AI for Science

Protein language models (PLMs) have emerged as powerful tools for controllable biomolecular design, yet their post-training adaptation typically relies on costly wet-lab validation or curated preference datasets. To overcome this supervision bottleneck, we introduce unsupervised reward optimization of PLMs, a comprehensive framework for steerable protein generation without ground-truth labels. Our key insight is that task-agnostic rewards, which combine intrinsic model uncertainty with extrinsic semantic consistency informed by protein representation models, exhibit strong correlation with controllability measures across base models and temperature regimes. Building upon this discovery, we propose two offline algorithms: Soft Reward Optimization (SRO) and Binarized Reward Optimization (BRO), which effectively maximize the classical RLHF objective induced by these proxy rewards. Extensive experiments on compositional out-of-distribution prompts demonstrate that both methods significantly outperform competitive baselines (DPO, KTO), while approaching oracle performance across multiple sampling temperatures, model scales and protein families. Moreover, PLMs fine-tuned with unsupervised rewards can achieve consistently higher coverage compared to their base model in pass@k evaluations. By enabling self-improvement of PLMs through their own generated experience, our framework provides a scalable pathway toward controllable biomolecular design in settings where labeled preferences or experimental feedback are scarce or unavailable.

Decision making in lithium production is challenging, whether from an investor's perspective or a strategic production standpoint. Determining which mines to open and when to open them involves not only geological and price uncertainties, but also complexities around the choice of extraction method, from direct lithium extraction to hard rock mining. Prior work explored models of this problem and different methods to optimize mining decisions; these models did not account for uncertainty in pricing, uncertainty in demand, or different mining technologies to extract lithium. Incorporating different pricing models and extraction technology into these models enables more robust strategies for determining not only when and where to open a mine, but also which method of production to pursue. We frame the problem as a partially observable Markov decision process (POMDP) and solve using belief state planning methods to get optimal decision making. In our study, we show that POMDP solvers outperform human inspired heuristics by dynamically adapting to shifting lithium price regimes (static, linear, exponential, and stochastic) through belief state planning and explicit uncertainty management. By optimally sequencing exploration, production, and technology choice, the framework achieves higher demand fulfillment and more balanced economic environmental outcomes over the projects lifetime in all different pricing and deposit scenarios.

Deep research agents are Large Language Model (LLM)-based systems designed for autonomous, multi-step scientific reasoning, and they hold immense potential for accelerating research in the physical sciences. However, comprehensive and in-depth evaluations of their capabilities within this domain remain lacking. To address this gap, we introduce PhySciBench, a benchmark highly relevant to physical science research, comprising 200 expert-curated questions, balanced between physics and chemistry, across six task categories that reflect real-world scientific workflows. Evaluations of state-of-the-art models and agent systems on PhySciBench reveal limited performance; even the strongest baseline, Gemini Deep Research, achieves an accuracy of only 33.5%. Analysis of failure cases identifies three recurrent deficiencies: fragility in extended reasoning chains, limited knowledge transfer across steps, and a lack of physics-grounded self-verification. Motivated by these findings, we develop DelveAgent, a modular multi-agent framework equipped with an adaptive planning loop, dual-granularity memory, and a hierarchical physics-grounded reflection mechanism. Across four scientific benchmarks, DelveAgent improves accuracy by up to 7.5 percentage points while reducing inference costs to approximately one-third of the strongest baseline. These results establish the significance of PhySciBench as a critical benchmark for evaluating AI systems in the physical sciences and demonstrate that architectural specialization can effectively enhance the reliability of autonomous scientific research.

