The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture.

Direct air capture (DAC) of CO2 with porous adsorbents such as metal-organic frameworks (MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for DAC relied on empirical force fields and ignored adsorbed H2O and MOF deformation. We performed quantum chemistry calculations overcoming these restrictions for thousands of MOFs. The resulting data enable efficient descriptions using machine learning.

Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal-organic frameworks.

Energy-related descriptors in machine learning are a promising strategy to predict adsorption properties of metal-organic frameworks (MOFs) in the low-pressure regime. Interactions between hosts and guests in these systems are typically expressed as a sum of dispersion and electrostatic potentials. The energy landscape of dispersion potentials plays a crucial role in defining Henry's constants for simple probe molecules in MOFs. To incorporate more information about this energy landscape, we introduce the Gaussian-approximated Lennard-Jones (GALJ) potential, which fits pairwise Lennard-Jones potentials with multiple Gaussians by varying their heights and widths. The GALJ approach is capable of replicating information that can be obtained from the original LJ potentials and enables efficient development of Gaussian integral (GI) descriptors that account for spatial correlations in the dispersion energy environment. GI descriptors would be computationally inconvenient to compute using the usual direct evaluation of the dispersion potential energy surface. We show that these new GI descriptors lead to improvement in ML predictions of Henry's constants for a diverse set of adsorbates in MOFs compared to previous approaches to this task.

Perioperative symptomatic venous thromboembolism after immediate chemoprophylaxis in patients with pelvic and lower-extremity fractures.

The purpose of this study was to investigate the incidence of symptomatic venous thromboembolism (VTE) after chemoprophylaxis in patients with pelvic and lower-extremity fractures, and to identify risk factors for VTEs in this subgroup of patients. To detect VTE, multi-detector computed tomography (CT) angiography was performed. Of 363 patients assessed, the incidence of symptomatic VTE was 12.4% (45 patients), and the incidence of symptomatic PE was 5.2% (19 patients). For the risk-factor analysis, a higher Charlson comorbidity index (p = 0.037), and a history of external fixator application (p = 0.007) were associated with increased VTE risk. Among patients who had VTE, male sex (p = 0.017), and above-the-knee fractures (p = 0.035) were associated with increased pulmonary embolism (PE) risk. In conclusions, the incidence of VTE in post-traumatic patients is not low after chemoprophylaxis. Risk factors for VTE and PE are different among patients with pelvic and lower-extremity fractures.

Text Mining Metal-Organic Framework Papers.

We have developed a simple text mining algorithm that allows us to identify surface area and pore volumes of metal-organic frameworks (MOFs) using manuscript html files as inputs. The algorithm searches for common units (e.g., m2/g, cm3/g) associated with these two quantities to facilitate the search. From the sample set data of over 200 MOFs, the algorithm managed to identify 90% and 88.8% of the correct surface area and pore volume values. Further application to a test set of randomly chosen MOF html files yielded 73.2% and 85.1% accuracies for the two respective quantities. Most of the errors stem from unorthodox sentence structures that made it difficult to identify the correct data as well as bolded notations of MOFs (e.g., 1a) that made it difficult identify its real name. These types of tools will become useful when it comes to discovering structure-property relationships among MOFs as well as collecting a large set of data for references.