ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks

ChatMOF is an autonomous Artificial Intelligence (AI) system that is built to predict and generate metal-organic frameworks (MOFs). By leveraging a large-scale language model (GPT-4 and GPT-3.5-turbo), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid structured queries. The system is comprised of three core components (i.e. an agent, a toolkit, and an evaluator) and it forms a robust pipeline that manages a variety of tasks, including data retrieval, property prediction, and structure generations. The study further explores the merits and constraints of using large language models (LLMs) AI system in material sciences using and showcases its transformative potential for future advancements

Energy-efficient CO2 hydrogenation with fast response using photoexcitation of CO2 adsorbed on metal catalysts.

Many heterogeneous catalytic reactions occur at high temperatures, which may cause large energy costs, poor safety, and thermal degradation of catalysts. Here, we propose a light-assisted surface reaction, which catalyze the surface reaction using both light and heat as an energy source. Conventional metal catalysts such as ruthenium, rhodium, platinum, nickel, and copper were tested for CO2 hydrogenation, and ruthenium showed the most distinct change upon light irradiation. CO2 was strongly adsorbed onto ruthenium surface, forming hybrid orbitals. The band gap energy was reduced significantly upon hybridization, enhancing CO2 dissociation. The light-assisted CO2 hydrogenation used only 37% of the total energy with which the CO2 hydrogenation occurred using only thermal energy. The CO2 conversion could be turned on and off completely with a response time of only 3 min, whereas conventional thermal reaction required hours. These unique features can be potentially used for on-demand fuel production with minimal energy input

MatGD: Materials Graph Digitizer

We have developed MatGD (Material Graph Digitizer), which is a tool for digitizing a data line from scientific graphs. The algorithm behind the tool consists of four steps: (1) identifying graphs within subfigures, (2) separating axes and data sections, (3) discerning the data lines by eliminating irrelevant graph objects and matching with the legend, and (4) data extraction and saving. From the 62,534 papers in the areas of batteries, catalysis, and MOFs, 501,045 figures were mined. Remarkably, our tool showcased performance with over 99% accuracy in legend marker and text detection. Moreover, its capability for data line separation stood at 66%, which is much higher compared to other existing figure mining tools. We believe that this tool will be integral to collecting both past and future data from publications, and these data can be used to train various machine learning models that can enhance material predictions and new materials discovery.Comment: 23 pages, 4 figure

Understanding metastatic growth through the traction force of human breast cancer cells

Cancer is a hard disease to control because of its ability to spread to different parts of the body. Cancer cells can spread out from a primary tumor to distant organs which gives rise to new cancer cell colonies and more tumors over time. This process is known as metastasis and causes over 90% of human cancer deaths. Cell traction force (CTF) coordinates many cellular functions such as cell migration. Cell migration plays a critical role in the spread of cancer. Therefore, CTF plays a significant role the the biological process of metastasis. The goal of this experiment is to simultaneously measure the deformation field around the cells and cellular responses between two different substrate rigidities. Past experiments have been mostly done with different substrate rigidities separately. However, it is more realistic to measure cellular contractility with two different substrate rigidities simultaneously. These conditions better reflect physiological conditions because there are varying rigidities within the human body

Capturing Nucleation at 4D Atomic Resolution

Nucleation plays a critical role in many physical and biological phenomena ranging from crystallization, melting and evaporation to the formation of clouds and the initiation of neurodegenerative diseases. However, nucleation is a challenging process to study in experiments especially in the early stage when several atoms/molecules start to form a new phase from its parent phase. Here, we advance atomic electron tomography to study early stage nucleation at 4D atomic resolution. Using FePt nanoparticles as a model system, we reveal that early stage nuclei are irregularly shaped, each has a core of one to few atoms with the maximum order parameter, and the order parameter gradient points from the core to the boundary of the nucleus. We capture the structure and dynamics of the same nuclei undergoing growth, fluctuation, dissolution, merging and/or division, which are regulated by the order parameter distribution and its gradient. These experimental observations differ from classical nucleation theory (CNT) and to explain them we propose the order parameter gradient (OPG) model. We show the OPG model generalizes CNT and energetically favours diffuse interfaces for small nuclei and sharp interfaces for large nuclei. We further corroborate this model using molecular dynamics simulations of heterogeneous and homogeneous nucleation in liquid-solid phase transitions of Pt. We anticipate that the OPG model is applicable to different nucleation processes and our experimental method opens the door to study the structure and dynamics of materials with 4D atomic resolution.Comment: 42 pages, 5 figures, 12 supplementary figures and one supplementary tabl

High-throughput Characterization of Porous Materials Using Graphics Processing Units

We have developed a high-throughput graphics processing units (GPU) code that can characterize a large database of crystalline porous materials. In our algorithm, the GPU is utilized to accelerate energy grid calculations where the grid values represent interactions (i.e., Lennard-Jones + Coulomb potentials) between gas molecules (i.e., CH$_{4}$ and CO$_{2}$) and material's framework atoms. Using a parallel flood fill CPU algorithm, inaccessible regions inside the framework structures are identified and blocked based on their energy profiles. Finally, we compute the Henry coefficients and heats of adsorption through statistical Widom insertion Monte Carlo moves in the domain restricted to the accessible space. The code offers significant speedup over a single core CPU code and allows us to characterize a set of porous materials at least an order of magnitude larger than ones considered in earlier studies. For structures selected from such a prescreening algorithm, full adsorption isotherms can be calculated by conducting multiple grand canonical Monte Carlo simulations concurrently within the GPU