The expansion of evidence-based medicine has profoundly shaped contemporary critical care practice, with randomized trials and guidelines increasingly informing bedside decisions. While trainees are often familiar with the headline results of major studies, many struggle to critically appraise trial design, methodology, and applicability to individual patients. This gap risks uncritical adoption of evidence without adequate consideration of context, limitations, or potential harms. Drawing on Richard Feynman's distinction between knowing labels and understanding underlying principles, this viewpoint argues that knowing trial outcomes is not equivalent to understanding the evidence itself. We highlight how headline-driven interpretations can mislead clinical practice and emphasize the importance of critical appraisal as a core clinical skill. A practical checklist is proposed to support structured bedside interpretation of ICU trials. Sundarsingh V, Mishra SB, Kumar RM. From Trial Results to Bedside Judgment: A Feynman Lesson in Evidence-based Critical Care. Indian J Crit Care Med 2026;30(5):368-370.
In the 1970s, physicist Richard Feynman turned lunch with a friend into a math problem-how to optimize dish selection over multiple meals-but his handwritten notes remained a mystery for decades. Here we present the fully deciphered problem and solution, prove its optimality, generalize it to related problems, and compare the results to human behavior. The optimal policy specifies decreasing thresholds for switching from exploring new dishes to exploiting the best, with thresholds varying based on the distribution of the quality of dishes. We connect these results to the existing psychological literature on optimal stopping problems, which has explored close variants on Feynman's problem, and use our generalization of the solution to explore how the underlying distribution of the quality of the options influences people's choices. A preregistered experiment with 2,520 participants shows that people adopt thresholds that decrease linearly with the proportion of trials remaining, consistent with the observation of linear thresholds in other optimal stopping problems. However, we show that people tend to explore more than predicted by linear thresholds, and that different distributions of quality result in thresholds with the same slope but different intercepts. These results indicate that people adapt linear thresholds used in optimal stopping tasks in a way that is sensitive to the underlying distribution-a simple strategy that we show is nearly as effective as Feynman's solution.
X-ray Thomson scattering (XRTS) constitutes an essential technique for diagnosing material properties under extreme conditions, such as high pressures and intense laser heating. Time-dependent density functional theory (TDDFT) is one of the most accurate available ab initio methods for modeling XRTS spectra, as well as a host of other dynamic material properties. However, strong thermal excitations, along with the need to account for variations in temperature and density as well as the finite size of the detector significantly increase the computational cost of TDDFT simulations compared to ambient conditions. In this work, we present a broadly applicable method for optimizing and enhancing the efficiency of TDDFT calculations. Our approach is based on a one-to-one mapping between the dynamic structure factor and the imaginary time density-density correlation function, which naturally emerges in Feynman's path integral formulation of quantum many-body theory. Specifically, we combine rigorous convergence tests in the imaginary time domain with a constraints-based attenuation of narrow-band fluctuations to improve the efficiency of TDDFT modeling without the introduction of any significant bias. As a result, we can report a speed-up by up to an order of magnitude, thus substantially reducing the burden of computational cost required for XRTS analysis.
Feynman attributed long-range dispersion forces to the attraction of each nucleus to the local dipolar distortion of the electronic charge distribution. Here we take a step toward the first demonstration of Feynman's statement with full configuration-interaction wave functions. We have used Stone's distributed multipole analysis (DMA) to obtain the local multipoles in H2 in the b3Σ+u and X1Σ+g states and the local dipoles in HeH and He⋯He in their ground states. Except for the H2 singlet, these states have repulsive potentials with shallow wells due to van der Waals dispersion. For H2, the DMA dispersion dipole on each nucleus, computed ab initio with the d-aug-cc-pV6Z basis, shows excellent agreement with the sum of the R-7 and R-9 terms predicted by perturbation theory. The DMA dipoles of HeH and He⋯He also agree quite well with the prediction of perturbation theory. The signs and the R-dependence of the DMA dispersion dipoles are fully consistent with Feynman's statement. For H2, we also find strong agreement between the results of perturbation theory and the dispersion terms in the DMA quadrupoles, DMA octopoles, DMA hexadecapoles, the total quadrupoles, and the total hexadecapoles. The dynamic correlation effects on the multipoles have physical meaning when computed with sufficiently large basis sets.
