Physics and Society
Photons = Tokens: The Physics of AI and the Economics of Knowledge
Alec Litowitz, Nick Polson and Vadim Sokolov
Debates about artificial intelligence capabilities and risks are often conducted without quantitative grounding. This paper applies the methodology of MacKay (2009) – who reframed energy policy as arithmetic – to the economy of AI computation. We define the
token, the elementary unit of large
language model input and output, as a physical quantity with measurable thermodynamic cost. Using Landauer's principle, Shannon's channel capacity, and current infrastructure data, we construct a supply-and-demand balance sheet for global
token production. We then derive a finite question budget: the number of meaningful queries humanity can direct at AI systems under physical, information-theoretic, and economic constraints. We apply Coase's theory of the firm and the durable-goods monopoly problem to the AI value chain – from
photon to atom to chip to power to
token to question – to identify where economic value concentrates and where regulatory intervention is warranted. We argue that the expansion of the
token budget does not resolve a deeper constraint: under structural uncertainty, the decisive variable is not how many questions can be answered but which questions are worth asking – a problem of agency and direction that computation alone cannot solve. We connect limits of measurement in the
token economy to a structural parallel between Goodhart's law and the Heisenberg uncertainty principle, and to Arrow's impossibility result for efficient information pricing. The framework yields order-of-magnitude estimates that discipline policy discussion: at current efficiency, the projected 2028 US AI energy allocation of 326~TWh could support roughly $6.5 \times 10^{17}$
tokens per year, or 225,000
tokens per person per day – more than three orders of magnitude above estimated mid-2024 utilization.
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Physics and Society
The Potential for an Innovation Winter: Estimating Impact of Federal Research Reductions on Faculty Activity
Robert A. Brown
The proposed reductions in federal research support proposed by the Trump Administration for 2026 would profoundly degrade the United States research universities, especially in the STEM fields and medicine (STEMM). A potentially devastating consequence would be on the funding distribution for individual faculty. Data and
stochastic modeling demonstrate that the result would be large fractions of previously research active faculty having subcritical research support. Research expenditure data from Boston University suggests that the funding distribution has a heavy tail (a Pareto like distribution)where a relatively small number of faculty have responsibility for a large fraction of the funding and that another fraction have minimal external support. A log normal distribution fits the expenditure data, and a multiplicative
stochastic model replicates the spending distributions for the STEMM faculty and, separately, the engineering faculty. Using data for the average expenditures by 146 R1 (very research intensive) engineering schools, the model predicts that the 40 percent funding reduction proposed by the Administration would increase from 26 to 47 percent the number of R1 universities with over half their faculty with less than 100,000 dollars of annual expenditures, if the reduction affects all equally. If the most research intensive universities win a disproportionate share, this percentage of universities jumps to almost 60. In such an environment, and in the face of other cost pressures, it would be difficult to maintain quality research and doctoral programs across many institutions, raising important questions about their optimal research strategy and organization if faced with this declining support. Ideas for navigating such a future are presented.
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Chemical Physics
Direct Variational Calculation of Two-Electron Reduced Density Matrices via Semidefinite Machine Learning
Luis H. Delgado-Granados and David A. Mazziotti
We introduce a data-driven framework for approximating the convex set of $N$-representable two-electron reduced density
matrices (2-RDMs). Traditional approaches characterize this set through linear
matrix inequalities that define its supporting hyperplanes. Here, we instead learn a vertex-based approximation to its boundary from molecular data and use this information to improve the set defined by low-order positivity constraints, without explicitly constructing higher-order conditions. The resulting semidefinite machine learning approach – combining an input convex neural network with semidefinite programming – drives a direct variational calculation of the 2-RDM with enhanced accuracy at computational cost comparable to two-positivity calculations. Applications to the potential energy curves of ${\rm C}_2^{2-}$, ${\rm N}_2$, and ${\rm O}_2^{2+}$ demonstrate these systematic improvements as well as close agreement with complete active space configuration interaction results. Overall, semidefinite machine learning interweaves data-driven boundary information with semidefinite positivity constraints to yield more accurate energies and 2-RDMs without explicit higher-order positivity conditions.
