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
Medicine Advances, Journal Year: 2025, Volume and Issue: unknown
Published: March 3, 2025
Language: Английский
Citations
3
Advanced Functional Materials, Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 5, 2024
Abstract Cephalo is presented as a series of multimodal vision large language models (V‐LLMs) designed for materials science applications, integrating visual and linguistic data enhanced understanding. A key innovation its advanced dataset generation method. trained on integrated image text from thousands scientific papers science‐focused Wikipedia demonstrates it can interpret complex scenes, generate precise descriptions, answer queries about images effectively. The combination encoder with an autoregressive transformer supports natural understanding, which be coupled other generative methods to create image‐to‐text‐to‐3D pipeline. To develop more capable smaller ones, both mixture‐of‐expert model merging are reported. examined in diverse use cases that incorporate biological materials, fracture engineering analysis, protein biophysics, bio‐inspired design based insect behavior. Generative applications include designs, including pollen‐inspired architected well the synthesis material microstructures photograph solar eclipse. Additional fine‐tuning molecular dynamics results demonstrate Cephalo's capabilities accurately predict statistical features stress atomic energy distributions, crack damage materials.
Language: Английский
Citations
8
Vicinagearth., Journal Year: 2024, Volume and Issue: 1(1)
Published: Oct. 8, 2024
Abstract The pursuit of more intelligent and credible autonomous systems, akin to human society, has been a long-standing endeavor for humans. Leveraging the exceptional reasoning planning capabilities large language models (LLMs), LLM-based agents have proposed achieved remarkable success across wide array tasks. Notably, multi-agent systems (MAS) are considered promising pathway towards realizing general artificial intelligence that is equivalent or surpasses human-level intelligence. In this paper, we present comprehensive survey these studies, offering systematic review MAS. Adhering workflow synthesize structure encompassing five key components: profile, perception, self-action, mutual interaction, evolution. This unified framework encapsulates much previous work in field. Furthermore, illuminate extensive applications MAS two principal areas: problem-solving world simulation. Finally, discuss detail several contemporary challenges provide insights into potential future directions domain.
Language: Английский
Citations
8
Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(20), P. 7895 - 7904
Published: Oct. 8, 2024
The advent of generative Large Language Models (LLMs) has greatly impacted the field Natural Processing. However, it is inconclusive how LLMs perform on domain-specific information extraction tasks. This study compares performance GPT-4 and a rule-based method based ChemDataExtractor band gap extraction, task that important implications for materials science domain. No training data required either method, which desirable because there lack in domain compared with variety material interest. Manual evaluation 415 randomly selected articles showed model achieved higher level accuracy extracting materials' than (Correctness 87.95% vs 51.08%, Partial correctness 11.33% 36.87%, incorrectness 0.72% 12.05%). Further analysis errors reveals strengths weaknesses to method. shows stronger interdependency resolution complicated name recognition, while also hallucination, identifying values, types. Revised prompt error leads improved GPT-4. To best our knowledge, this first compare task. provides evidence support using
Language: Английский
Citations
4
Aging, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 16, 2025
With the global population aging at an unprecedented rate, there is a need to extend healthy productive life span. This review examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) are used in biomarker discovery, deep clock development, geroprotector identification generation of dual-purpose therapeutics targeting disease. The paper explores emergence multimodal, multitasking research systems highlighting promising future directions for GenAI human animal research, as well clinical application longevity medicine.
Language: Английский
Citations
0
Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(4)
Published: Jan. 24, 2025
The design of new alloys is a multiscale problem that requires holistic approach involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, process typically slow reserved for human experts. Machine learning can help accelerate this process, instance, through use deep surrogate models connect structural chemical features to material properties, or vice versa. However, existing data-driven often target specific objectives, offering limited flexibility integrate out-of-domain knowledge cannot adapt new, unforeseen challenges. Here, we overcome these limitations by leveraging distinct capabilities multiple AI agents collaborate autonomously within dynamic environment solve complex materials tasks. proposed physics-aware generative platform, AtomAgents, synergizes intelligence large language (LLMs) collaboration among with expertise in various domains, including retrieval, multimodal data integration, physics-based simulations, comprehensive results analysis across modalities. concerted effort multiagent system allows addressing problems, as demonstrated examples include designing metallic enhanced properties compared their pure counterparts. Our enable accurate prediction key characteristics highlight crucial role solid solution alloying steer development alloys. framework enhances efficiency multiobjective tasks opens avenues fields such biomedical engineering, renewable energy, environmental sustainability.
Language: Английский
Citations
0
Materials Today Communications, Journal Year: 2025, Volume and Issue: unknown, P. 112186 - 112186
Published: March 1, 2025
Language: Английский
Citations
0
Science Advances, Journal Year: 2025, Volume and Issue: 11(13)
Published: March 26, 2025
Peptides are ubiquitous and important biomolecules that self-assemble into diverse structures. Although extensive research has explored the effects of chemical composition exterior conditions on self-assembly, a systematic study consolidating these data to uncover global rules is lacking. In this work, we curate peptide assembly database through combination manual processing by human experts large language model–assisted literature mining. As result, collect over 1000 experimental entries with information about sequence, conditions, corresponding self-assembly phases. Using data, machine learning models developed, demonstrating excellent accuracy (>80%) in phase classification. Moreover, fine-tune GPT model for mining developed dataset, which markedly outperforms pretrained extracting from academic publications. This workflow can improve efficiency when exploring potential self-assembling candidates, guiding while also deepening our understanding governing mechanisms.
Language: Английский
Citations
0
BioScience, Journal Year: 2025, Volume and Issue: unknown
Published: March 28, 2025
Abstract Natural history collections play a crucial role in our understanding of biodiversity, informing research, management, and policy areas such as biosecurity, conservation, climate change, food security. However, the growing volume specimens associated data presents significant challenges for curation management. By leveraging human–AI collaborations, we aim to transform way biological are curated managed, realizing their full potential addressing global challenges. In this article, discuss vision improving management using collaboration. We explore rationale behind approach, faced general problems, benefits that could be derived from incorporating AI-based assistants collection teams. Finally, examine future possibilities collaborations between human digital curators collection-based research.
Language: Английский
Citations
0
Advanced Materials, Journal Year: 2024, Volume and Issue: unknown
Published: Dec. 18, 2024
Abstract A key challenge in artificial intelligence (AI) is the creation of systems capable autonomously advancing scientific understanding by exploring novel domains, identifying complex patterns, and uncovering previously unseen connections vast data. In this work, SciAgents, an approach that leverages three core concepts presented: (1) large‐scale ontological knowledge graphs to organize interconnect diverse concepts, (2) a suite large language models (LLMs) data retrieval tools, (3) multi‐agent with in‐situ learning capabilities. Applied biologically inspired materials, SciAgents reveals hidden interdisciplinary relationships were considered unrelated, achieving scale, precision, exploratory power surpasses human research methods. The framework generates refines hypotheses, elucidating underlying mechanisms, design principles, unexpected material properties. By integrating these capabilities modular fashion, system yields discoveries, critiques improves existing retrieves up‐to‐date about research, highlights strengths limitations. This achieved harnessing “swarm intelligence” similar biological systems, providing new avenues for discovery. How model accelerates development advanced materials unlocking Nature's resulting biocomposite enhanced mechanical properties improved sustainability through energy‐efficient production shown.
Language: Английский
Citations
3