This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular ...dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.
Curie's principle states that “when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them.” We demonstrate that symmetry equivariant neural networks uphold ...Curie's principle and can be used to articulate many symmetry-relevant scientific questions as simple optimization problems. We prove these properties mathematically and demonstrate them numerically by training a Euclidean symmetry equivariant neural network to learn symmetry breaking input to deform a square into a rectangle and to generate octahedra tilting patterns in perovskites.
Abstract
A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, ...however, imposes an effective limit of about a few dozen atoms on traditional electronic structure calculations. We present a machine learning (ML) method to break through this scaling limit for electron densities. We show that Euclidean neural networks can be trained to predict molecular electron densities from limited data. By learning the electron density, the model can be trained on small systems and make accurate predictions on large ones. In the context of water clusters, we show that an ML model trained on clusters of just 12 molecules contains all the information needed to make accurate electron density predictions on cluster sizes of 50 or more, beyond the scaling limit of current quantum chemistry methods.
Deep learning algorithms are responsible for a technological revolution in a variety of tasks including image recognition or Go playing. Yet, why they work is not understood. Ultimately, they manage ...to classify data lying in high dimension – a feat generically impossible due to the geometry of high dimensional space and the associated curse of dimensionality. Understanding what kind of structure, symmetry or invariance makes data such as images learnable is a fundamental challenge. Other puzzles include that (i) learning corresponds to minimizing a loss in high dimension, which is in general not convex and could well get stuck bad minima. (ii) Deep learning predicting power increases with the number of fitting parameters, even in a regime where data are perfectly fitted. In this manuscript, we review recent results elucidating (i, ii) and the perspective they offer on the (still unexplained) curse of dimensionality paradox. We base our theoretical discussion on the (h,α) plane where h controls the number of parameters and α the scale of the output of the network at initialization, and provide new systematic measures of performance in that plane for two common image classification datasets. We argue that different learning regimes can be organized into a phase diagram. A line of critical points sharply delimits an under-parametrized phase from an over-parametrized one. In over-parametrized nets, learning can operate in two regimes separated by a smooth cross-over. At large initialization, it corresponds to a kernel method, whereas for small initializations features can be learnt, together with invariants in the data. We review the properties of these different phases, of the transition separating them and some open questions. Our treatment emphasizes analogies with physical systems, scaling arguments and the development of numerical observables to quantitatively test these results empirically. Practical implications are also discussed, including the benefit of averaging nets with distinct initial weights, or the choice of parameters (h,α) optimizing performance.
Deep learning has been immensely successful at a variety of tasks, ranging from classification to artificial intelligence. Learning corresponds to fitting training data, which is implemented by ...descending a very high-dimensional loss function. Understanding under which conditions neural networks do not get stuck in poor minima of the loss, and how the landscape of that loss evolves as depth is increased, remains a challenge. Here we predict, and test empirically, an analogy between this landscape and the energy landscape of repulsive ellipses. We argue that in fully connected deep networks a phase transition delimits the over- and underparametrized regimes where fitting can or cannot be achieved. In the vicinity of this transition, properties of the curvature of the minima of the loss (the spectrum of the Hessian) are critical. This transition shares direct similarities with the jamming transition by which particles form a disordered solid as the density is increased, which also occurs in certain classes of computational optimization and learning problems such as the perceptron. Our analysis gives a simple explanation as to why poor minima of the loss cannot be encountered in the overparametrized regime. Interestingly, we observe that the ability of fully connected networks to fit random data is independent of their depth, an independence that appears to also hold for real data. We also study a quantity Δ which characterizes how well (Δ<0) or badly (Δ>0) a datum is learned. At the critical point it is power-law distributed on several decades, P_{+}(Δ)∼Δ^{θ} for Δ>0 and P_{-}(Δ)∼(-Δ)^{-γ} for Δ<0, with exponents that depend on the choice of activation function. This observation suggests that near the transition the loss landscape has a hierarchical structure and that the learning dynamics is prone to avalanche-like dynamics, with abrupt changes in the set of patterns that are learned.
Large-scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will ...require picking them out of at least tens of millions of images, and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects, reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100 000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. Having multi-band, ground based data is found to be better for this purpose than single-band space based data with lower noise and higher resolution, suggesting that multi-colour data is crucial. Multi-band space based data will be superior to ground based data. The most difficult challenge for a lens finder is differentiating between rare, irregular and ring-like face-on galaxies and true gravitational lenses. The degree to which the efficiency and biases of lens finders can be quantified largely depends on the realism of the simulated data on which the finders are trained.
•A new way of protecting solar thermal systems from overheating has been proposed.•V1-xWxO2 films were deposited by RF magnetron co-sputtering using V-metal target.•The W-concentration in V1-xWxO2 ...films was precisely determined by RBS analysis.•The dielectric function of V1-xWxO2 films were inferred in the VIS-MIR range.•The thermal coupling between the absorber and the glazing has been studied.
Overheating is a common problem both with the use of active and passive solar energy in thermal solar energy systems and in highly glazed buildings, even in central European latitudes. In solar thermal collectors, the elevated temperatures occurring during stagnation result in reduced lifetime of the collector materials. They lead to water evaporation, glycol degradation and stresses in the collector with increasing vapor pressure. Special precautions are necessary to release this pressure; only mechanical solutions exist nowadays. The temperature of degradation of glycols is above 160–170°C. However, it would be preferable to limit the temperature of the collector to approximately 100°C, avoiding likewise the evaporation of the used water-glycol mixture. Additionally, the elevated temperatures lead to degradation of the materials that compose the collector, such as sealing, thermal insulation and the selective absorber coating. A new way of protecting solar thermal systems without any mechanical device (e.g. for shading or for pressure release) is proposed. A durable inorganic thermochromic material, which exhibits a change in optical properties at a transition temperature Tt, is vanadium dioxide (VO2). At 68°C, VO2 undergoes a reversible crystal structural phase transition accompanied by a strong variation in optical properties. Therefore, a dynamical switching of the thermal emittance ∊th can be achieved by VO2. By doping the material with tungsten, it is possible to lower the transition temperature making it suitable as a glazing coating. The possibility of using the switch in emittance of the absorber coating in order to trigger the transition of a thermochromic coating on the glazing of the solar collector has been studied. An analytical approach yielded the required transition temperature of such a switching glazing. The fascinating optical properties of these switchable films elucidate the way towards novel intelligent thermal solar collector materials.
Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption ...of the World Health Organization’s Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI’s potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars—models, data, feasibility/equity, and regulation/innovation—through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.
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