A predictive physics-based model of human respiratory, phonatory, and articulatory subsystems is developed to simulate voice production. Representing lungs, compressible airways, and vocal folds as spring-damper-mass controlled piston-cylinder systems, our mathematical model robustly captures the intricate dynamics of airways during phonation. The nonlinear viscoelastic properties of lung tissues and compressible airways were investigated, yielding a responsive and expressive baseline respiratory model with the capability to further extend into a patient-specific model for both respiration and phonation. The resulting framework was subsequently integrated with a mechanical representation of the vocal tract, governed by the glottal area waveform (GAW) capturing the motion of vocal folds during sustained phonation. The GAW is extracted from laryngeal high-speed videoendoscopy data of a normophonic participant using deep learning. Our novel paradigm transcends beyond modeling the respiratory system, enabling AI-driven modeling of vocalization, including vocal fold dynamics, interactions with flow aerodynamics, and flow resistances, induced by the oscillatory behavior of vocal folds. Our investigation leads to the first-ever simulation of respiratory dynamics for vocalization, directly advancing the prediction of subglottal pressure distributions, impossible to measure directly and noninvasively in humans, dynamic resistances, and energy transfer mechanisms during phonation in voice mechanics.

Time-dependent partial differential equations (PDEs) arise in many engineering systems, including thermo-fluid applications. Classical numerical simulations of such systems can become computationally expensive for long-time dynamics because they typically require sequential time integration with time steps constrained by stability, accuracy, or nonlinear solvers. Although scientific machine learning provides an alternative for approximating PDE solutions, standard neural network approximations often degrade when extrapolated beyond the training time interval. In this work, we propose a steady-state-informed neural network representation for dissipative PDE systems whose solutions relax toward a stationary equilibrium. The proposed ansatz decomposes the solution into a steady-state component and a transient correction modulated by a time-dependent decay profile. When the decay profile vanishes at long time and the transient correction remains bounded, the representation embeds convergence to the prescribed steady state directly into the architecture, rather than enforcing it through an additional penalty term. This allows the network to learn the transient dynamics while preserving the correct asymptotic behavior. We implement the approach within a physics-informed neural network (PINN) framework and train the resulting model using the SOAP optimizer. The method is evaluated on a sequence of problems of increasing physical and geometric complexity, ranging from the one-dimensional heat equation to incompressible Navier-Stokes flow in a lid-driven cavity, natural convection in a square cavity, and a full three-dimensional conjugate heat transfer problem. The numerical results show that the steady-state-informed architecture substantially improves temporal extrapolation beyond the training interval compared with architectures that do not explicitly enforce the asymptotic condition.

Operator learning is an emerging interdisciplinary field that integrates machine learning with scientific computing. By mapping infinite-dimensional function spaces, this approach provides an efficient surrogate modeling framework for high-dimensional partial differential equations (PDEs). Compared to traditional numerical solvers, it achieves a superior trade-off between computational complexity and approximation accuracy, demonstrating significant advantages in many-query tasks such as real-time prediction and parameter sweeps. Given the stringent accuracy requirements of both forward simulation and inverse inference, as well as the precision bottlenecks of existing operator learning methods in handling complex boundaries or long-term evolution, we propose the Starter-Iterator Neural Operator (SINO). Our framework reinterprets the initialization strategies and iterative formats of traditional iterative methods through neural networks, establishing an efficient approach for spectral-spatiotemporal collaborative modeling. Specifically, the frequency-domain initialization module captures globally stable low-frequency features, while the time-domain learning module focuses on optimizing local solution residuals, thereby effectively overcoming the inherent limitations of conventional single-domain modeling approaches. Extensive experiments on typical dynamical systems such as the Navier-Stokes equations and acoustic wave equations, as well as practical applications including super-resolution imaging and weather forecasting, demonstrate that SINO achieves outstanding performance in numerical accuracy, generalization capability, and robustness.

Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement. However, its role in scientific machine learning remains largely unexplored. Physics-informed neural fields provide a natural setting, because differential equations require trial spaces that preserve phase, frequency and derivative structure. Here we introduce a photonic quantum neural field in which coordinates become trainable optical phases, are mixed by multi-photon Fock-space interference and are decoded from photon-number measurements. The photonic circuit is optimized as the neural-field representation itself, not as a fixed feature map or hardware accelerator. Photonic measurement is therefore a trainable representation on which the physics-informed residual is minimized. Across seven elliptic, wave, nonlinear dispersive and inverse PDE benchmarks, we observe a phase-complexity transition: classical coordinate and Fourier-feature networks suffice in smooth regimes, whereas the photonic field is most accurate when residual derivatives amplify phase mismatch. In the hardest regimes it gives the lowest errors, with margins reaching an order of magnitude and about one quarter of the trainable parameters of classical baselines. Frozen and shuffled controls, together with noise stress tests, attribute this gain to learned interference and stable Fock-probability readout under compound perturbations. These results identify photonic quantum measurement as a representation-learning principle for scientific machine learning.

We review beam-quality physics in laser-driven (LWFA) and beam-driven (PWFA) plasma wakefield accelerators through the symmetry group of the idealised blowout wake -- axisymmetry $\mathrm{SO}(2)_ϕ$, adiabatic longitudinal translation, and propagation-direction parity. Transverse perturbations of the wake are classified by an integer azimuthal multipole order $m$ labelling the irreducible representations of $\mathrm{SO}(2)_ϕ$, with the lowest beam-quality observables coupling at a specific multipole: the bunch centroid at $m=1$, cross-plane emittance coupling at $m=2$. A symplectic analogy relates transverse matching to longitudinal beam loading. Several phenomena common to LWFA and PWFA -- hose instabilities, pulse-front-tilt jitter, spot-asymmetry emittance growth, polarisation-dependent centroid motion, resonant cross-plane mixing -- populate the two lowest non-trivial $m$-channels and admit a unified discussion. The positron-witness problem reorganises in the same language: each known mitigation abandons one specific feature of the uniform-density blowout, drawn from a finite set. The classification also raises the possibility of an $m=3$ response channel whose magnitude remains open. We note the connection to symmetry-equivariant Bayesian optimisation of plasma accelerators.

We investigate the driving mechanism of low-frequency unsteadiness in bow shock-turbulent boundary layer interactions due to protuberances. Wind tunnel experiments are conducted at a freestream Mach number of 2.87 with protuberances of different shapes and sizes. From time-resolved surface pressure measurements and schlieren imaging, the unsteadiness is characterized by low-frequency shock oscillations, with a Strouhal number of $St_δ\sim 0.01$ based on the boundary layer thickness ($δ$), while the separated region exhibits predominantly mid-frequency pressure oscillations, with $St_δ \sim 0.1$. Mid-span separation length, $L_{sep}$, is identified as a key parameter in determining time and length scales of shock oscillations. Further details of the interaction are examined through compressible adaptive detached eddy simulations for one particular case, viz.,the cubical protuberance of side 15 mm. A detailed modal analysis using proper orthogonal decomposition (POD) is performed with the 3-D data from computations. Flapping of shock-foot about mid-span was apparent, over and above the coherent to-and-fro oscillations, with the dominance of anti-symmetric mode in the POD of wall pressure fluctuations. The motion of the shock foot is initiated near mid-span, while the shock foot at other spanwise locations lags behind. The flap and asymmetries are related to the spanwise extent of reverse flow. From the reconstructed 3-D flow field using low-frequency modes, along with corroborating observations from the two-point correlations, it is inferred that the imbalance and time lag between the mass injected into the separated region at reattachment and the mass leaving spanwise at the horseshoe vortex core govern the observed shock motion.

Free-surface liquid metal flows are a leading candidate for the plasma-facing components of future fusion reactors. Existing transient, three-dimensional, free-surface MHD solvers rely on the inductionless approximation in which the induced magnetic field is neglected. This paper extends the open-source solver FreeMHD [B. Wynne et al., Phys. Plasmas 32, 013907 (2025)] beyond the inductionless approximation to resolve the induced magnetic field self-consistently using a vector-potential formulation that enforces $\nabla\cdot\boldsymbol{B}=0$ by construction while preserving the original multi-region, two-phase framework. The solver is verified against analytical Shercliff and Hunt duct-flow solutions across a range of Hartmann numbers and validated against free-surface height measurements from the LMX-U experiment. To the best of our knowledge, FreeMHD2 is the first open-source, experimentally validated free-surface liquid metal solver to resolve the evolution of the induced magnetic field without invoking the inductionless approximation. By removing this approximation rather than relaxing it, the formulation provides the basis for future modeling of the finite magnetic Reynolds number conditions expected in large-scale, transient fusion events.

