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We study OH(Σ) + H2 → H2O(X) + H reactive scattering using two potential energy models found in the literature.

The Framers of the Sixth and Seventh Amendments to the United States Constitution recognized that jury trials were essential for maintaining democratic legitimacy and avoiding epistemic crises. As an institution, the jury trial is purpose-built to engage citizens in the process of deliberative, participatory democracy with ground rules. The jury trial provides a carefully constructed setting aimed at sorting truth from falsehood. Despite its value, the jury trial has been under assault for decades. Concededly, jury trials can sometimes be inefficient, unreliable, unpredictable, and impractical. The COVID–19 pandemic rendered most physical jury trials unworkable but spurred some courts to begin using technology to transcend time-and-place limitations. These reforms inspire more profound changes. Rather than abolishing or cabining the jury trial, it should be reinvented with the benefit of modern science and technology. Features to be reconsidered include having local juries even for national civil cases, using unrepresentative groups of only six to twelve jurors, allowing attorneys to arbitrarily exclude jurors during voir dire, having synchronous and chronological presentations of cases over days or weeks, asking jurors to ignore inadmissible evidence and arguments that arise during live trials, and relying on secretive deliberations infected by implicit bias. Several of these extant practices work to disenfranchise or disempower Americans along racial, gender, and economic lines, thereby undermining one of the jury’s core functions. Other features cause jurors to err in resolving cases accurately, which can sometimes mean the difference between life and death. A reinvented, modernized jury institution can better serve its purposes by increasing citizen engagement, better fostering civic education and democratic deliberation, improving accuracy in sorting truth from falsehood, and enhancing efficiency in terms of both time and cost.

The prediction of the thermodynamic and kinetic properties of chemical reactions is increasingly being addressed by machine-learning (ML) methods, such as artificial neural networks (ANNs). While a number of recent studies have reported success in predicting chemical reaction activation energies, less attention has been focused on how the accuracy of ML predictions filters through to predictions of macroscopic observables. Here, we consider the impact of the uncertainty associated with ML prediction of activation energies on observable properties of chemical reaction networks, as given by microkinetics simulations based on ML-predicted reaction rates. After training an ANN to predict activation energies, given standard molecular descriptors for reactants and products alone, we performed microkinetics simulations of three different prototypical reaction networks: formamide decomposition, aldol reactions, and decomposition of 3-hydroperoxypropanal. We find that the kinetic modeling predictions can be in excellent agreement with corresponding simulations performed with ab initio calculations, but this is dependent on the inherent energetic landscape of the networks. We use these simulations to suggest some guidelines for when ML-based activation energies can be reliable and when one should take more care in applications to kinetics modeling.

Minimum‐energy path (MEP) calculations, such as those typified by the nudged elastic band method, require input of reactant and product molecular configurations at initialization. In the case of reactions involving more than one molecule, generating initial reactant and product configurations requires careful consideration of the relative position and orientations of the reactive molecules in order to ensure that the resulting MEP calculation proceeds without converging on an alternative reaction‐path, and without requiring excessive numbers of optimization iterations; as such, this initial system set‐up is most commonly performed “by hand,” with an expert user arranging reactive molecules in space to ensure that the following MEP calculation runs smoothly. In this Article, we introduce a simple preconditioning scheme which replaces this labor‐intensive, human‐knowledge‐based step with an automated deterministic computational scheme. In our approach, initial reactant and product configurations are generated such that steric hindrance between reactive molecules is minimized in the reactant and product configurations, while also simultaneously requiring minimal structural differences between the reactants and products. The method is demonstrated using a benchmark test‐set of >3400 organic molecular reactions, where comparison of the reactant/product configurations generated using our approach compare very well to initial configurations which were generated on an ad hoc basis.\ud \ud

Clinical ethicists hold near consensus on the view that healthcare should be provided regardless of patients’ past behaviors. In classic cases, the consensus can be explained by two key rationales—a lack of acute scarcity and the intractability of the facts around those behaviors, which make discrimination on past behavior gratuitous and infeasible to do fairly. Healthcare providers have a duty to help those who can be helped. In contrast, the COVID-19 pandemic suggests the possible recurrence of a very different situation, where a foreseeable acute shortage of healthcare resources means that some cannot be helped. And that shortage is exacerbated by the discrete decision of some to decline a free, safe, and highly effective vaccine, where the facts are clear. In such a future case, if healthcare must be denied to some patients, rationers who ignore vaccination status will become complicit in externalizing the consequences of refusing vaccination onto those who did not refuse. I argue that giving the unvaccinated person healthcare resources that would have otherwise gone to other patients is to wrongfully set back the interests of, or harm, those patients. The article considers rejoinders around the voluntariness of the vaccination choice, which impinges both access and information, and how to scale this criterion proportionally with other rationing criteria that serve utility. Ultimately, the article speculates on why there will be some cognitive dissonance under this approach, while upholding a more general solidarity with and concern for all those seeking healthcare.

