Evan Spotte-Smith (they/them)

@ewcss.info

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Ad Astra Fellow, Asst. Prof. of Digital Chemistry, @ucddublin.bsky.social‬ | Editor, @joss-openjournals.bsky.social | Personal: ewcss.info | Research group (@coreacter.org): CoReACTER.org | ORCID: orcid.org/0000-0003-1554-197X | All opinions mine

Lecturer / Assistant Professor of Organic Chemistry, UCD School of Chemistry, Permanent Applications are invited for a permanent post of a Lecturer / Assistant Professor of Organic Chemistry within UCD School of Chemistry. The successful candidate will have research interests in any area of Organic Chemistry, and these include, but are not limited to, the development of novel methods in synthetic chemistry, sustainability, supramolecular chemistry, natural products chemistry, green chemistry, catalysis, and industrial chemistry. The candidate will contribute to our undergraduate and postgraduate taught programmes in particular, curriculum development of our BSc programmes in (a) Chemistry, (b) Medicinal Chemistry & Chemical Biology and (c) Chemistry with Environmental and Sustainable Chemistry. The candidate will supervise and perform cutting edge research and contribute to administration of the school. 95 Lecturer / Assistant Professor (above the bar) 2010 Salary Scale: €64,753 - €102,539 per annum. Appointment will be made on scale and in accordance with the Department of Finance guidelines Closing date: 12:00 noon (local Irish time) on 27/02/2026. Applications must be submitted by the closing date and time specified. Any applications which are still in progress at the closing time of 12:00 noon (Local Irish Time) on the specified closing date will be cancelled automatically by the system. UCD are unable to accept late applications. UCD do not require assistance from Recruitment Agencies. Any CV's submitted by Recruitment Agencies will be returned. Note: Hours of work for academic staff are those as prescribed under Public Service Agreements. For further information please follow link below: https://www.ucd.ie/hr/t4media/Academic%20Contract.pdf
A screenshot from a scholarly paper. The text reads: "On this picture, questions about the legitimacy of various gender classifications are both descriptive and normative. There is no context-independent fact of the matter about who is a man, a woman, or a sinner. 16 These things depend on contingent and localized social realities. As a result, our theoretical approach to gender is always guided by normative commitments—whether we are aware of that or not. Questions about which classification practices we ought to accept are not just about how the social world is organized, but about how it ought to be." Footnote 16, referenced above, reads: "16. Ah, yes, the three genders."
A screenshot of the Google Scholar homepage. Below the "Google Scholar" name/logo and the main search bar are two additions. One says "New! Scholar Labs: An AI Powered Scholar Search", with "New!" in red and the rest of the text in blue. The other, below, is a box with a blue-to-purple gradient background, white text that says "A new way to search". and a button with a flask and text that reads "Try Scholar Labs".
A screen-capture from an e-mail. The header reads "nature research assistant", and the body reads: Save time with a tool you can trust Your new AI-powered research assistant Dear Evan Spotte-Smith, Welcome to Nature Research Assistant–Springer Nature’s brand-new tool to help you read faster, contextualise insights and improve the communication of your research. You have been exclusively selected to access the all-new Nature Research Assistant. It is currently in beta and immediate access is only available with an invitation. To get started, login using your email address (ewcspottesmith@cmu.edu). If you do not already have a Springer Nature account, we ask that you create one by following the registration steps on the login page. The text of the body is white. Behind it is a black background with an array of red dots, some of them seemingly connected by red lines. Perhaps a depiction of a network of some kind?