The dynamic response of a zero-pressure gradient turbulent boundary layer (TBL) to a large-scale perturbation in the outer region was investigated experimentally. The baseline TBL had a moderate Reynolds number such that there was no naturally occurring energetic large-scale structure (LSS) present. An active plasma-based actuator was then placed in the outer region of the TBL to introduce a periodic, spanwise-uniform, synthetic LSS. This novel actuation scheme provides a new tool by which to experimentally examine the `top-down' view of TBL dynamics/interactions. The TBL response to this synthetic structure was investigated using a combination of planar particle imaging velocimetry and spanwise offset hot-wires, over a large streamwise extent downstream of the actuator device. Phase-locked analysis was implemented to isolate and measure the streamwise development of large-scale motions and changes in turbulence amplitude induced by this synthetic LSS. A strong correlation was observed between large-scale motions near the wall, linearly superimposed from the synthetic LSS, and a periodic modulation of turbulence amplitudes. This periodic modulation was found to be linked to phase-dependent changes in both the production and transport of turbulence driven by the induced large-scale motions. The phase speed of these induced large-scale motions, coupled with intermittent changes to spanwise coherence near the wall, revealed an additional, but transient, effect of the synthetic LSS on near-wall cycle dynamics. Overall, these results characterize the influences, and limitations, of top-down interactions on global TBL dynamics.

This study presents a comparative investigation of Physics-Informed Neural Networks (PINNs) and data-driven Deep Operator Networks (DeepONets) for predicting the evolution of plane Newtonian Couette flow with dynamic wall slip. While traditional numerical methods, such as the Crank-Nicolson scheme, offer high accuracy, their computational demand poses challenges in real-time applications. To address this, we first implement a PINN framework to solve the governing equations for specific physical parameters. Subsequently, we develop a data-driven DeepONet, trained on high-fidelity numerical data, to learn the continuous solution operator across a broad range of slip boundary conditions and upper wall velocities. Our results indicate that while the PINN achieved superior point-wise precision with a relative L_2 error of 0.083%, it remains constrained by the requirement for instance-specific retraining. In contrast, the DeepONet demonstrates robust generalization on unseen and out-of-distribution signals with a mean relative error of 0.36% and 0.88%, respectively. Most notably, it provides near-instantaneous inference, achieving a speedup factor of approximately 540X over the numerical solver and 30.5% over the PINN. This work demonstrates the synergy between physics-based and data-driven architectures and establishes DeepONet as a highly efficient surrogate model for rapid parametric exploration and real-time fluid dynamics forecasting.

Quantum systems governed by unitary and reversible microscopic dynamics may nevertheless exhibit equilibration, in the sense that some effective description becomes time-independent. Standard equilibration results usually consider two separate situations: system-environment structures, in which the composite system evolves unitarily while the system of interest equilibrates, and restricted measurements, such as coarse-grained POVMs and observables, in which the measurement statistics equilibrate. Here, we bring these descriptions into a common state-level framework using the concept of generalized subsystems, where the accessible effective state appears as the output of a quantum channel acting on the microscopic state. We derive bounds showing that generalized subsystems equilibrate when their dimension is small compared with the effective dimension of the discarded microscopic information. We further show that this condition is met for typical initial states in large subspaces and that the resulting equilibrium description is largely insensitive to microscopic initial details. The framework recovers the usual equilibration bounds for ordinary subsystems and finite families of POVMs. As an example, we also introduce a finite-resolution energy channel that maps unresolved microscopic energy levels into effective energy levels, thereby making residual effective coherences explicit and showing how spectral multiplicities constrain those coherences while strengthening equilibration. Our results provide a unified state-level formulation of quantum equilibration under general forms of limited accessible information.

