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We present an investigation of 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 ∼10× 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 >2× speedup to the DFT relaxation when compared to initializing at structures relaxed with a classical force field., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Recent measurements [Xu, J.; J. Phys. Chem. Lett. 2019, 10 (22), 7044-7049] have reported temperature-dependent rates of detachment of diamine from Mg sites in diamine-appended Mg 2 (dobpdc) [dobpdc 4- = 4,4'-dihydroxy(1,1'-biphenyl)-3,3'-dicarboxylic] metal-organic frameworks, a process hypothesized to be a precursor for cooperative CO 2 adsorption, leading to step-shaped isotherms or isobars. Here, we compute the rate of diamine exchange in this system for different diamines using metadynamics simulations based on a density functional theory-derived neural network potential. Reanalyzing recent measurements accounting for entropic effects, we find a positive correlation between the previously reported CO 2 adsorption step pressure and the free energy at room temperature and show that the experiments and simulations are in good qualitative and quantitative agreement. The rates obtained here provide new insight into the chemical dynamics of CO 2 adsorption in a class of materials that are promising for carbon capture and a lower bound on the time scale of cooperative adsorption.

Low carbon fuel and waste management policies at the federal and state levels have catalyzed the construction of California's 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 CO 2 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 CO 2 captured. Adding CCS to CNG-producing systems ( Biogas to CNG ) only requires a single unit operation to prepare the CO 2 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.

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 N 2 -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 O 2 -selective adsorbents may facilitate more widespread adoption of adsorptive air separations, which could enable the decentralization of O 2 production and utilization and advance new uses for O 2 . Here, we present a detailed evaluation of the potential of metal-organic frameworks (MOFs) to serve as O 2 -selective adsorbents for air separations. Drawing insights from biological and molecular systems that selectively bind O 2 , we survey the field of O 2 -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 O 2 adsorption enthalpy, Δ H , is emphasized, and the free energy of O 2 adsorption, Δ G , is discussed as the key metric for understanding and predicting MOF performance under practical conditions. Based on a proof-of-concept assessment of O 2 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 Δ G 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., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)