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Geophysics
GreenPhase: A Green Learning Approach for Earthquake Phase Picking
Yixing Wu, Shiou-Ya Wang, Dingyi Nie et al. (+5 more)
Earthquake detection and seismic phase picking are fundamental yet challenging tasks in seismology due to low signal-to-noise ratios, waveform variability, and overlapping events. Recent deep-learning models achieve strong results but rely on large
datasets and heavy backpropagation training, raising concerns over efficiency, interpretability, and sustainability. We propose GreenPhase, a multi-resolution, feed-forward, and mathematically interpretable model based on the Green Learning framework. GreenPhase comprises three resolution levels, each integrating unsupervised representation learning, supervised feature learning, and decision learning. Its feed-forward design eliminates backpropagation, enabling independent module optimization with stable training and clear interpretability. Predictions are refined from coarse to fine resolutions while computation is restricted to candidate regions. On the Stanford Earthquake
Dataset (STEAD), GreenPhase achieves excellent performance with F1 scores of 1.0 for detection, 0.98 for P-wave picking, and 0.96 for S-wave picking. This is accomplished while reducing the computational cost (FLOPs) for inference by approximately 83% compared to state-of-the-art models. These results demonstrate that the proposed model provides an efficient, interpretable, and sustainable alternative for large-scale seismic monitoring.
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All authors Yixing Wu, Shiou-Ya Wang, Dingyi Nie, Sanket Kumbhar, Yun-Tung Hsieh, Yun-Cheng Wang, Po-Chyi Su, C.-C. Jay Kuo
Fluid Dynamics
Counterflow around a cylinder
Matheus P. Severino, Leandro F. Souza, Elmer M. Gennaro et al. (+2 more)
The incompressible flow around a circular cylinder, positioned at the center of an unconfined planar counterflow, is studied by means of numerical solutions of the conservation equations and linear stability analysis. The flow is completely defined by the Reynolds number ($\Rey$) – based on the cylinder radius, the strain rate defining the counterflow, and the kinematic viscosity. For very low values of $\Rey$, the flow is steady, two-dimensional, and fully attached to the cylinder wall. Increasing $\Rey$ above $\Rey_s \approx 16.86$, the flow separates, giving rise to two symmetric, counter-rotating recirculation regions on each side of the cylinder. Further increasing $\Rey$ leads to a progressive enlargement of the recirculation regions and the appearance of multiple recirculation centers, akin to Moffatt eddies. However, the convective acceleration imposed by the counterflow limits their size. An oscillatory mode becomes linearly unstable for $\Rey_{c} \approx 4146$. This mode gives rise to a sinuous meandering of the wake flow, on each side of the cylinder, being analogous to the well-known von Kármán instability. The frequency of this mode is directly proportional to the strain rate defining the counterflow.
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All authors Matheus P. Severino, Leandro F. Souza, Elmer M. Gennaro, Daniel Rodríguez, Fernando F. Fachini
Instrumentation and Detectors
Surrogate neutron-capture studies with fission detection in inverse kinematics at the ESR storage ring
Bogusław Włoch, Camille Berthelot, Guy Leckenby et al. (+39 more)
The NECTAR (
Nuclear rEaCTions At storage Rings) experiment at the ESR heavy-ion storage ring at GSI/FAIR Darmstadt is dedicated to surrogate reaction studies of
neutron-induced reactions on heavy
nuclei in inverse kinematics. In this work, we report on the implementation and performance of a newly developed fission-fragment detection system integrated into the NECTAR experimental setup. The upgraded detector configuration enables, for the first time in a surrogate experiment, the simultaneous detection ofgamma-decay residues, multi-
neutron-emission residues, and fission fragments. The full setup was used for the first time in an experiment where a stored beam of bare 238U92+ ions at 17.24 MeV/u interacted with a gas-jet deuterium target, populating excited 238U and 239U
nuclei via the 238U(d,d') and 238U(d,p) reactions. We describe the geometry of the used fission fragment detectors, design constraints, and simulation-based efficiency determination. The target-like particle identification and beam-like residue spectra demonstrating the performance of the complete setup are also shown.
