Yang Jeong Park

We investigate whether large language models can perform the creative hypothesis generation that human researchers regularly do. While the error rate is high, generative AI seems to be able to effectively structure vast amounts of scientific knowledge and provide interesting and testable hypotheses. The future scientific enterprise may include synergistic efforts with a swarm of “hypothesis machines”, challenged by automated experimentation and adversarial peer reviews.

Coupled electrochemical systems for the direct capture and conversion of CO2 have garnered significant attention owing to their potential to enhance energy- and cost-efficiency by circumventing the amine regeneration step. However, optimizing the coupled system is more challenging than handling separated systems because of its complexity, caused by the incorporation of solvent and heterogeneous catalysts. Nevertheless, the deployment of machine learning can be immensely beneficial, reducing both

Metal-organic frameworks (MOFs) are attractive materials with easily tunable porous structures. Their selective carbon dioxide (CO 2 ) capture ability can be varied by altering the functionality of the organic ligands. However, rule-based approaches to tuning and developing MOFs with high CO 2 capture and conversion abilities are hindered by the numerous possible combinations of metal ions and organic linkers. Recently, machine learning (ML) has been applied to unravel key descriptors in predict

We investigate whether large language models can perform the creative hypothesis generation that human researchers regularly do. While the error rate is high, generative AI seems to be able to effectively structure vast amounts of scientific knowledge and provide interesting and testable hypotheses. The future scientific enterprise may include synergistic efforts with a swarm of "hypothesis machines", challenged by automated experimentation and adversarial peer reviews.

The advent of artificial intelligence (AI) has enabled a comprehensive exploration of materials for various applications. However, AI models often prioritize frequently encountered material examples in the scientific literature, limiting the selection of suitable candidates based on inherent physical and chemical attributes. To address this imbalance, we generated a dataset consisting of 1,453,493 natural language-material narratives from OQMD, Materials Project, JARVIS, and AFLOW2 databases bas

Artificial intelligence (AI) is increasingly used for the inverse design of materials, such as crystals and molecules. Existing AI research on molecules has integrated chemical structures of molecules with textual knowledge to adapt to complex instructions. However, this approach has been unattainable for crystals due to data scarcity from the biased distribution of investigated crystals and the lack of semantic supervision in peer-reviewed literature. In this work, we introduce a contrastive la

The advent of artificial intelligence (AI) has enabled a comprehensive exploration of materials for various applications. However, AI models often prioritize frequently encountered materials in the scientific literature, limiting the selection of suitable candidates based on inherent physical and chemical properties. To address this imbalance, we have generated a dataset of 1,494,017 natural language-material paragraphs based on combined OQMD, Materials Project, JARVIS, COD and AFLOW2 databases,

Abstract Large-scale foundation models, including universal neural network interatomic potentials (NIPs) in computational materials science, have demonstrated significant progress. However, despite their success in accelerating atomistic simulations, NIPs still face challenges in modeling certain property classes. Machine learning (ML) offers alternatives for structure-to-property mapping but different ML approaches present distinct trade-offs: feature-based methods often lack generalizability,

Since ancient times, oracles (e.g., Delphi) has the ability to provide useful visions of where the society is headed, based on key event correlations and educated guesses. Currently, foundation models are able to distill and analyze enormous text‐based data that can be used to understand where societal components are headed in the future. This work investigates the use of three large language models (LLM) and their ability to aid the research of nuclear materials. Using a large dataset of Journa

Interfacial engineering is essential for improving charge extraction and suppressing non-radiative recombination in perovskite solar cells (PSCs). Although numerous organic interfacial materials (IMs) have been explored, the vast molecular design space renders purely experimental screening inefficient. Here, we report on a machine learning-based framework that rapidly screens IMs using an in-house database. Six physicochemical descriptors capturing perovskite-molecule interactions were selected

Cellular force transmission across a hierarchy of molecular switchers is central to mechanobiological responses. However, current cellular force microscopies suffer from low throughput and resolution. Here we introduce and train a generative adversarial network (GAN) to paint out traction force maps of cell monolayers with high fidelity to the experimental traction force microscopy (TFM). The GAN analyzes traction force maps as an image-to-image translation problem, where its generative and disc