Short-lived, unobservable, and not subject to the usual rules of conservation of energy and momentum, virtual particles-an integral part of the conceptual framework of quantum field theory (QFT)-exhibit a number of curious characteristics which, in recent decades, have in part fueled important discussions about their ontological status. Central to these debates is Richard Feynman's diagrammatic technique for QFT calculations, which provided in the late 1940s the first systematized and generalized description of the concept of virtual particles. At the time, however, the curious characteristics and the ontology of the latter were the subject of little, if any, debate. This article explores how the concept of virtual particles gradually became subject to interpretative scrutiny in the post-war period. It examines the weight of various aspects of pre-Feynman developments which once guaranteed a firmer phenomenological anchoring of the scientific practices associated with the virtual particle concept. Subsequently, it shows how the questioning of this concept did not result from a simple assessment of its curious characteristics but was part of a wider critique of the new quantum electrodynamics and Feynman's methods.
Dipolar Bose-Einstein condensates (dBECs) exhibit a plethora of physics phenomena, from supersolidity to the rotonlike minimum in the elementary excitation spectrum. In this work we first demonstrate the existence of axis-symmetric solitary waves in (quasi-)two-dimensional dBECs: these localized excitations are characterized by quantized vortex dipoles that continuously transit to vortex-free density depletions. We then show how the presence of the roton minimum fundamentally alters the fate of such solutions when approaching Landau's critical speed: when propagating along the polarization direction where the roton minimum occurs, the solitary wave transits into roton excitations rather than into phonons as for standard contact-interaction BECs. This finding suggests that Feynman's hypothesis, conjectured for 3D superfluid liquid helium regarding the creation of rotons as fading vortex excitations, is valid in the context of 2D dBECs.
Feynman's statement, "There is plenty of room at the bottom", underscores vast potential at the atomic scale, envisioning microscopic machines. Today, this vision extends into 3D space, where thousands of atoms and molecules are volumetrically patterned to create light-driven technologies. To fully harness their potential, 3D designs must incorporate high-refractive-index elements with exceptional mechanical and chemical resilience. The frontier, however, lies in creating spatially patterned micro-optical architectures in glass and ceramic materials of dissimilar compositions. This multi-material capability enables novel ways of shaping light, leveraging the interaction between diverse interfaced chemical compositions to push optical boundaries. Specifically, it encompasses both multi-material integration within the same architectures and the use of different materials for distinct architectural features in an optical system. Integrating fluid handling systems with two-photon lithography (TPL) provides a promising approach for rapidly prototyping such complex components. This review examines single and multi-material TPL processes, discussing photoresin customization, essential physico-chemical conditions, and the need for cross-scale characterization to assess optical quality. It reflects on challenges in characterizing multi-scale architectures and outlines advancements in TPL for both single and spatially patterned multi-material structures. The roadmap provides a bridge between research and industry, emphasizing collaboration and contributions to advancing micro-optics.
The Feynman path integral formalism for non-relativistic quantum mechanics is revisited. A comparison is made with cases of light propagation (Huygens' principle) and Brownian motion. The difficulties for a physical model applying Feynman's formalism are pointed out. A reformulation is proposed, where the transition probability of a particle from one space-time point to another one is the sum of probabilities of the possible paths. As an application, Born approximation for scattering is derived within the formalism, which suggests an interpretation involving the stochastic motion of a particle rather than the square of a wavelike amplitude.
The nanomedicine revolution represents a paradigm shift in modern health care, leveraging nanoscale materials to achieve unprecedented precision, personalization, and performance in diagnosis and therapy. Emerging from Feynman's foundational vision of nanotechnology, nanomedicine integrates nanoengineering, materials science, and biotechnology to enable targeted drug delivery, advanced imaging, and regenerative applications. Nanoparticles (NPs) function as intelligent carriers that enhance bioavailability and minimize systemic toxicity, while nanoscale contrast agents redefine diagnostic accuracy through enhanced magnetic resonance imaging (MRI), computed tomography (CT), and photoacoustic imaging (PAI). In oncology and infectious diseases, nanomedicine's capacity for selective targeting and antimicrobial innovation is reshaping therapeutic outcomes. The convergence of nanotechnology with artificial intelligence (AI) and machine learning (ML) further facilitates predictive modeling, smart NP design, and real-time clinical decision-making. Despite persistent challenges in safety, scalability, and ethical regulation, nanomedicine is emerging as a cornerstone of precision health care, where " tiny tech " delivers an outsized impact on human health. Based on an extensive review of scientific studies published between 1964 and 2025, this article discusses the fundamental principles, biomedical applications, and transformative role of nanomedicine in shaping the next generation of personalized and precision medicine.
