural spaces while satisfying multiple, often conflicting, objectives. We present \textbf{LL}M-guided \textbf{E}volution for \textbf{MA}terials design (\AlgName), a unified framework that couples the scientific knowledge embedded in large language models with chemistry-informed evolutionary rules and memory-based refinement. At each iteration, an LLM proposes crystallographically specified candidates under explicit property constraints; a surrogate-augmented oracle estimates physicochemical properties; and a multi-objective scorer updates success/failure memories to guide subsequent generations. Evaluated on \textbf{14} realistic tasks spanning electronics, energy, coatings, optics, and aerospace, \AlgName discovers candidates that are chemically plausible, thermodynamically stable, and property-aligned, achieving higher hit-rates and stronger Pareto fronts than generative and LLM-only baselines. Ablation studies confirm the importance of rule-guided generation, memory-based refinement, and surrogate prediction. By enforcing synthesizability and multi-objective trade-offs, \AlgName delivers a principled pathway to accelerate practical materials discovery. \\
Code: \url{https://github.com/scientific-discovery/LLEMA}
\end{abstract}

\section{Introduction}
\input{sections/introduction}

\section{\AlgName Method}
\input{sections/methodology}

\vspace{-0.1in}
\section{Experiments}
\vspace{-0.1in}
\input{sections/results}

\vspace{-0.1in}
\section{Analysis}
\input{sections/ablation}

\section{Related Work}
\input{sections/related_works}

\section{Conclusion}
\input{sections/conclusion}

\section*{Acknowledgements}

This research was partially supported by the U.S. National Science Foundation (NSF) under Grant No. 2416728. Saaketh Desai is supported in part by the Center for Integrated Nanotechnologies, an Office of Science user facility operated for the U.S. Department of Energy. This article has been authored by an employee of National Technology \& Engineering Solutions of Sandia, LLC under Contract No. DE-NA0003525 with the U.S. Department of Energy (DOE). The employee owns all right, title, and interest in and to the article and is solely responsible for its contents. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this article or allow others to do so, for United States Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan.

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\appendix
\input{appendix/tasks_and_rules}
\input{appendix/dataset}
\input{appendix/llema}
\input{appendix/ablation}
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