Spectral kissing, recently realized in a Kerr parametric oscillator (KPO), refers to the merging of pairs of energy levels and arises as a manifestation of an excited-state quantum phase transition (ESQPT). Here, we show that this phenomenon has a dissipative counterpart encoded in the spectrum of the steady-state density matrix. Using a dissipative KPO as a representative example, we demonstrate that, in the weak-dissipation regime, the eigenvalues of the steady-state density matrix organize into quasi-degenerate pairs that mirror the spectral kissing of the corresponding closed system. As the dissipation strength increases, this pairing gradually disappears. By analyzing the classical limit of the system, we derive analytical expressions for the critical lines governing both the onset of steady-state spectral kissing and its disappearance at a dissipative phase transition.

The effect of a large environment on a finite-size quantum mechanical system is two-fold: It brings dissipation, but also fluctuations of thermal and quantum origin. While dissipation tends to stabilize the dynamics, we question if and how environmental quantum fluctuations can generate chaos in an otherwise classically non-chaotic system. We work out a paradigmatic model of quantum optics: the dissipative Dicke model, where a large spin interacts with a dissipative harmonic mode. We dial in the classical/quantum correspondence by working in the semiclassical regime at large but finite spin. We demonstrate that, starting from a classically regular phase space in the superradiant regime, quantum noise can generate a strange attractor with fractal dimension and a positive Lyapunov exponent. We unveil the deep connection with shear-induced chaos that was recently developed in the mathematical community.

The ground state of random Hamiltonians with all-to-all interactions such as the quantum Sherrington-Kirkpatrick (SK) model and the Sachdev-Ye-Kitaev (SYK) model follow volume-law entanglement and are expected to be hard to model using tensor networks. In recent years, some progress has been made to push the limit of classical methods using neural quantum states. However, it remains an open question whether there exist quantum algorithms that could offer a quantum advantage over the state-of-the-art classical methods in simulating random Hamiltonians. In this work, we show that one such algorithm, TETRIS-ADAPT-VQE, can construct accurate ground states for dense and sparse SYK models containing up to $N=20$ Majorana fermions achieving fidelities $\geq 99.3\%$ and for the quantum SK model with up to $L=18$ sites achieving fidelities $\geq 99.9998\%$. We find that while the preparation of ground states is efficient (in terms of operator pool size and circuit depth) for the SK model, it is not efficient for either dense or moderately sparse SYK models.

Ultra-cold bosonic systems can be tuned to exhibit quantum phase transitions. For example, the Rabi-coupled bosonic system exhibits ferromagnetic and paramagnetic phases, whereas the spin-orbit-coupled system exhibits exciting phases such as supersolidity. The physics of these phases and phase transitions is very rich. It is an important topic of research to probe these phases and phase transitions using various tools in many-body physics. The operator product expansion (OPE) provides one such tool. It expresses the product of two separated operators as a series expansion of local operators. In this article, we will derive the OPE of two operators $ψ^\dagger_σ(\vec r)$ and $ψ_{σ'}(\vec r')$. More specifically, we look for the contact density term, which controls many of the universal physics of the underlying bosonic system.

Quantum impurity models with strong electron correlations, such as the paradigmatic Anderson Impurity Model (AIM), are central to our understanding of a range of physical phenomena including local moment formation, Coulomb blockade and Kondo screening. They describe magnetic atoms and molecules on surfaces, quantum dot circuits, and correlated materials through dynamical mean field theory. The physics of such systems in strongly non-equilibrium conditions is particularly complex and challenging to capture, whereas Gaussian models of free fermions can be easily solved. Here we show that the time-evolving dynamics of the AIM after a quench can be described by a completely non-interacting version of the model, at the expense of coupling to additional static auxiliary degrees of freedom. Starting from the full solution of the quenched AIM using ED and DMRG, we study the properties of this mapping using numerical optimization, and uncover intriguing structure in the auxiliary system. The method allows us to understand interacting non-equilibrium dynamics through the simpler lens of an effective non-interacting system of larger dimension.