We identify nine categories of clauses in Host Government Instruments (HGIs, ie licences, concessions, production sharing contracts (PSCs), etc.) that potentially affect the Fair Market Value of petroleum exploration and production rights, in addition to the clauses quantifying the fiscal terms. The categories comprise state participation, performance bonds and penalties for failing to perform minimum work programmes, local content, decommissioning, natural gas terms, stabilization, assignment and change of control, renewals and extensions and governance issues. For each category, we review and summarize global practice in the top 50 oil-producing countries. We quantify our analysis using statistics from a data set of 55 representative HGIs, including contemporary alternative types for five countries. The age of each HGI used varies, guided by the history of oil production in each country. Texts are available for 53 HGIs and published summaries are available for the other two. The discussion provides a checklist for use in negotiating new HGIs or performing due diligence for transactions involving existing HGIs. Our choice of representative HGIs and our characterizations of some clauses are both in part subjective. Nevertheless, we suggest the statistics provide a useful guide to trends in global practice.

BACKGROUND Games for health are a promising approach to health promotion. Their success depends on achieving both experiential (game) and instrumental (health) objectives. There is little to guide game for health (G4H) designers in integrating the science of behavior change with the art of game design. OBJECTIVE The aim of this study is to extend the Behaviour Change Wheel program planning model to develop Challenges for Healthy Aging: Leveraging Limits for Engaging Networked Game-Based Exercise (CHALLENGE), a G4H centered on increasing physical activity in insufficiently active older women. METHODS We present and apply the G4H Mechanics, Experiences, and Change (MECHA) process, which supplements the Behaviour Change Wheel program planning model. The additional steps are centered on identifying target G4H player experiences and corresponding game mechanics to help game designers integrate design elements and G4H objectives into behavioral interventions. RESULTS We identified a target behavior of increasing moderate-intensity walking among insufficiently active older women and key psychosocial determinants of this behavior from self-determination theory (eg, autonomy). We used MECHA to map these constructs to intervention functions (eg, persuasion) and G4H target player experiences (eg, captivation). Next, we identified behavior change techniques (eg, framing or reframing) and specific game mechanics (eg, transforming) to help realize intervention functions and elicit targeted player experiences. CONCLUSIONS MECHA can help researchers map specific linkages between distal intervention objectives and more proximal game design mechanics in games for health. This can facilitate G4H program planning, evaluation, and clearer scientific communication.

UNSTRUCTURED This is a peer review report.

Artificial intelligence (AI) has the potential to improve diagnostic accuracy. Yet people are often reluctant to trust automated systems, and some patient populations may be particularly distrusting. We sought to determine how diverse patient populations feel about the use of AI diagnostic tools, and whether framing and informing the choice affects uptake. To construct and pretest our materials, we conducted structured interviews with a diverse set of actual patients. We then conducted a pre-registered (osf.io/9y26x), randomized, blinded survey experiment in factorial design. A survey firm provided n = 2675 responses, oversampling minoritized populations. Clinical vignettes were randomly manipulated in eight variables with two levels each: disease severity (leukemia versus sleep apnea), whether AI is proven more accurate than human specialists, whether the AI clinic is personalized to the patient through listening and/or tailoring, whether the AI clinic avoids racial and/or financial biases, whether the Primary Care Physician (PCP) promises to explain and incorporate the advice, and whether the PCP nudges the patient towards AI as the established, recommended, and easy choice. Our main outcome measure was selection of AI clinic or human physician specialist clinic (binary, “AI uptake”). We found that with weighting representative to the U.S. population, respondents were almost evenly split (52.9% chose human doctor and 47.1% chose AI clinic). In unweighted experimental contrasts of respondents who met pre-registered criteria for engagement, a PCP’s explanation that AI has proven superior accuracy increased uptake (OR = 1.48, CI 1.24–1.77, p < .001), as did a PCP’s nudge towards AI as the established choice (OR = 1.25, CI: 1.05–1.50, p = .013), as did reassurance that the AI clinic had trained counselors to listen to the patient’s unique perspectives (OR = 1.27, CI: 1.07–1.52, p = .008). Disease severity (leukemia versus sleep apnea) and other manipulations did not affect AI uptake significantly. Compared to White respondents, Black respondents selected AI less often (OR = .73, CI: .55-.96, p = .023) and Native Americans selected it more often (OR: 1.37, CI: 1.01–1.87, p = .041). Older respondents were less likely to choose AI (OR: .99, CI: .987-.999, p = .03), as were those who identified as politically conservative (OR: .65, CI: .52-.81, p < .001) or viewed religion as important (OR: .64, CI: .52-.77, p < .001). For each unit increase in education, the odds are 1.10 greater for selecting an AI provider (OR: 1.10, CI: 1.03–1.18, p = .004). While many patients appear resistant to the use of AI, accuracy information, nudges and a listening patient experience may help increase acceptance. To ensure that the benefits of AI are secured in clinical practice, future research on best methods of physician incorporation and patient decision making is required.