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All authors Bogusław Włoch, Camille Berthelot, Guy Leckenby, Beatriz Jurado, Jerome Pibernat, Manfred Grieser, Jan Glorius, Yuri A. Litvinov, Laurent Audouin, Bertram Blank, Lucas Bégué–Guillou, Alex Cobo Zarzuelo, Sophia Florence Dellmann, Marc Dupuis, Oliver Forstner, Alexis Francheteau, David Freire Fernández, Miki Fukutome, Mathias Gerbaux, Jérôme Giovinazzo, Alexandre Gumberidze, Andreas Heinz, Ana Henriques, Regina Hess, Indu Jangid, Anton Kalinin, Wolfram Korten, Sergey Litvinov, Bernd Lorentz, Antonio M. Moro, Nikolaos Petridis, Ulrich Popp, Gregory Potel, Diego Ramos, Mathieu Roche, Mohammad Shahab Sanjari, Michele Sguazzin, Ragandeep Singh Sidhu, Uwe Spillmann, Markus Steck, Thomas Stoehlker, Takayuki Yamaguchi
Atomic Physics · Cross-listed from cond-mat.quant-gas
Revealing Pseudo-Fermionization and Chiral Binding of One-Dimensional Anyons using Adiabatic State Preparation
Brice Bakkali-Hassani, Joyce Kwan, Perrin Segura et al. (+5 more)
Fractional statistics give rise to
quantum behaviors that differ fundamentally from those of
bosons and
fermions. While two-dimensional anyons play a major role in strongly correlated systems and topological
quantum computing, the nature of their one-dimensional (1D) counterparts remains the subject of intense debate, with renewed interest fueled by recent experimental progress. Theoretically, 1D anyons are predicted to host exotic many-body phases and
quantum phase transitions, yet experimental signatures have remained elusive. Using ultracold atoms in an optical
lattice, we prepare two-body ground states of the 1D anyon-Hubbard model by combining
Hamiltonian engineering via quasiperiodic drives and adiabatic state manipulation. We uncover the effects of statistical interactions that lead to pseudo-fermionization and to the formation of
chiral bound states when particles remain close together. Our results establish a link between
lattice and continuum realizations of anyon models, and mark important steps towards the precise control of 1D anyons in both
equilibrium and out-of-
equilibrium settings.
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All authors Brice Bakkali-Hassani, Joyce Kwan, Perrin Segura, Yanfei Li, Isaac Tesfaye, Gerard Valentí-Rojas, André Eckardt, Markus Greiner
Accelerator Physics
Driver-delay chicanes for a multistage plasma-based accelerator facility
D. Kalvik, E. Adli, P. Drobniak et al. (+2 more)
The SPARTA project aims to design a medium-sized accelerator facility that facilitates new experiments in strong-field
quantum electrodynamics using
plasma-based accelerators. For this, we need several
plasma stages and, therefore, several drivers. Drivers can be either an ultra-relativistic charged particle beam or a high-intensity
laser beam. In case we use particle beams, we need a method of distributing these beams from a
radio-frequency accelerator to the different
plasma stages. A central part of this is a delay scheme that ensures temporal synchronization of the drivers. In this paper, we demonstrate how to achieve a 2 ns delay in $\sim$12 m, while keeping the first-order beam parameters periodic.
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All authors D. Kalvik, E. Adli, P. Drobniak, F. Peña, C.A. Lindstrøm
Optics
DC-to-GHz Modulation In Microring Modulators Using Ferroelectric Nematic Liquid Crystal-on-Silicon in a Foundry Photonic Process
Iman Taghavi, Alexander Tofini, Edward K. Leung et al. (+4 more)
Silicon-organic hybrid (SOH) platforms exhibit exceptional electro-optic (EO) properties, including high-speed operation, low energy consumption, and compact footprints. However, the absence of a scalable poling method for EO
polymers, combined with the slow switching speeds characteristic of
liquid crystals, has impeded the integration and compatibility of these materials with commercial silicon photonic foundries. On the other hand, the realization of very-large-scale photonic integrated
circuits (PICs) in the native silicon photonics platform itself is impeded by the complexities associated with the wavelength and thermal stabilization for microring modulators (MRMs). This study establishes the foundation for a poling-free, CMOS-compatible SOH MRM platform by exploiting simultaneous AC phase shifts in ferroelectric nematic
liquid crystals (FN-LCs). We present the first demonstration of an MRM coated with FN-LC, with both the RF signal and the DC bias applied to the same electrodes, taking advantage of the dual phase shift uniquely available in FN-LC. An EO bandwidth of f$_{-6dB}\approx$ 7.8 GHz is achieved \textendash~ the highest reported value for an SOH MRM to date. As a proof of concept, we demonstrate an approximately linear resonance shift across the full width at half maximum ($\approx$ 150 pm/V), with a static power efficiency of $\approx$ 4.5 nW/$\pi$ for an MRM occupying a total footprint of $\approx$ 0.084 mm$^2$ and exhibiting an on-chip optical insertion loss of $\approx$ 0.78 dB. Successful infiltration of FN-LC, selectively patterned on top of phase shifters, along with optical input/output channels established using free-form photonic wire bonds, is demonstrated in the proposed PIC.
