Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations

We present an investigation into diffusion models for molecular generation, with the aim of better understanding how their predictions compare to the results of physics-based calculations. The investigation into these models is driven by their potential to significantly accelerate electronic structure calculations using machine learning, without requiring expensive first-principles datasets for training interatomic potentials. We find that the inference process of a popular diffusion model for de novo molecular generation is divided into an exploration phase, where the model chooses the atomic species, and a relaxation phase, where it adjusts the atomic coordinates to find a low-energy geometry. As training proceeds, we show that the model initially learns about the first-order structure of the potential energy surface, and then later learns about higher-order structure. We also find that the relaxation phase of the diffusion model can be re-purposed to sample the Boltzmann distribution over conformations and to carry out structure relaxations. For structure relaxations, the model finds geometries with ~10x lower energy than those produced by a classical force field for small organic molecules. Initializing a density functional theory (DFT) relaxation at the diffusion-produced structures yields a >2x speedup to the DFT relaxation when compared to initializing at structures relaxed with a classical force field

Erythrocyte and Porcine Intestinal Glycosphingolipids Recognized by F4 Fimbriae of Enterotoxigenic Escherichia coli

Enterotoxigenic F4-fimbriated Escherichia coli is associated with diarrheal disease in neonatal and postweaning pigs. The F4 fimbriae mediate attachment of the bacteria to the pig intestinal epithelium, enabling an efficient delivery of diarrhea-inducing enterotoxins to the target epithelial cells. There are three variants of F4 fimbriae designated F4ab, F4ac and F4ad, respectively, having different antigenic and adhesive properties. In the present study, the binding of isolated F4ab, F4ac and F4ad fimbriae, and F4ab/ac/ad-fimbriated E. coli, to glycosphingolipids from erythrocytes and from porcine small intestinal epithelium was examined, in order to get a comprehensive view of the F4-binding glycosphingolipids involved in F4-mediated hemagglutination and adhesion to the epithelial cells of porcine intestine. Specific interactions between the F4ab, F4ac and F4ad fimbriae and both acid and non-acid glycosphingolipids were obtained, and after isolation of binding-active glycosphingolipids and characterization by mass spectrometry and proton NMR, distinct carbohydrate binding patterns were defined for each fimbrial subtype. Two novel glycosphingolipids were isolated from chicken erythrocytes, and characterized as GalNAcα3GalNAcß3Galß4Glcß1Cer and GalNAcα3GalNAcß3Galß4GlcNAcß3Galß4Glcß1Cer. These two compounds, and lactosylceramide (Galß4Glcß1Cer) with phytosphingosine and hydroxy fatty acid, were recognized by all three variants of F4 fimbriae. No binding of the F4ad fimbriae or F4ad-fimbriated E. coli to the porcine intestinal glycosphingolipids occurred. However, for F4ab and F4ac two distinct binding patterns were observed. The F4ac fimbriae and the F4ac-expressing E. coli selectively bound to galactosylceramide (Galß1Cer) with sphingosine and hydroxy 24:0 fatty acid, while the porcine intestinal glycosphingolipids recognized by F4ab fimbriae and the F4ab-fimbriated bacteria were characterized as galactosylceramide, sulfatide (SO3-3Galß1Cer), sulf-lactosylceramide (SO3-3Galß4Glcß1Cer), and globotriaosylceramide (Galα4Galß4Glcß1Cer) with phytosphingosine and hydroxy 24:0 fatty acid. Finally, the F4ad fimbriae and the F4ad-fimbriated E. coli, but not the F4ab or F4ac subtypes, bound to reference gangliotriaosylceramide (GalNAcß4Galß4Glcß1Cer), gangliotetraosylceramide (Galß3GalNAcß4Galß4Glcß1Cer), isoglobotriaosylceramide (Galα3Galß4Glcß1Cer), and neolactotetraosylceramide (Galß4GlcNAcß3Galß4Glcß1Cer)

Accelerated Discovery of CH4 Uptake Capacity MOFs using Bayesian Optimization

High-throughput computational studies for discovery of metal-organic frameworks (MOFs) for separations and storage applications are often limited by the costs of computing thermodynamic quantities, with recent studies reliant ab initio results for a narrow selection of MOFs and empirical force-field methods for larger selections. Here, we conduct a proof-of-concept study using Bayesian optimization on CH4 uptake capacity of hypothetical MOFs for an existing dataset (Wilmer et al, Nature Chem. 2012, 4, 83). We show that less than 0.1% of the database needs to be screened with our Bayesian optimization approach to recover the top candidate MOFs. This opens the possibility of efficient screening of MOF databases using accurate ab-initio calculations for future adsorption studies on a minimal subset of MOFs. Furthermore, Bayesian optimization and the surrogate model presented here can offer interpretable material design insights and our framework will be applicable in the context of other target properties

Temporal Bayesian Networks

Temporal formalisms are useful in several applications such as planning, scheduling and diagnosis. Probabilistic temporal reasoning emerged to deal with the uncertainties usually encountered in such applications. Bayesian networks provide a simple compact graphical representation of a probability distribution by exploiting conditional independencies. This paper presents a simple technique for representing time in Bayesian networks by expressing probabilities as functions of time. Probability transfer functions allow the formalism to deal with causal relations and dependencies between time points. Techniques to represent related time instants are distinct from those used to represent independent time instants but the probabilistic formalism is useful in both cases. The study of the cumulative e ect of repeated events involves various models such as the competing risks model and the additive model. Dynamic Bayesian networks inference mechanisms are adequate for temporal probabilistic reasoning described in this work. Examples from medical diagnosis, circuit diagnosis and common sense reasoning help illustrate the use of these techniques.