We have recently demonstrated how potential energy surface (PES) interpola- tion methods such as kernel ridge regression (KRR), can be combined with accu- rate wavefunction time-propagation methods, specifically the multi-configuration time- dependent Hartree (MCTDH) method, to generate a new “on-the-fly” MCTDH scheme (DD-MCTDH) which does not require the pre-fitting of the PES which is normally re- quired by MCTDH. Specifically, we have shown how our DD-MCTDH strategy can be used to model non-adiabatic dynamics in a 4-mode/2-state model of pyrazine, with ab initio electronic structure calculations performed directly during propagation, requir- ing around 100 hours of computer wall-time. In this Article, we show how the efficiency and accuracy of DD-MCTDH can be dramatically improved further still by: (i) using systematic tensor decompositions of the KRR PES, and (ii) using a novel scheme for di- abatisation within the framework of configuration interaction (CI) methods which only requires local adiabatic electronic states, rather than non-adiabatic coupling matrix el- ements. The result of these improvements is that our latest version of DD-MCTDH can perform a 12-mode/2-state simulation of pyrazine, with PES evaluations at CAS level, in just 29-90 hours on a standard desktop computer; this work therefore represents an enormous step towards direct quantum dynamics with MCTDH.

Tolane (diphenyl-acetylene) is the smallest component of macromolecular arrays known as dendrimers that have interesting energy transport properties after photo-excitation. In this paper, a vibronic coupling Hamiltonian is set up to describe the initial isomerization of this molecule. The calculated absorption spectrum is in good agreement with experiment, with the ordering of states and energies from MRCI-DFT calculation. The focus of the study is the pathway for photo-excited isomerisation from the linear geometry at the Frank-Condon point to a trans -structure. The model shows that the origin of the excited-state minimum for the trans -isomer is due to stabilisation of a high lying state. Quantum dynamics calculations using the MCTDH algorithm show the model agrees with experiment that isomerisation only occurs at high temperature. It also suggests that internal conversion to the S 1 global minima happens via second order coupling terms, which can explain the observed picosecond timescales.

The reaction path Hamiltonian (RPH) can be used to calculate chemical reaction rate constants, going beyond transition-state theory in taking account of recrossing by providing an approximation to the dynamic transmission coefficient. However, the RPH necessitates the calculation of the Hessian matrix at a number of points along the minimum energy path; the associated computational cost stands as a bottleneck in RPH calculations, especially if one is interested in using high-accuracy electronic structure methods. In this work, four different Hessian update schemes (symmetric rank-1, Powell-symmetric Broyden, Bofill, and TS-BFGS updates) are assessed to see whether or not they reliably reproduce calculated transmission coefficients for three different chemical reactions. Based on the reactions investigated, the symmetric rank-1 Hessian update was the least appropriate for RPH construction, giving different transmission coefficients from the standard analytical Hessian approach, as well as inconsistent frequencies and coupling properties. The Bofill scheme, the Powell-symmetric Broyden scheme, and the TS-BFGS scheme were the most reliable Hessian update methods, with transmission coefficients that were in good agreement with those calculated by the standard RPH calculations. The relative accuracy of the different Hessian update schemes is further rationalized by investigating the approximated Coriolis and curvature coupling terms along the reaction-path, providing insight into when these schemes would be expected to work well. Furthermore, the associated computational cost associated with the RPH calculations was substantially reduced by the tested update schemes. Together, these results provide useful rules-of-thumb for using Hessian update schemes in RPH simulations.