Inspired by Richard Feynman's 1959 lecture and the 1966 film Fantastic Voyage, the field of micro/nanorobots has evolved from science fiction to reality, with significant advancements in biomedical and environmental applications. Despite the rapid progress, the deployment of functional micro/nanorobots remains limited. This review of the technology roadmap identifies key challenges hindering their widespread use, focusing on propulsion mechanisms, fundamental theoretical aspects, collective behavior, material design, and embodied intelligence. We explore the current state of micro/nanorobot technology, with an emphasis on applications in biomedicine, environmental remediation, analytical sensing, and other industrial technological aspects. Additionally, we analyze issues related to scaling up production, commercialization, and regulatory frameworks that are crucial for transitioning from research to practical applications. We also emphasize the need for interdisciplinary collaboration to address both technical and nontechnical challenges, such as sustainability, ethics, and business considerations. Finally, we propose a roadmap for future research to accelerate the development of micro/nanorobots, positioning them as essential tools for addressing grand challenges and enhancing the quality of life.
In the thermodynamic analysis of thermoelectric devices, typical irreversibilities are for the processes of finite-rate heat transfer, heat leak, and Joule heating. Approximate analyses often focus on either internal or external irreversibility, obtaining well-known expressions for the efficiency at maximum power (EMP), such as the Curzon-Ahlborn value for the endoreversible model and the Schmiedl-Seifert form for the exoreversible model. Within the Constant Properties model, we simultaneously incorporate internal as well as external irreversibilities. We employ the approximation of a symmetric and small external irreversibility, allowing a tractable expression for EMP that depends on three parameters: (1) the ratio of internal to external thermal conductance, (2) the figure of merit of the thermoelectric material, and (3) the ratio of hot and cold reservoir temperatures. We study limiting forms of this EMP and compare our framework with the exact model as well as with other irreversible models in finite-time thermodynamics, such as the minimally nonlinear model. In particular, we argue that the thermoelectric generator (TEG) in endoreversible approximation can be mapped to the mesoscopic model of Feynman's ratchet in the high-temperatures regime, thus providing an alternative to the viewpoint in literature where the ratchet in linear regime is mapped to an exoreversible TEG model. Finally, extending our study to the thermoelectric refrigerator under similar assumptions as for the generator, we analyze the efficiency at the maximum cooling power.
DL_POLY Quantum 2.0, a vastly expanded software based on DL_POLY Classic 1.10, is a highly parallelized computational suite written in FORTRAN77 with a modular structure for incorporating nuclear quantum effects into large-scale/long-time molecular dynamics simulations. This is achieved by presenting users with a wide selection of state-of-the-art dynamics methods that utilize the isomorphism between a classical ring polymer and Feynman's path integral formalism of quantum mechanics. The flexible and user-friendly input/output handling system allows the control of methodology, integration schemes, and thermostatting. DL_POLY Quantum is equipped with a module specifically assigned for calculating correlation functions and printing out the values for sought-after quantities, such as dipole moments and center-of-mass velocities, with packaged tools for calculating infrared absorption spectra and diffusion coefficients.
Feynman's diagrammatic series is a common language for a formally exact theoretical description of systems of infinitely-many interacting quantum particles, as well as a foundation for precision computational techniques. Here we introduce a universal framework for efficient summation of connected or skeleton Feynman diagrams for generic quantum many-body systems. It is based on an explicit combinatorial construction of the sum of the integrands by dynamic programming, at a computational cost that can be made only exponential in the diagram order on a classical computer and potentially polynomial on a quantum computer. We illustrate the technique by an unbiased diagrammatic Monte Carlo calculation of the equation of state of the 2D SU(N) Hubbard model in an experimentally relevant regime, which has remained challenging for state-of-the-art numerical methods.