Policy Analysis of CO2 Capture and Sequestration with Anaerobic Digestion for Transportation Fuel Production.

Low carbon fuel and waste management policies at the federal and state levels have catalyzed the construction of Californias wet anaerobic digestion (AD) facilities. Wet ADs can digest food waste and dairy manure to produce compressed natural gas (CNG) for natural gas vehicles or electricity for electric vehicles (EVs). Carbon capture and sequestration (CCS) of CO2 generated from AD reduces the fuel carbon intensity by carbon removal in addition to avoided methane emissions. Using a combined lifecycle and techno-economic analysis, we determine the most cost-effective design under current and forthcoming federal and state low carbon fuel policies. Under many scenarios, designs that convert biogas to electricity for EVs (Biogas to EV) are favored; however, CCS is only cost-effective in these systems with policy incentives that exceed $200/tonne of CO2 captured. Adding CCS to CNG-producing systems (Biogas to CNG) only requires a single unit operation to prepare the CO2 for sequestration, with a sequestration cost of$34/tonne. When maximizing negative emissions is the goal, incentives are needed to either (1) fund CCS with Biogas to EV designs or (2) favor CNG over electricity production from wet AD facilities

Higher-order equivariant neural networks for charge density prediction in materials

Abstract The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-standing challenge. We introduce ChargE3Net, an E(3)-equivariant graph neural network for predicting electron density in atomic systems. ChargE3Net enables the learning of higher-order equivariant features to achieve high predictive accuracy and model expressivity. We show that ChargE3Net exceeds the performance of prior work on diverse sets of molecules and materials. When trained on the massive dataset of over 100K materials in the Materials Project database, our model is able to capture the complexity and variability in the data, leading to a significant 26.7% reduction in self-consistent iterations when used to initialize DFT calculations on unseen materials. Furthermore, we show that non-self-consistent DFT calculations using our predicted charge densities yield near-DFT performance on electronic and thermodynamic property prediction at a fraction of the computational cost. Further analysis attributes the greater predictive accuracy to improved modeling of systems with high angular variations. These results illuminate a pathway towards a machine learning-accelerated ab initio calculations for materials discovery

Metal-organic frameworks as O2-selective adsorbents for air separations.

Oxygen is a critical gas in numerous industries and is produced globally on a gigatonne scale, primarily through energy-intensive cryogenic distillation of air. The realization of large-scale adsorption-based air separations could enable a significant reduction in associated worldwide energy consumption and would constitute an important component of broader efforts to combat climate change. Certain small-scale air separations are carried out using N2-selective adsorbents, although the low capacities, poor selectivities, and high regeneration energies associated with these materials limit the extent of their usage. In contrast, the realization of O2-selective adsorbents may facilitate more widespread adoption of adsorptive air separations, which could enable the decentralization of O2 production and utilization and advance new uses for O2. Here, we present a detailed evaluation of the potential of metal-organic frameworks (MOFs) to serve as O2-selective adsorbents for air separations. Drawing insights from biological and molecular systems that selectively bind O2, we survey the field of O2-selective MOFs, highlighting progress and identifying promising areas for future exploration. As a guide for further research, the importance of moving beyond the traditional evaluation of O2 adsorption enthalpy, ΔH, is emphasized, and the free energy of O2 adsorption, ΔG, is discussed as the key metric for understanding and predicting MOF performance under practical conditions. Based on a proof-of-concept assessment of O2 binding carried out for eight different MOFs using experimentally derived capacities and thermodynamic parameters, we identify two existing materials and one proposed framework with nearly optimal ΔG values for operation under user-defined conditions. While enhancements are still needed in other material properties, the insights from the assessments herein serve as a guide for future materials design and evaluation. Computational approaches based on density functional theory with periodic boundary conditions are also discussed as complementary to experimental efforts, and new predictions enable identification of additional promising MOF systems for investigation

Hybrid Approach for Selective Sulfoxidation via Bioelectrochemically Derived Hydrogen Peroxide over a Niobium(V)–Silica Catalyst

In this work, we demonstrate a combined bioelectrochemical and inorganic catalytic system for resource recovery from wastewater. We designed a microbial peroxide producing cell (MPPC) for hydrogen peroxide (H₂O₂) production and used this bioelectrochemically derived H₂O₂ as a green oxidant for sulfoxidation, an industrial reaction used for chemical synthesis and oxidative desulfurization of transportation fuels. We operated an MPPC equipped with a gas diffusion electrode cathode for six months, achieving a peak current density above 1.4 mA cm^–2 with 60% average acetate removal and 61% average anodic Coulombic efficiency. We evaluated several cathode buffers under batch and continuous-flow conditions for solubility and pH compatibility with downstream catalytic systems. During 24-h batch tests, a phosphate-buffered MPPC achieved a maximum H₂O₂ concentration of 4.6 g L^–1 and a citric acid–phosphate-buffered MPPC obtained a moderate H₂O₂ concentration (3.1 g L^–1) at a low energy input (1.6 Wh g^–1 H₂O₂) and pH (10). The MPPC-derived H₂O₂ was used directly as an oxidant for the catalytic sulfoxidation of 4-hydroxythioanisole over a solid niobium­(V)–silica catalyst. We achieved 82% conversion of 50 mM 4-hydroxythioanisole to 4-(methylsulfinyl)­phenol with 99% selectivity with a 0.5 mol % catalyst loading in 100 min in aqueous media. Our results demonstrate a new and versatile approach for valorization of wastewater through continuous production of H₂O₂ and its subsequent use as a selective green oxidant in aqueous conditions for green chemistry applications