Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

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Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

Quantum computing not only holds the potential to solve long-standing problems in quantum physics, but also to offer speed-ups across a broad spectrum of other fields. Access to a computational space that incorporates quantum effects, such as superposition ...

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

The chemisorption energy of reactants on a catalyst surface, Eads, is among the most informative characteristics of understanding and pinpointing the optimal catalyst. The intrinsic complexity of catalyst surfaces and chemisorption reactions presents signi ...

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

Over the last two decades, many technological and scientific discoveries, ranging from the development of materials for energy conversion and storage through the design of new drugs, have been accelerated by the use of preliminary in silico experiments, to ...

Continuous assessment of transferable forcefields for molecular simulations is essential to identify their weaknesses and direct improvement efforts. The latest efforts focused on better describing disordered proteins while retaining proper description of ...

Nanoporous single-layer graphene is promising as an ideal membrane because of its extreme thinness, chemical resistance, and mechanical strength, provided that selective nanopores are successfully incorporated. However, screening and understanding the tran ...

In the current deep learning paradigm, the amount and quality of training data are as critical as the network architecture and its training details. However, collecting, processing, and annotating real data at scale is difficult, expensive, and time-consum ...

Modifying the properties of bio-based materials has garnered increasing interest in recent years. In related applications, the ability of alginates to complex with metal ions has been shown to be effective in liquid-to-gel transitions, useful in the develo ...

Non-covalent interactions are ubiquitous in nature. The diversity and functionality of these interactions are employed by nature as a universal tool for building the molecular mechanism of life. Although weak, these interactions play a key role in both for ...

Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottlen ...

Lattice dynamics in low-dimensional materials and, in particular, the quadratic behaviour of the flexural acoustic modes play a fundamental role in their thermomechanical properties. A first-principles evaluation of these can be very demanding, and can be ...

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals. Approach. EEG recordings of 17 heal ...

Atomistic-continuum multiscale modelling is becoming an increasingly popular tool for simulating the behaviour of materials due to its computational efficiency and reliable accuracy. In the case of ferromagnetic materials, the atomistic approach handles th ...

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. Automatic cough counting tools must provide accurate information, while runnin ...

Enteroviruses are common agents of infectious disease that are spread by the fecal-oral route. They are readily inactivated by mild heat, which causes the viral capsid to disintegrate or undergo conformational change. While beneficial for the thermal treat ...

Due to the subtle balance of intermolecular interactions that govern structure-property relations, predicting the stability of crystal structures formed from molecular building blocks is a highly non-trivial scientific problem. A particularly active and fr ...

Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal–organic frameworks (MOFs). To aid disco ...

Physics-Inspired Equivariant Descriptors of Nonbonded Interactions

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Low-Rank Tree Tensor Network Operators for Long-Range Pairwise Interactions

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

Scalable Quantum Algorithms for Noisy Quantum Computers

Efficient and insightful descriptors for representing molecular and material space

Interpreting chemisorption strength with AutoML-based feature deletion experiments

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

A general and efficient framework for atomistic machine learning

Assessment of transferable forcefields for protein simulations attests improved description of disordered states and secondary structure propensities, and hints at multi-protein systems as the next challenge for optimization

Rapid screening of nanopore candidates in nanoporous single-layer graphene for selective separations using molecular visualization and interatomic potentials

AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration

Revisiting Cation Complexation and Hydrogen Bonding of Single-Chain Polyguluronate Alginate

Structural and analytical identifications of biomolecules by cold ion spectroscopy of non-covalent complexes

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

Thermomechanical properties of honeycomb lattices from internal-coordinates potentials: the case of graphene and hexagonal boron nitride

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Modelling long-range interactions in multiscale simulations of ferromagnetic materials

A Multimodal Dataset for Automatic Edge-AI Cough Detection

Salt enhances the thermostability of enteroviruses by stabilizing capsid protein interfaces

A data-driven interpretation of the stability of organic molecular crystals

Geometric landscapes for material discovery within energy–structure–function maps