Chemical Properties from Graph Neural Network-Predicted Electron Densities

According to density functional theory, any chemical property can be inferred from the electron density, making it the most informative attribute of an atomic structure. In this work, we demonstrate the use of established physical methods to obtain important chemical properties from model-predicted electron densities. We introduce graph neural network architectural choices that provide physically relevant and useful electron density predictions. Despite not training to predict atomic charges, the model is able to predict atomic charges with an order of magnitude lower error than a sum of atomic charge densities. Similarly, the model predicts dipole moments with half the error of the sum of atomic charge densities method. We demonstrate that larger data sets lead to more useful predictions in these tasks. These results pave the way for an alternative path in atomistic machine learning, where data-driven approaches and existing physical methods are used in tandem to obtain a variety of chemical properties in an explainable and self-consistent manner

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains. To address this, we introduce Joint Multi-domain Pre-training (JMP), a supervised pre-training strategy that simultaneously trains on multiple datasets from different chemical domains, treating each dataset as a unique pre-training task within a multi-task framework. Our combined training dataset consists of $\sim$120M systems from OC20, OC22, ANI-1x, and Transition-1x. We evaluate performance and generalization by fine-tuning over a diverse set of downstream tasks and datasets including: QM9, rMD17, MatBench, QMOF, SPICE, and MD22. JMP demonstrates an average improvement of 59% over training from scratch, and matches or sets state-of-the-art on 34 out of 40 tasks. Our work highlights the potential of pre-training strategies that utilize diverse data to advance property prediction across chemical domains, especially for low-data tasks

Study of an ammonia-based wet scrubbing process in a continuous flow system

A continuous gas and liquid flow, regenerative scrubbing process for CO{sub 2} capture was demonstrated at the bench-scale level. An aqueous ammonia-based solution captures CO{sub 2} from simulated flue gas in an absorber and releases a nearly pure stream of CO{sub 2} in the regenerator. After the regeneration, the solution of ammonium compounds is recycled to the absorber. The design of a continuous flow unit was based on earlier exploratory results from a semi-batch reactor, where a CO{sub 2} and N{sub 2} simulated flue gas mixture flowed through a well-mixed batch of ammonia-based solution. During the semi-batch tests, the solution was cycled between absorption and regeneration steps to measure the carrying capacity of the solution at various initial ammonia concentrations and temperatures. Consequentially, a series of tests were conducted on the continuous unit to observe the effect of various parameters on CO{sub 2} removal efficiency and regenerator effectiveness within the flow system. The parameters that were studied included absorber temperature, regenerator temperature, initial NH{sub 3} concentration, simulated flue gas flow rate, liquid solvent inventory in the flow system, and height of the packed-bed absorber. From this testing and subsequent testing, ammonia losses from both the absorption and regeneration steps were quantified, and attempts were made to maintain steady state during operations. Implications of experimental results with respect to process design are discussed

Alloy surface segregation in reactive environments: A first-principles atomistic thermodynamics study of Ag3Pd(111) in oxygen atmospheres

We present a first-principles atomistic thermodynamics framework to describe the structure, composition and segregation profile of an alloy surface in contact with a (reactive) environment. The method is illustrated with the application to a Ag3Pd(111) surface in an oxygen atmosphere, and we analyze trends in segregation, adsorption and surface free energies. We observe a wide range of oxygen adsorption energies on the various alloy surface configurations, including binding that is stronger than on a Pd(111) surface and weaker than that on a Ag(111) surface. This and the consideration of even small amounts of non-stoichiometries in the ordered bulk alloy are found to be crucial to accurately model the Pd surface segregation occurring in increasingly O-rich gas phases.Comment: 13 pages including 6 figures; related publications can be found at http://www.fhi-berlin.mpg.de/th/th.htm

The atomic simulation environment — a python library for working with atoms

The Atomic Simulation Environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simula- tions. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple "for-loop" construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations

Maps, Memories and Manchester: The Cartographic Imagination of the Hidden Networks of the Hydraulic City

The largely unseen channelling, culverting and controlling of water into, through and out of cities is the focus of our cartographic interpretation. This paper draws on empirical material depicting hydraulic infrastructure underlying the growth of Manchester in mapped form. Focusing, in particular, on the 19th century burst of large-scale hydraulic engineering, which supplied vastly increased amounts of clean drinking water, controlled unruly rivers to eliminate flooding, and safely removed sewage, this paper explores the contribution of mapping to the making of a more sanitary city, and towards bold civic minded urban intervention. These extensive infrastructures planned and engineered during Victorian and Edwardian Manchester are now taken-for-granted but remain essential for urban life. The maps, plans and diagrams of hydraulic Manchester fixed particular forms of elite knowledge (around planning foresight, topographical precision, civil engineering and sanitary science) but also facilitated and freed flows of water throughout the city. The survival of these maps and plans in libraries, technical books and obscure reports allows the changing cultural work of water to be explored and evokes a range of socially specific memories of a hidden city. Our aetiology of hydraulic cartographics is conducted using ideas from science and technology studies, semiology, and critical cartography with the goal of revealing how they work as virtual witnesses to an 1 unseen city, dramatizing engineering prowess and envisioning complex and messy materiality into a logical, holistic and fluid network underpinning the urban machine. 1