Characterizing electronic band structures requires precise knowledge of wave functions and their quantum geometry. Here, we introduce a tomography method to reconstruct the density matrix of electron states from quasiparticle interference maps around single impurities. We consider two-orbital models on a honeycomb lattice, relevant to graphene heterostructures and direct-gap semiconductors. For on-site impurities, backscattering between time-reversed states directly maps the density matrix populations and coherences into distinct orbital contributions in the interference map. While local probes usually lack orbital resolution, these orbital contributions transform under distinct symmetry group representations and can thus be disentangled to reveal the density matrix and quantum geometric tensor of the scattering states. This establishes impurities as tomographic probes for band structures in scanning tunneling microscopy using conventional, unpolarized tips.

We demonstrate that all anti-self-dual Einstein metrics with non--zero cosmological constant $Λ$ locally arise from solutions of a single second order PDE introduced by Lipstein and Nagy. We show how this equation fits into the hyper--heavenly formalism of Plebański, and establish a Lax pair. Finally we show how Plebański's second heavenly equation arises in the limit as $Λ\rightarrow 0$.

We analyze a two-dimensional Bose-Fermi mixture at zero temperature in the presence of a tunable Bose-Fermi attraction. We adopt a diagrammatic T-matrix approach and study the behavior of several thermodynamic quantities for the two species as functions of density, mass ratio, and coupling strength. These include the chemical potentials, the boson momentum distribution function, the condensate density, and Tan's contact parameter. We analytically demonstrate that the present T-matrix formalism recovers the correct second-order perturbative expansion of the chemical potentials in the weak-coupling regime, and test it numerically. The near-universal behavior of the condensate fraction already found in prior work for the mass-balanced case is confirmed for different masses and becomes even more accurate when the boson mass is large. The mass imbalance emerges as an additional control parameter that qualitatively affects the bosonic momentum distribution. In particular, we found that it can be used to allow for the experimental observation of a peculiar peak in the boson momentum distribution at finite momentum.

Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena. Previous work demonstrated that Long Short-Term Memory (LSTM) networks can successfully predict forecast errors in the High-Resolution Rapid Refresh (HRRR) model using mesonet observations, but we believe performance degradation is linked to periods of complex vertical atmospheric evolution. To address this limitation, we develop a hybrid LSTM-Vision Transformer (LSTM-ViT) framework that combines temporal sequence learning from surface observations with atmospheric profiles from the New York State Mesonet profiler network. The LSTM-ViT framework is trained to predict HRRR hourly precipitation, 10 m wind speed, and 2 m temperature forecast errors at individual mesonet stations. Across all three predictors, incorporation of profiler-derived atmospheric structure improves forecast error prediction skill relative to the baseline LSTM architecture, with the largest gains occurring at shorter forecast lead times and during periods of enhanced PBL activity. Improvements are particularly pronounced for precipitation forecast error, where the LSTM-ViT framework achieves approximately a twofold increase in predictive skill relative to the baseline LSTM while better capturing convectively driven error evolution and reducing degradation associated with PBL processes. These results demonstrate that combining temporal sequence learning with vertically informed attention mechanisms provides a physically meaningful pathway for improving forecast error prediction in operational NWP systems. Our research offers forecasters enhanced guidance regarding model bias and forecast confidence.

Two-dimensional electronic spectroscopy (2DES) offers unique insights into the coupling between electronic and nuclear motion and dynamics, making it a key technique in diverse fields, including materials science and biology. Obtaining 2DES data requires a series of measurements that involve multiple pulses to construct the full picture - a time-consuming task that often necessitates working with limited or noisy data. Here we introduce a machine-learning based framework that aims to maximize the data that can be extracted from 2DES experiments and provides guidance towards the selection of additional experiments. We design a Gaussian mixture model to learn the underlying spectral density of a system, allowing the extraction of vibronic couplings and the extrapolation of the 2DES spectra to other time delays beyond those measured, and demonstrate how our framework can be used to select additional measurements to further improve the accuracy. We show that our approach yields accurate results on a variety of systems, including simulations ranging from photoactive yellow protein in the gas phase to Nile red in benzene to the anionic green fluorescent protein chromophore in water, and experiments on Nile blue in ethanol. Our work provides an efficient route to extract maximum insights from 2DES while incurring minimal experimental costs.

