Molecular analysis and design using generative artificial intelligence via multi-agent modeling DOI Creative Commons

Isabella Stewart,

Markus J. Buehler

Molecular Systems Design & Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

We report the use of a multiagent generative artificial intelligence framework, X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The model, inspired by biological...

Language: Английский

A generative model for inorganic materials design DOI Creative Commons

Claudio Zeni,

Robert Pinsler, Daniel Zügner

et al.

Nature, Journal Year: 2025, Volume and Issue: 639(8055), P. 624 - 632

Published: Jan. 16, 2025

Language: Английский

Citations

18
Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials DOI Creative Commons
Bohayra Mortazavi

Advanced Energy Materials, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 10, 2024

Abstract This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification candidates with desirable properties. Recently, development highly accurate interatomic potentials generative models has not only improved robust prediction physical but also significantly accelerated discovery In past couple years, methods have enabled high‐precision first‐principles predictions electronic optical properties for large systems, providing unprecedented opportunities science. Furthermore, ML‐assisted microstructure reconstruction physics‐informed solutions partial differential equations facilitated understanding microstructure–property relationships. Most recently, seamless integration various platforms led emergence autonomous laboratories that combine quantum mechanical calculations, language models, experimental validations, fundamentally transforming traditional approach novel synthesis. While highlighting aforementioned advances, existing challenges are discussed. Ultimately, is expected fully integrate atomic‐scale simulations, reverse engineering, process optimization, device fabrication, empowering system design. will drive transformative innovations conversion, storage, harvesting technologies.

Language: Английский

Citations

17
Large language models for reticular chemistry DOI
Zhiling Zheng, Nakul Rampal,

Theo Jaffrelot Inizan

et al.

Nature Reviews Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Language: Английский

Citations

4
Generative diffusion model for surface structure discovery DOI

Nikolaj Rønne,

Alán Aspuru‐Guzik, Bjørk Hammer

et al.

Physical review. B./Physical review. B, Journal Year: 2024, Volume and Issue: 110(23)

Published: Dec. 24, 2024

Language: Английский

Citations

4
Applied Artificial Intelligence in Materials Science and Material Design DOI Creative Commons
Emigdio Chávez‐Ángel, Martin Eriksen, Alejandro Castro‐Álvarez

et al.

Advanced Intelligent Systems, Journal Year: 2025, Volume and Issue: unknown

Published: March 2, 2025

Materials science has traditionally relied on a combination of experimental techniques and theoretical modeling to discover develop new materials with desired properties. However, these processes can be time‐consuming, resource‐intensive, often limited by the complexity material systems. The advent artificial intelligence (AI), particularly machine learning, revolutionized offering powerful tools accelerate discovery, design, characterization novel materials. AI not only enhances predictive properties but also streamlines data analysis in like X‐Ray diffraction, Raman spectroscopy, scanning probe microscopy, electron microscopy. By leveraging large datasets, algorithms identify patterns, reduce noise, predict behavior unprecedented accuracy. In this review, recent advancements applications across various domains science, including synchrotron studies, microscopies, metamaterials, atomistic modeling, molecular drug are highlighted. It is discussed how AI‐driven methods reshaping field, making discovery more efficient, paving way for breakthroughs design real‐time analysis.

Language: Английский

Citations

0
Advances in high-pressure materials discovery enabled by machine learning DOI Creative Commons
Zhenyu Wang, Xiaoshan Luo, Q. Wang

et al.

Matter and Radiation at Extremes, Journal Year: 2025, Volume and Issue: 10(3)

Published: March 14, 2025

Crystal structure prediction (CSP) is a foundational computational technique for determining the atomic arrangements of crystalline materials, especially under high-pressure conditions. While CSP plays critical role in materials science, traditional approaches often encounter significant challenges related to efficiency and scalability, particularly when applied complex systems. Recent advances machine learning (ML) have shown tremendous promise addressing these limitations, enabling rapid accurate crystal structures across wide range chemical compositions external This review provides concise overview recent progress ML-assisted methodologies, with particular focus on potentials generative models. By critically analyzing advances, we highlight transformative impact ML accelerating discovery, enhancing efficiency, broadening applicability CSP. Additionally, discuss emerging opportunities this rapidly evolving field.

Language: Английский

Citations

0
Machine Learning-Driven Web Tools for Predicting Properties of Materials and Molecules DOI
Dmitriy M. Makarov, Pavel S. Bocharov,

Michail M. Lukanov

et al.

Challenges and advances in computational chemistry and physics, Journal Year: 2025, Volume and Issue: unknown, P. 273 - 292

Published: Jan. 1, 2025

Language: Английский

Citations

0
A Perspective on Foundation Models in Chemistry DOI Creative Commons
Junyoung Choi,

Gunwook Nam,

Jaesik Choi

et al.

JACS Au, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Foundation models are an emerging paradigm in artificial intelligence (AI), with successful examples like ChatGPT transforming daily workflows. Generally, foundation large-scale, pretrained capable of adapting to various downstream tasks by leveraging extensive data and model scaling. Their success has inspired researchers develop for a wide range chemical challenges, from materials discovery understanding structure-property relationships, areas where conventional machine learning (ML) often face limitations. In addition, hold promise addressing persistent ML challenges chemistry, such as scarcity poor generalization. this perspective, we review recent progress the development chemistry across applications varying scope. We also discuss trends provide outlook on promising approaches advancing chemistry.

Language: Английский

Citations

0
Generative deep learning for predicting ultrahigh lattice thermal conductivity materials DOI Creative Commons

Liangshuai Guo,

Yuanbin Liu, Zekun Chen

et al.

npj Computational Materials, Journal Year: 2025, Volume and Issue: 11(1)

Published: April 11, 2025

Language: Английский

Citations

0
A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes DOI Creative Commons

Magnus Strandgaard,

Trond Linjordet, Hannes Kneiding

et al.

JACS Au, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Language: Английский

Citations

0