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All authors Iman Taghavi, Alexander Tofini, Edward K. Leung, Cory Pecinovsky, Nicolas A.F. Jaeger, Lukas Chrostowski, Sudip Shekhar
Chemical Physics · Cross-listed from cond-mat.other
Transient Plastic Spin Labeling with Chlorine Dioxide
Bence G. Márkus, Sándor Kollarics, Kristóf Kály-Kullai et al. (+5 more)
Plastic waste, being one of the most important problems for humankind, poses severe threats to ecosystems, wildlife, and human health. Tracing, quantifying, and identifying types of plastic waste is of crucial importance to understand its environmental pathways and develop targeted strategies for reduction, recycling, and remediation. To contribute to addressing this global issue, we investigated the
spin-labeling capabilities of aqueous chlorine dioxide (ClO$_2$) radicals upon introduction into poly(ethylene terephthalate) and utilized electron
spin resonance spectroscopy for detection. The technique is capable of identifying plastic species as the unpaired electron of the radical molecule is strongly sensitive to its local environment through its coupling parameters. Temperature-dependent measurements revealed that the molecules are immobilized at low temperatures and exhibit well-resolved anisotropic and hyperfine spectra that are quantitatively described by a model
spin Hamiltonian. Even above the melting point of water, some degrees of freedom remain blocked as a result of the
polymer matrix. Furthermore, employing a time-series measurement at room temperature enabled us to determine the
diffusion coefficient of the molecule in the
polymer.
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All authors Bence G. Márkus, Sándor Kollarics, Kristóf Kály-Kullai, Bernadett Juhász, Dávid Beke, László Forró, Zoltán Noszticzius, Ferenc Simon
Instrumentation and Detectors
LANTERN: Characterization technology for low threshold cryogenic detectors
Giorgio Del Castello
The use of low-temperature detectors, such as cryogenic calorimeters, has pioneered the recent advancements in low-energy rare event searches. These detectors provide a low-noise environment essential for the direct detection of dark matter and
neutrinos. Characterizing these detectors within the region of interest (ROI), typically spanning from O(10~eV) to O(1~keV), has proven to be a challenging task. Conventional radioactive sources produce signals above this range, leading to nonlinearities and saturation effects. Moreover, these detectors are usually deployed in low background environments, meaning that having a radioactive source during physics runs can spoil the measurement making the use of this type of solution unfeasible. As a solution to these issues, we introduce LANTERN, an optical calibration system designed for highly segmented cryogenic calorimeters. LANTERN utilizes the photostatistics resulting from the absorption of monochromatic UV-Vis
photons emitted by LEDs to analyze the detector response curve, without needing prior knowledge of the total energy deposited. The system employs a fast-switching LED
matrix that operates at excitation times faster than the typical response of cryogenic detectors and can currently characterize up to 64 calorimeters independently. In this work, the validation of the final electronics designed for the project is shown. The first test was carried out by calibrating one of the cryogenic detectors of the BULLKID-DM experiment and checking the energy-reconstruction error of the spectral features produced by the surrounding lead casing. An error of $\approx 2\%$ has been observed in the energy reconstruction. The second validation was carried out by cross-calibrating one of the CALDER thin detectors with a commercial LED driver, and compatible results between the two setups were achieved.