Example of a sign previously posted in CMU bathrooms (both single-stall and multi-stall). It reads: "This restrooms is for everyone. Carnegie Mellon supports individuals using the facility of their choice. Please help us make campus a more welcoming and safer place for everyone. For more information visit: cmu.edu/all-gender"
Recently, one of the invited speakers for my symposium reached out to me. This speaker, who is an immigrant and a person of color, did not feel safe traveling to D.C. and attending ACS in person. They (here I am using gender-neutral pronouns to respect and protect anonymity) requested the option to present virtually so that they could participate in the conference without risking their safety. This is beyond reasonable, and it is certainly possible to accommodate. ACS meetings generally have strong internet connection, allowing for video-conferencing. While I understand that virtual presentations might introduce some additional technical logistics, the extraordinary circumstances justify the relatively minor headache that those logistics would bring. Even so, I was told that ACS would not be allowing any virtual talks in the Fall Meeting. ACS claims to prioritize the "safety and well-being" of its members and participants, but your actions tell a different story. At this very moment, you are asking researchers to either put their bodies at risk by entering a hostile territory in the midst of a fascist power-grab, or else to be sidelined from valuable scientific dialogue. This is an unacceptable choice that no chemist, and especially no marginalized chemist, should be forced to make. Our community deserves better, and I demand better, from ACS. Your choices have already damaged the present ACS meeting, and there is perhaps nothing that can be done, now that these choices have been made. Going forward, ACS must make the changes necessary to truly ensure safety and well-being, including making your conferences fully accessible. I would be open to meeting with your staff to discuss this matter further. Sincerely, Evan Walter Clark Spotte-Smith
To whom it may concern, Hello. My name is Evan Spotte-Smith (they/them). I am a Carnegie Bosch Institute Postdoctoral Fellow at Carnegie Mellon University (CMU), an incoming Adjunct Professor of Chemcial Engineering (CMU), and an incoming Ad Astra Fellow and Assistant Professor of Digital Chemistry at University College Dublin (UCD) in Ireland. I am an ACS member and an organizer for the 2025 ACS Fall Meeting, for which I co-organized the symposium "Chemical reaction networks, retrosynthesis, and reaction prediction" alongside Dr. Samuel Blau and Prof. Brett Savoie. I am writing to you with concerns regarding the 2025 ACS Fall Meeting in Washington, D.C. The District of Columbia is currently in a state of _de facto_ martial law. Armed federal agents roam the streets. In some cases, these agents are masked and refuse to self-identify, making them indistinguishable from vigilante militias or secret police. Coupled with the Trump administration's extralegal, authoritarian, and violent detentions and "deportations" of immigrants, visitors, and in some cases even United States citizens, this makes D.C. an unsafe place for many ACS members and meeting attendees... Alt text is continued in next post
a) A plot of "# of regions, core + shell" vs. "maximum nanoparticle radius (in nm)". Each box in the plot has two sections, with the lower section representing the ML predicted intensity of the optimal particle in that region of the search space and the upper section representing the kinetic Monte Carlo-simulated ultraviolet emission of the same particle. The color of each section of each box represents the ultraviolet/blue intensity (in counts per second or cps). The maximum intensity within the training set is around 20,000 cps, while the brightest particle observed has a kMC-predicted intensity > 100,000 cps; b) four selected optimized particles represented by layered pie charts indicating the doping in each region of the particles: i) 4 regions, with a maximum radius of 10nm. This particle has 2x UV intensity compared to the best particle in the training set; ii) 5 regions, with a maximum radius of 12nm; iii) 7 regions, with a maximum radius of 14nm; iv) 10 regions, with a maximum radius of 15nm. This particle had 6.5x UV intensity compared to the best particle in the training set.
a) A visual depiction of subdivision invariance. Two versions of the same particle are shown. One has only a single layer; the other is split in two layers. These two structurally equivalent particles have non-equivalent hetero-graph representations; this is shown through an explicit depiction of their associated graphs. b) A comparison of un-augmented and augmented training. Unaugmented training leads to an out-of-distribution (OOD) MSE of 2.3%, while on-the-fly data augmentation, with new, randomly-subdivided particles added in each training epoch, leads to an OOD MSE of 1.7%.
A depiction of heterogeneous graph representation for upconverting nanoparticles (UCNPs) (a-b) and a hetero-graph neural network architecture (c-e). a) A directed graph is shown, with green circles representing dopant nodes, red triangles representing intra-layer interaction nodes, and blue triangles representing inter-layer interaction nodes between a core layer and a shell layer. Directed edges link dopant nodes to interaction nodes. Node feature vectors, represented by gray boxes, are shown. Node features, shown in a box on the right, include dopant type, dopant concentration, inner and outer layer radius, and interaction type. b) Integrated interaction is a way to account for the variation in interaction strength between dopants as a function of distance. We integrate over pairwise distances; this is shown graphically using a circle with a line segment representing the pairwise distance between two points, as well as with an equation for the integrated interaction phi. c) The overall hetero-GNN architecture. Dopants, intra-layer interactions, and inter-layer interactions all go through separate embedding layers. Then, there are three "hetero-convolution" layers. These pass through a mean aggregation, followed by a fully connected neural network layer that produces the log(intensity). d) A more detailed depiction of the dopant node embedding, including a species embedding, feature-wise linear modulation (FiLM) layers and batch normalization; e) a more detailed depiction of the interaction node embedding, which includes a type-based embedding, the integrated interaction layer, a batch normalization layer, and a FiLM layer.