This paper introduces a comprehensive two-dimensional analytical model of a toroidal magnetic ring with circular cross-section under sinusoidal excitation. Applying Maxwell's equations in local polar coordinates within a complex permeability, the model derives analytical expressions for the internal magnetic field, magnetic flux, complex impedance, and total losses. It rigorously separates the contributions of eddy current losses, hysteresis losses, and winding losses, while explicitly incorporating the skin effect in the conductive core via Bessel functions. An expression for the apparent permeability is also provided, enabling the nonlinear core behavior to be mapped onto simplified linear material models. The resulting analytical model offers a computationally efficient and accurate foundation for standardized magnetic material characterization, such as Brockhaus and Iwatsu ring measurements, as a powerful alternative to 2D and 3D finite element analysis.

The chirality-induced spin selectivity (CISS) effect, discovered by Naaman and collaborators in 1999, describes the emergence of a finite spin polarization in response to current flow through a chiral electronic system. While extensive experimental studies have verified the presence of CISS in molecular systems and, more recently, in chiral materials, a complete microscopic understanding of this effect remains elusive. In this work, we propose a theoretical framework linking the CISS effect to the orbital Edelstein effect. In the latter, a drive current induces a finite orbital magnetization, even in the absence of spin-orbit coupling. Our non-equilibrium theory naturally explains key features of the CISS effect: its persistence in systems with weak or vanishingly small spin-orbit coupling and its connection to natural optical activity, a distinctive signature of chiral systems.

Identifying the lowest-energy surface-adsorbate configuration is critical for modeling heterogeneous catalysis, yet exhaustive exploration with ab initio calculations is computationally prohibitive. Machine-learning force fields (MLFFs) accelerate structural relaxation but leave the search over the vast configurational space a major bottleneck, and open-loop large language model (LLM) agents lack a physics-grounded feedback mechanism to correct erroneous initial guesses. We propose AdsMind (Adsorption configuration discovery with Machine intelligence and relaxation feedback), a closed-loop multi-agent framework that enables autonomous error correction through MLFF relaxation feedback. Across four LLM backends, AdsMind achieves consistently high search reliability, with success rates of 100% and 98.8% on the benchmarks AA20 and OCD-GMAE62. Relative to its single-pass (1-Shot) ablation it reduces cross-backend energy dispersion, and it uses only 4.11 and 4.67 MLFF relaxations per case, respectively -- an approximately 14-fold reduction over heuristic enumeration baselines. Density functional theory (DFT) validation using VASP/PBE on six representative AA20 systems shows that the reported open-loop Adsorb-Agent outputs exhibit qualitative adsorption-energy sign errors for molecular adsorbates, whereas AdsMind preserves the correct sign in all tested cases with closer quantitative agreement. AdsMind thus delivers reliability, self-reflection, and interpretability simultaneously, supporting more DFT-informed autonomous chemistry workflows.

The ability to compute electron-phonon coupling from first principles, using density functional perturbation theory and interpolation techniques, has enabled predictive calculations of electronic transport coefficients in crystalline materials. However, these methods are still computationally expensive. Here we present two inexpensive methods to obtain thermoelectric transport properties of semiconductors using a small number of electron-phonon matrix elements calculated from first principles. The first method combines models for coupling of electrons with different phonon modes whose parameters are obtained from $\sim 10$ matrix elements per electronic band and phonon mode calculated from first principles. Within this method, we formulate the acoustic deformation potential model for arbitrary crystal symmetries and band extrema locations. The second method uses machine learning to interpolate $\sim 100$ electron-phonon matrix elements per electronic band and phonon mode on dense reciprocal space grids in the parts of the Brillouin zone relevant for transport. We apply both methods to two-dimensional MoS$_2$ and show very good agreement with the state-of-the-art method. The calculated thermoelectric properties also agree well with experiments. We find that the machine-learning method is more accurate and straightforward to implement compared to the model approach.