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History and Philosophy of Physics · Cross-listed from quant-ph
No change in Hilbert space fundamentalism
Ovidiu Cristinel Stoica
Hilbert space fundamentalism (HSF) states that everything about the physical world is encoded in the
Hamiltonian operator and the state vector (as a unit vector, not a wavefunction, which requires additional specification of a configuration space, a position basis, or the position observables). That all structures needed to describe reality, including subsystems, space, fields, emerge from these. I show that HSF can't account for our observations that the physical world changes in time.
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Chemical Physics
Environment-Induced Exciton Renormalization in the Photosystem II Reaction Center
Tucker Allen, Barry Y. Li, Nadine C. Bradbury et al. (+1 more)
Protein electrostatics tune excitation energies in the Photosystem II reaction center (PSII-RC), yet a fully
quantum-mechanical many-body description of how the surrounding
protein environment renormalizes
excitons has remained computationally inaccessible. The Bethe-Salpeter equation (BSE) within many-body perturbation theory accurately describes excitonic physics through an explicit electron-hole interaction, but is prohibitively expensive for systems containing thousands of valence electrons. Here, we show that for sufficiently large systems the BSE becomes simpler to solve when treated with modern
stochastic sampling techniques, as atomistic interactions self-average. In this regime, the effective electron-hole interaction mediated by the environment is governed by collective $k$-dependent polarization. These insights enable an ab initio study of the PSII-RC in which all six chlorins forming the hexameric dye core are treated explicitly together with a roughly seven Angstrom local
protein environment. We directly compare the low-lying optical excitations of the isolated chromophore hexamer (1276 valence electrons) and the
protein-dye cluster (3238 valence electrons). For $Q_y$ excitations near 680 nm, inclusion of the
protein environment induces polarization-dependent energy shifts, redistributes spectral weight, and alters
exciton delocalization and pigment character. Lateral and transverse asymmetries in the low-lying excited states are captured at the BSE level of theory. These results establish that we now have the tools for many-body calculations of biological nanostructures.
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All authors Tucker Allen, Barry Y. Li, Nadine C. Bradbury, Daniel Neuhauser
Plasma Physics · Cross-listed from eess.SP
A general extractor for coherent patterns across time-frequency measurements
Nathaniel Chen, Kouroche Bouchiat, Peter Steiner et al. (+7 more)
Scientific discovery increasingly depends on finding meaningful patterns within volumes of sensor data too large for manual inspection. Existing time-frequency event extractors are often engineered or trained on manually labeled examples for each instrument, which can be laborious and difficult to adapt to new environments. To address this, we introduce TokEye, a reference-free method that learns a general criterion for coherent structure from heterogeneous fusion measurements. A model developed under this method, without retraining, can extract signals from various other fields including marine bioacoustics and wideband radar. It recovers 82.5% of expert-annotated stellarator signals, detects 78% of dolphin whistles, and identifies radar events with 81.5% average precision across six
benchmarks, while processing full experimental spectrograms in under one second on a GPU. This transfer across unrelated sensors and physical systems reveals common exploitable structure in time-frequency measurements and establishes a scalable foundation for automated scientific signal discovery.
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All authors Nathaniel Chen, Kouroche Bouchiat, Peter Steiner, Andrew Rothstein, David Smith, Max Austin, Mike van Zeeland, Marie Roch, Azarakhsh Jalalvand, Egemen Kolemen
Computational Physics · Cross-listed from quant-ph
Entanglement Barriers from Computational Complexity: Matrix-Product-State Approach to Satisfiability
Tim Pokart, Frank Pollmann and Jan Carl Budich
We approach the 3-SAT satisfiability problem with the
quantum-inspired method of imaginary time propagation (ITP) applied to
matrix product states (MPS) on a classical computer. This ansatz is fundamentally limited by a
quantum entanglement barrier that emerges in imaginary time, reflecting the exponential hardness expected for this NP-complete problem. Strikingly, we argue based on careful analysis of the structure imprinted onto the MPS by the 3-SAT instances that this barrier arises from classical computational complexity. To reveal this connection, we elucidate with
stochastic models the specific relationship between the classical hardness of the $\sharp$P $\supseteq$ NP-complete counting problem $\sharp$3-SAT and the
entanglement properties of the
quantum state. Our findings illuminate the limitations of this
quantum-inspired approach and demonstrate how purely classical computational complexity can manifest in
quantum entanglement. Furthermore, we present estimates of the non-stabilizerness required by the protocol, finding a similar resource barrier. Specifically, the necessary amount of non-Clifford operations scales superlinearly in system size, thus implying extensive resource requirements of ITP on different architectures such as Clifford
circuits or gate-based
quantum computers.