a) A 3D depiction of a core-shell upconverting nanoparticle (UCNP), with a shell of undoped NaYF4 and core layers doped with Yb3+, Nd3+, and Er3+; b) the heterostructure - where the dopants are, and what their concentrations are - determines the photophysics. This is depicted in two ways: i) a circle representing a UCNP contains three smaller circles representing dopants, with arrows between them indicating energy transfer. Two of the dopants (Nd and Er) engage in "cross-relaxation" or quenching, which prevents near infrared (NIR) light from being converted to ultraviolet light. ii) Two NIR photons hit two Nd dopants, which then pass energy to Yb dopants until eventually reaching Er, which emits UV light; c) The core and each shell layer can be doped differently. This is shown by a layered pie chart, with white representing Y, orange representing Er3+, green representing Nd3+, and blue representing Yb3+; d) to construct a dataset, multiple nanoparticles (represented by the layered pie charts) are used as inputs to kinetic Monte Carlo simulations, which produce a database of upconversion spectra; e) a plot with "model error" as a y-axis and "representation development" as an x-axis is shown. Three points are plotted. From right to left and top to bottom: tabular data, images (n-D tensors), and graphs. The graph point links to a box labeled "hetero-GNN"; the hetero-GNN passes gradients and predicted intensities to subfigure f; f) We preform gradient based optimization. A simple optimization surface is shown in blue, with an orange star at the maximum point. An arrow links this orange star to a pie chart representing the composition of an optimized UCNP.
A depiction of the SUNSET dataset; a) a table shows the breakdown of four sub-datasets (SUNSET-1, SUNSET-2, etc.) in terms of incident wavelength (800nm for SUNSET-1, 980nm for the others), presence of dopants (only SUNSET-1 has Nd, but all four have Yb and Er and only SUNSET-4 has Tm) and surface effects (only present in SUNSET-2 and 4). A plot showing the number of datapoints in each sub-dataset is also shown, with SUNSET-1 having about 6,000, SUNSET-2 and SUNSET-3 having somewhat less, and SUNSET-4 having the most at about 20,000. b) A further depiction of SUNSET-1. In-distribution train and test data contains core-only particles and particles with one, two, and three shells. The core size varies from a radius of 1nm to 4nm, and the shell thickness varies from 1nm to 2.5nm. The out-of-distribution test set contains 206 4-shell nanoparticles. At the bottom of the sub-figure are plots showing the number of particles versus nanoparticle radius (a distribution centered at around 8nm), shell thickness (with a somewhat even distribution between 1.0nm and 2.5nm), dopant concentration (a distribution which decays from 0.0 to 1.0), and log(UV intensity) (a sharp peak around 2, then a relatively flat distribution until about 3.5, at which point the distribution decays).
A picture of Evan (white person with long, wavy brown hair) sitting on a staircase in front of a shrub and smiling at the camera. They're wearing an orange spaghetti strap dress, a black beanie reading "Slim Dilly Dogs", and sunglasses. In their hand is a biodegradable single-use cup, and on their lap is an N95 mask. A cell phone and hand sanitizer are visible in their dress pocket.
A group portrait taken in a conference room with two rows of people - one larger row standing and one smaller row sitting on rolling chairs. In the background is a screen with the Toyota Research Institute logo displayed.
A shot of a pale-skinned genderqueer chemist chest-up, shot from below. They're wearing a name tag from the 2023 AIChE Annual Meeting, a black blouse, an orange blazer, and an N95 mask. A light fixture above them is causing some strange effects with the camera.
Energy diagram for reactions between ethylene carbonate (EC) and peroxide anion. Three reactions are shown: a proton transfer (shown in light blue); a nucleophilic substitution (dark blue); and an addition to form a tetrahedral complex (purple). All pathways are thermodynamically favorable and have low barriers, with proton transfer being the most favorable, with a barrier of 0.08 eV.
An energy diagram for two reactions between ethylene carbonate (EC) and singlet oxygen (1O2): a multi-step reaction forming a dicarbonyl and water, shown in red; and a concerted, single-step reaction forming hydrogen peroxide (H2O2) and vinylene carbonate (VC). Both pathways suffer from extremely high barriers and are kinetically inaccessible at moderate temperatures.
(a) Depiction of the stepwise oxidation reaction ethylene carbonate (EC) → EC+ + e– , with 3D structures for EC and EC+. The oxidation potential is Eox = 6.98 V. (b) Depictions of the concerted dissociative oxidation reaction 2EC → (EC+H)+ + (EC–H) + e–, with 3D structures of the reactants and products as clusters (top, Eox = 5.80 V) and isolated molecules (bottom, Eox = 5.84 V). (c) ΔG for the stepwise electrochemical oxidation of EC, as a function of potential and relative concentration. Oxidation is unfavorable until at least ~6.5 V (d) ΔG for the concerted dissociative oxidation of EC as a function of potential and relative concentration, where we assume that (EC+H)+ and EC–H are at the same concentration, denoted as [product]. This reaction is still unfavorable but could occur at ~4.8 V given extremely low product concentrations.