The issue of reversibility in hydromechanical sprinklers that auto-rotate while ejecting fluid from S-shaped tubes raises fundamental questions that remain unresolved. Here, we report on precision experiments that reveal robust and persistent reverse rotation under suction and a model that accounts for the observed motions. We implement an ultralow friction bearing in an apparatus that allows for free rotation under ejection and suction for a range of flow rates and arbitrarily long times. Flow measurements reveal a rocketlike mechanism shared by the reverse and forward modes that involves angular momentum flux, whose subtle manifestation in the reverse case stems from centrifugal effects for flows in curved conduits. These findings answer Feynman's long-standing question by providing quantitatively accurate explanations of both modes, and they suggest further inquiries into flux-based force generation and the roles of geometry and Reynolds number.
In this paper, we propose employing electron scattering to realize unitary quantum gates that are controlled by three qubits. Using Feynman's rules, we find an expression for the transition amplitude for scattering from an external electromagnetic source. In this context, the scattering amplitude is modeled as a unitary gate whose state can be regulated. The optimal value of the vector potential needed to implement the gate is obtained by minimizing the difference between the designed gate and the target gate, with the total energy consumed as a constraint. The design algorithm is obtained by discretizing the resulting integral equations into vector equations. This design algorithm can be applied in various fields such as quantum computing, communication, and sensing. It offers a promising approach for developing efficient and accurate gates for quantum information processing. Furthermore, this approach can also be extended to design gates for multi-qubit systems, which are essential for large-scale quantum computing. The use of this algorithm can significantly contribute to the development of practical quantum technologies.
Machine learning has had a significant impact on multiple areas of science, technology, health, and computer and information sciences. Through the advent of quantum computing, quantum machine learning has developed as a new and important avenue for the study of complex learning problems. Yet there is substantial debate and uncertainty in regard to the foundations of machine learning. Here, we provide a detailed exposition of the mathematical connections between a general machine learning approach called Boltzmann machines and Feynman's description of quantum and statistical mechanics. In Feynman's description, quantum phenomena arise from an elegant, weighted sum over (or superposition of) paths. Our analysis shows that Boltzmann machines and neural networks have a similar mathematical structure. This allows the interpretation that the hidden layers in Boltzmann machines and neural networks are discrete versions of path elements and allows a path integral interpretation of machine learning similar to that in quantum and statistical mechanics. Since Feynman paths are a natural and elegant depiction of interference phenomena and the superposition principle germane to quantum mechanics, this analysis allows us to interpret the goal in machine learning as finding an appropriate combination of paths, and accumulated path-weights, through a network, that cumulatively captures the correct properties of an x-to-y map for a given mathematical problem. We are forced to conclude that neural networks are naturally related to Feynman path-integrals and hence may present one avenue to be considered as quantum problems. Consequently, we provide general quantum circuit models applicable to both Boltzmann machines and Feynman path integrals.
This article continues the development of the idea that all human behavior and thinking are innate. A model of thinking and functioning of the brain has been constructed, which is capable of explaining both the accuracy of molecular processes and the innateness of behaviors. The focus of the model is the phase of the wave function of the particle, which is an additional (free) parameter. It should also be emphasized that the phase of the wave function of a particle is inextricably linked with the quantum action S in the Feynman's formulation of quantum mechanics (path integrals). A hypothesis is proposed: the set of particles that make up neurons and the brain is controlled by changing the phases from the outside (by a higher order system). Such a control system must be outside our world because our measurement methods do not allow us to determine the phase of an elementary particle. In a sense, it can be viewed as an extension of Bohm's ideas about the holographic brain and the holographic universe. Experiments are proposed that could confirm or disprove this model.
Kinetic isotope effect values on the decarboxylation of 3-carboxybenzisoxazole have been computed using the second-order Kleinert's variational perturbation theory in the framework of Feynman's path integrals along with the potential energy surface obtained at the MP2/6-31+G(d) level. Good agreement with the experimental data was obtained, demonstrating that this novel computational approach for computing KIE values of organic reaction is a viable alternative to the traditional method employing the Bigeleisen equation and harmonic vibrational frequencies. Compared with the experimental measurements, consideration of anharmonicity and tunneling effects can significantly improve the calculated KIE values, reducing the root-mean-square deviation from 1.19 % for traditional method to 0.20 % for path-integral method.