Proton relays are known to increase reaction rates for H₂ evolution and lower overpotentials in electrocatalytic reactions. In this report we describe two electrocatalysts, [Fe₄N(CO)₁₁(PPh₃)]^- (1^-) which has no proton relay, and hydroxyl-containing [Fe₄N(CO)₁₁(Ph₂P(CH₂)₂OH)]^- (2^-). Solid state structures indicate that these phosphine-substituted clusters are direct analogs of [Fe₄N(CO)₁₂]^- where one CO ligand has been replaced by a phosphine. We show that the proton relay changes the selectivity of reactions: CO₂ is reduced selectively to formate by 1^- in the absence of a relay, and protons are reduced to H₂ under a CO₂ atmosphere by 2^-. These results implicate a hydride intermediate in the mechanism of the reactions and demonstrate the importance of controlling proton delivery to control product selectivity. Thermochemical measurements performed using infrared spectroelectrochemistry provided pK_a and hydricity values for [HFe₄N(CO)₁₁(PPh₃)]^-, which are 23.7, and 45.5 kcal mol^-1, respectively. The pK_a of the hydroxyl group in 2^- was determined to fall between 29 and 41, and this suggests that the proximity of the proton relay to the active catalytic site plays a significant role in the product selectivity observed, since the acidity alone does not account for the observed results. More generally, this work emphasizes the importance of substrate delivery kinetics in determining the selectivity of CO₂ reduction reactions that proceed through metal-hydride intermediates.

The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning methods are being explored to replace computationally expensive components. Additionally, differentiable programming has been proposed as a complementary approach, providing controllable and scalable simulation routines. In this document, new and ongoing efforts for surrogate models and differential programming applied to detector simulation are discussed in the context of the 2021 Particle Physics Community Planning Exercise (`Snowmass').

Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.