Implications for sequencing of biologic therapy and choice of second anti-TNF in patients with inflammatory bowel disease:results from the IMmunogenicity to Second Anti-TNF therapy (IMSAT) therapeutic drug monitoring study

BACKGROUND: Anti-drug antibodies are associated with treatment failure to anti-TNF agents in patients with inflammatory bowel disease (IBD).AIM: To assess whether immunogenicity to a patient's first anti-TNF agent would be associated with immunogenicity to the second, irrespective of drug sequence METHODS: We conducted a UK-wide, multicentre, retrospective cohort study to report rates of immunogenicity and treatment failure of second anti-TNF therapies in 1058 patients with IBD who underwent therapeutic drug monitoring for both infliximab and adalimumab. The primary outcome was immunogenicity to the second anti-TNF agent, defined at any timepoint as an anti-TNF antibody concentration ≥9 AU/ml for infliximab and ≥6 AU/ml for adalimumab.RESULTS: In patients treated with infliximab and then adalimumab, those who developed antibodies to infliximab were more likely to develop antibodies to adalimumab, than patients who did not develop antibodies to infliximab (OR 1.99, 95%CI 1.27-3.20, p = 0.002). Similarly, in patients treated with adalimumab and then infliximab, immunogenicity to adalimumab was associated with subsequent immunogenicity to infliximab (OR 2.63, 95%CI 1.46-4.80, p < 0.001). For each 10-fold increase in anti-infliximab and anti-adalimumab antibody concentration, the odds of subsequently developing antibodies to adalimumab and infliximab increased by 1.73 (95% CI 1.38-2.17, p < 0.001) and 1.99 (95%CI 1.34-2.99, p < 0.001), respectively. Patients who developed immunogenicity with undetectable drug levels to infliximab were more likely to develop immunogenicity with undetectable drug levels to adalimumab (OR 2.37, 95% CI 1.39-4.19, p < 0.001). Commencing an immunomodulator at the time of switching to the second anti-TNF was associated with improved drug persistence in patients with immunogenic, but not pharmacodynamic failure.CONCLUSION: Irrespective of drug sequence, immunogenicity to the first anti-TNF agent was associated with immunogenicity to the second, which was mitigated by the introduction of an immunomodulator in patients with immunogenic, but not pharmacodynamic treatment failure

Implications for sequencing of biologic therapy and choice of second anti-TNF in patients with inflammatory bowel disease:results from the IMmunogenicity to Second Anti-TNF therapy (IMSAT) therapeutic drug monitoring study

BACKGROUND: Anti-drug antibodies are associated with treatment failure to anti-TNF agents in patients with inflammatory bowel disease (IBD).AIM: To assess whether immunogenicity to a patient's first anti-TNF agent would be associated with immunogenicity to the second, irrespective of drug sequence METHODS: We conducted a UK-wide, multicentre, retrospective cohort study to report rates of immunogenicity and treatment failure of second anti-TNF therapies in 1058 patients with IBD who underwent therapeutic drug monitoring for both infliximab and adalimumab. The primary outcome was immunogenicity to the second anti-TNF agent, defined at any timepoint as an anti-TNF antibody concentration ≥9 AU/ml for infliximab and ≥6 AU/ml for adalimumab.RESULTS: In patients treated with infliximab and then adalimumab, those who developed antibodies to infliximab were more likely to develop antibodies to adalimumab, than patients who did not develop antibodies to infliximab (OR 1.99, 95%CI 1.27-3.20, p = 0.002). Similarly, in patients treated with adalimumab and then infliximab, immunogenicity to adalimumab was associated with subsequent immunogenicity to infliximab (OR 2.63, 95%CI 1.46-4.80, p < 0.001). For each 10-fold increase in anti-infliximab and anti-adalimumab antibody concentration, the odds of subsequently developing antibodies to adalimumab and infliximab increased by 1.73 (95% CI 1.38-2.17, p < 0.001) and 1.99 (95%CI 1.34-2.99, p < 0.001), respectively. Patients who developed immunogenicity with undetectable drug levels to infliximab were more likely to develop immunogenicity with undetectable drug levels to adalimumab (OR 2.37, 95% CI 1.39-4.19, p < 0.001). Commencing an immunomodulator at the time of switching to the second anti-TNF was associated with improved drug persistence in patients with immunogenic, but not pharmacodynamic failure.CONCLUSION: Irrespective of drug sequence, immunogenicity to the first anti-TNF agent was associated with immunogenicity to the second, which was mitigated by the introduction of an immunomodulator in patients with immunogenic, but not pharmacodynamic treatment failure