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Atomic Physics · Cross-listed from hep-ph
Earth Matter Enhanced Axion Dark Matter Search
Xiaofei Huang, Xiaolin Ma, Zitong Xu et al. (+2 more)
Laboratory searches for ultralight
axion dark matter (DM) have traditionally assumed the terrestrial density of
axions is equal to the average density of DM in the
solar system. However, quadratic couplings to matter introduce a non-trivial field profile near the Earth. In this work, we present the first dedicated experimental implementation of this environment-aware
axion DM wind search framework. Leveraging the extreme sensitivity of a K–Rb–$^{21}$Ne comagnetometer to pseudo-
magnetic fields induced by
axion DM, we analyzed our data in the context of the massively enhanced local gradient of
axions due to interactions with matter, though no signal candidates were found. Consequently, we have set the most stringent limits on
axion-
neutron derivative interactions for masses $m_a \in [0.041, ~28.9]~\rm feV$, improving from previous experiments that ignore terrestrial matter effects by as much as three orders of magnitude for certain masses. Our work highlights the necessity of accounting for environmental modifications in precision frontier experiments and demonstrates how geophysical variations can be harnessed to act as a natural amplifier for DM possibly enabling future detection in parts of the parameter space that were previously beyond reach.
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All authors Xiaofei Huang, Xiaolin Ma, Zitong Xu, Itay M. Bloch, Kai Wei
Accelerator Physics
Operational Accelerator Tuning via Model-Coupled Optics and Bayesian Steering
O. Hassan, O. Shelbaya, P. M. Jung et al. (+3 more)
We present an on-line tuning strategy for the
ISAC post-accelerator that pre-sets machine optics with a digital twin and then performs
Bayesian optimization for steering under online operation with beam. The model computes end-to-end tunes in seconds and interfaces with the control system under device bounds, slew-rate limits, and loss interlocks. We report three experimental case studies demonstrating that decoupling optics from steering yields faster and more reliable convergence than a fully
Bayesian optics-plus-steering baseline under identical conditions. Across these cases, iterations to high transmission tunes are reduced by a factor of 4-6, with final average transmissions in the mid- to high-90% range. By factorizing optics from steering, the dimensionality of the parameter space is reduced, convergence becomes more predictable, and operational safeguards are easier to enforce.
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All authors O. Hassan, O. Shelbaya, P. M. Jung, O. Kester, T. Planche, W. Fedorko
Chemical Physics · Cross-listed from cs.LG
Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions
Luca Thiede, Abdulrahman Aldossary, Andreas Burger et al. (+9 more)
Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond DFT and for predicting properties that closely align with experiment. It is known as the ”gold standard” of
quantum chemistry. Unfortunately, the high computational cost of CC limits its widespread applicability. In this work, we present the Molecular Orbital Learning (MōLe) architecture, an equivariant machine learning model that directly predicts CC's core mathematical objects, the excitation amplitudes, from the mean-field Hartree-Fock molecular orbitals as inputs. We test various aspects of our model and demonstrate its remarkable data efficiency and out-of-distribution generalization to larger molecules and off-
equilibrium geometries, despite being trained only on small
equilibrium geometries. Finally, we also examine its ability to reduce the number of cycles required to converge CC calculations. MōLe can set the foundations for high-accuracy wavefunction-based ML architectures to accelerate molecular design and complement force-field approaches.
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All authors Luca Thiede, Abdulrahman Aldossary, Andreas Burger, Jorge Arturo Campos-Gonzalez-Angulo, Ning Wang, Alexander Zook, Melisa Alkan, Kouhei Nakaji, Taylor Lee Patti, Jérôme Florian Gonthier, Mohammad Ghazi Vakili, Alán Aspuru-Guzik