Computational methods for predicting multi-step reaction mechanisms, such as those found in homogeneous catalysis by organometallic complexes, are rapidly emerging as powerful tools to support experimental mechanistic insight. We have recently shown how a graph-driven sampling scheme can be successfully used to propose a series of candidate reaction mechanisms for nanoparticle catalysis; however, identifying the most-likely reaction mechanism amongst this candidate set in an efficient scheme remains a challenge. Here, we show how simple descriptors for each reaction path, calculated using quick semi-empirical quantum chemistry, enable identification of the mechanism, but only if one considers both thermodynamic and kinetic parameters of proposed reaction mechanisms. Successful application to cobalt-catalysed alkene hydroformylation is used to benchmark this strategy, and provides insight into remaining algorithmic challenges.

The ground-up design of new molecular sunscreens, with improved photostability, absorbance and spectral coverage, stands as a challenge to fundamental chemical science. Correlating sunscreen molecular structure and function requires detailed insight into the relaxation pathways available following photoexcitation; however, the complex coupled electron/nuclear dynamics in these systems stands as a tough challenge to computational chemistry. To address this challenge, we have recently developed efficient and accurate simulation methods to model non-adiabatic dynamics of general molecular systems as a route to correlating photoinduced dynamics and potential sunscreen activity. Our approach, combining the multi-configuration time-dependent Hartree (MCTDH) method with PESs generated using machine-learning, represents a new “on-the-fly” strategy for accurate wavefunction propagation, with the potential to provide a new “black box” strategy for interrogating ultrafast dynamics in general photoinduced energy transport processes. Here, we illustrate our attempts to apply this methodology to study the ultrafast photochemistry of four compounds derived from mycosporine-like amino acids (MAAs), compounds which are believed to act as microbial sunscreens in micro-organisms such as algae. Specifically, we investigate how the choice of active vibrational space and diabatic electronic states in MCTDH strongly influences the predictions of ultrafast dynamic relaxation in representative MAAs. Our results serve to demonstrate that “on-the-fly” quantum dynamics using MCTDH is increasingly viable, but important barriers relating to coordinate choices and diabatisation remain.

Proposing and testing mechanistic hypotheses stands as one of the key applications of contemporary computational chemistry. In the majority of computational mechanistic analyses, the individual elementary steps leading from reactants to products are proposed by the user, based on learnt chemical knowledge, intuition, or comparison to an existing well-characterized mechanism for a closely-related chemical reaction. However, the pre-requisite of prior chemical knowledge is a barrier to automated (or ‘black box’) mechanistic generation and assessment, and may simultaneously preclude mechanistic proposals which lie outside the ‘standard’ chemical reaction set. In this Article, we propose a simple random-walk algorithm that searches for the set of elementary chemical reactions which transform defined reactant structures into target products. Our approach operates exclusively in the space of molecular connectivity matrices, seeking out the set of chemically-sensible bonding changes which link connectivity matrices for input reactant and product structures. We subsequently illustrate how atomic coordinates for each elementary reaction can be generated under the action of a graph- restraining potential, prior to further analysis by quantum chemical calculations. Our approach is successfully demonstrated for carbon monoxide oxidation, the water-gas shift reaction, and n-hexane aromatization, all catalyzed by Pt nanoparticles.

Magnetic Resonance Imaging (MRI) and Ultrasound (US) imaging are both routinely used in the assessment of rotator cuff pathology. Factors influencing the frequency of use of the respective technologies include access to equipment, provider preference, and cost. The purpose of our study was to compare the accuracy of in clinic ultrasound with that of MRI in diagnosing pathology of the rotator cuff in a large sample size with limited exclusionary criteria. 500 patient profiles assessed by each imaging type, MRI and ultrasound, who then proceeded to arthroscopic surgery were gathered and analyzed for interobserver agreement between the image interpretation and the surgical observations. While ultrasound displayed slightly higher sensitivity and specificity ratings in the diagnosis of full tears (0.90 and 0.92 vs. 0.86 and 0.91), MRI had a higher sensitivity (0.87 vs. 0.80) and Ultrasound had a higher specificity (0.86 vs. 0.76) for general cuff damage. The Weighted Kappa Values for both MRI (0.699) and Ultrasound (0.668) both indicate a substantial strength of agreement between the image interpretation and surgical findings. While there was a balanced distribution of errors observed in the MRI category, the most common error made in the Ultrasound assessment was a false negative assessment of a partial tear. The data indicate that in a clinical setting Ultrasound imaging is a cost-effective and accurate alternative to MRI and can be a valuable addition to the diagnostic assessment of rotator cuff injury and pathology.