In this study, the structural, mechanical, electronic, optical, and thermoelectric properties of the novel lead-free halide double perovskite series beta2SnGeX6 (beta = K, Rb; X = Cl, Br, I) are systematically investigated using density functional theory (DFT). Calculated formation energies, Tolerance factors, and octahedral factors confirm that all six compounds exhibit robust thermodynamic stability within a highly symmetric cubic geometry. Mechanical analysis derived from elastic parameters characterizes the entire series as fundamentally ductile, ensuring high processing elasticity and resistance to micro-cracking during device manufacturing. Electronic band structures reveal direct bandgaps showing exceptional composition-dependent tunability from 1.44 eV down to 0.64 eV via progressive halogen substitution. The wide gap chloride variations are optimized for single-junction photovoltaic absorbers, while the narrower-gap bromide and iodide analogs show immense promise for tandem solar architectures and near-infrared photodetectors. Thermoelectrically, heavy constituent atoms introduce strong lattice anharmonicity and intense high-temperature Umklapp phonon scattering, significantly suppressing lattice thermal conductivity. Combined with low carrier effective masses that optimize electrical transport, the iodide compounds achieve higher power factors and outstanding dimensionless figures of merit (ZT = 2.4 for K2SnGeI6 at 1000 K). Ultimately, these lead-free double perovskite family emerges as an environmentally benign and versatile platform for next-generation green optoelectronics and solid-state waste-heat recovery.

Iron germanium telluride (FGT; FemGenTe2) compounds have attracted significant interest due to their layered van der Waals structure, relatively high Curie temperature, and tunable magnetic properties. Chemical vapor deposition (CVD) is a particularly promising synthesis route owing to its simplicity, low cost, potential for scalability, and widespread adoption in the semiconductor industry, yet it has not been used previously to synthesize FGT with dopants. Here, we report CVD synthesis of both undoped and Ni-doped FGT nanosheets on SiO2/Si substrates. By adjusting precursor molar ratios, we synthesized Ni-doped FGT with multiple Fe concentrations and a 4% Ni-to-Fe ratio. X-ray photoelectron spectroscopy depth profiling further demonstrates that Ni is present in the bulk of the crystals. This straightforward, low-cost, and CMOS-compatible approach demonstrates a route to Ni-doped FGT nanosheets, establishing a foundation for future characterization of Ni-doped FGT and its potential integration into spintronic devices.

Proton dissociation constants (pKa) are critical for functional molecule discovery and molecular modeling. Building on iBonD, the largest experimental pKa database established, we and other researchers have developed several methods including machine-learning-based empirical prediction and high-accuracy energy calculations. Despite this foundation, the rapid augmentation of high-quality pKa data remains fundamentally constrained. As part of this work, we performed large-scale regression-based pKa prediction on unlabeled molecular datasets using a collection of extensively optimized machine-learning models. The results indicate that, since the feature distributions of unlabeled molecular datasets, the pKa data distribution approximates normality, with extreme scarcity of tail-region samples. Although such augmentation is highly valuable for improving overall data availability and predictive modeling, it remains insufficient for efficiently discovering molecules with broad-spectrum pKa properties. To address this, we explore the targeted generation of molecules with sparse pKa properties from the vast chemical space. Given that traditional continuous latent space VAE-RNN methods for molecular generation suffer from insufficient stability and fail to demonstrate clear advantages in complementing sparse data, we design and implement a quantum-assisted sparse-pKa molecular generation. Feasibility is validated on a simulated quantum annealer, and superior extreme-value sampling is further achieved on physical coherent Ising machines (CIMs). (to be continued)