Background: Flu-like symptoms (FLSs) and injection-site reactions (ISRs) have been reported with interferon beta treatments for multiple sclerosis (MS). We sought to obtain consensus on the characteristics/management of FLSs/ISRs in patients with relapsing-remitting MS based on experiences from the randomized, placebo-controlled ADVANCE study of peginterferon beta-1a.Methods: ADVANCE investigators with a predefined number of enrolled patients were eligible to participate in a consensus-generating exercise using a modified Delphi method. An independent steering committee oversaw the development of two sequential Delphi questionnaires. An average rating (AR) of 2.7 or more was defined as consensus a priori.Results: Thirty and 29 investigators (ie, responders) completed questionnaires 1 and 2, respectively, representing 374 patients from ADVANCE. Responders reported that the incidence/duration of FLSs/ISRs in their typical patient generally declined after 3 months of treatment. Responders reached consensus that FLSs typically last up to 24 hours (AR = 3.17) and have mild/moderate effects on activities of daily living (AR = 3.34). Patients should initiate acetaminophen/nonsteroidal anti-inflammatory drug treatment on a scheduled basis (AR = 3.31) and change the timing of injection (AR = 3.28) to manage FLSs. Injection-site rotation/cooling and drug administration at room temperature (all AR ≥ 3.10) were recommended for managing ISRs. Patient education on FLSs/ISRs was advocated before treatment initiation.Conclusions: Delphi responders agreed on the management strategies for FLSs/ISRs and agreed that patient education is critical to set treatment expectations and promote adherence.

The ratio of NMR relaxation time constantsmml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"mml:mrowmml:msubmml:miT/mml:mimml:mn1/mml:mn/mml:msubmml:mo//mml:momml:msubmml:miT/mml:mimml:mn2/mml:mn/mml:msub/mml:mrow/mml:mathprovides a non-destructive indication of the relative surface affinities exhibited by adsorbates within liquid-saturated mesoporous catalysts. In the present work we provide supporting evidence for the existence of a quantitative relationship between such measurements and adsorption energetics. As a prototypical example with relevance to green chemical processes we examine and contrast the relaxation characteristics of primary alcohols and cyclohexane within an industrial silica catalyst support.mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"mml:mrowmml:msubmml:miT/mml:mimml:mn1/mml:mn/mml:msubmml:mo//mml:momml:msubmml:miT/mml:mimml:mn2/mml:mn/mml:msub/mml:mrow/mml:mathvalues obtained at intermediate magnetic field strength are in good agreement with DFT adsorption energy calculations performed on single molecules interacting with an idealised silica surface. Our results demonstrate the remarkable ability of this metric to quantify surface affinities within systems of relevance to liquid-phase heterogeneous catalysis, and highlight NMR relaxation as a powerful method for the determination of adsorption phenomena within mesoporous solids.

The vibronic coupling Hamiltonian is a standard model used to describe the potential energy surfaces of systems in which non-adiabatic coupling is a key feature. This includes Jahn–Teller and Renner–Teller systems. The model approximates diabatic potential energy functions as polynomials expanded about a point of high symmetry. One must ensure the model Hamiltonian belongs to the totally symmetric irreducible representation of this point group. Here, a simple approach is presented to generate functions that form a basis for totally symmetric irreducible representations of non-Abelian groups and apply it to D∞h (2D) and O (3D). For the O group, the use of a well known basis-generating operator is also required. The functions generated for D∞h are then used to construct a ten state, four coordinate model of acetylene. The calculated absorption spectrum is compared to the experimental spectrum to serve as a validation of the approach.

We present an approximation to the thermal symmetric form of the quantum time-correlation function in the standard position path-integral representation. By transforming to a sum-and-difference position representation and then Taylor-expanding the potential energy surface of the system to second order, the resulting expression provides a harmonic weighting function that approximately recovers the contribution of the phase to the time-correlation function. This method is readily implemented in a Monte Carlo sampling scheme and provides exact results for harmonic potentials (for both linear and non-linear operators) and near-quantitative results for anharmonic systems for low temperatures and times that are likely to be relevant to condensed phase experiments. This article focuses on one-dimensional examples to provide insights into convergence and sampling properties, and we also discuss how this approximation method may be extended to many-dimensional systems.