Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytes. [PDF]
Nat CommunLi-ion batteries, widely used in electronic devices, electric vehicles, and aviation, demand high energy density, fast charging capabilities, and broad operating temperature ranges.
Wang F, Tang YH, Ma ZB, Jin YC, Cheng J.
europepmc +2 more sources
An automated framework for exploring and learning potential-energy surfaces. [PDF]
Nat CommunMachine learning has become ubiquitous in materials modelling and now routinely enables large-scale atomistic simulations with quantum-mechanical accuracy.
Liu Y +8 more
europepmc +3 more sources
Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential. [PDF]
ACS Cent Sci, 2023 Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans–cis isomerism in response to light.
Axelrod S +2 more
europepmc +3 more sources
Choosing the right molecular machine learning potential. [PDF]
Chem Sci, 2021 Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra.
Pinheiro M +4 more
europepmc +2 more sources
Exploring the frontiers of condensed-phase chemistry with a general reactive machine learning potential. [PDF]
Nat ChemAtomistic simulation has a broad range of applications from drug design to materials discovery. Machine learning interatomic potentials (MLIPs) have become an efficient alternative to computationally expensive ab initio simulations.
Zhang S +10 more
europepmc +2 more sources
Effect of Interlayer Bonding on Superlubric Sliding of Graphene Contacts: A Machine-Learning Potential Study. [PDF]
ACS NanoSurface defects and their mutual interactions are anticipated to affect the superlubric sliding of incommensurate layered material interfaces. Atomistic understanding of this phenomenon is limited due to the high computational cost of ab initio ...
Ying P, Natan A, Hod O, Urbakh M.
europepmc +2 more sources
The Influence of Computerized Dynamic Assessment on the Learning Potential of Graphical Analogical Reasoning in Children with Autism: Evidence from Eye-Movement Synchronization [PDF]
Behavioral SciencesGraphical analogical reasoning ability is crucial for the cognitive development of children with autism spectrum disorder (ASD). However, there are currently no methods available to enhance its analogical reasoning potential.
Kun Zhang +4 more
doaj +2 more sources
Selecting for Learning Potential: Is Implicit Learning the New Cognitive Ability? [PDF]
Journal of Intelligence, 2022 For decades, the field of workplace selection has been dominated by evidence that cognitive ability is the most important factor in predicting performance. Meta-analyses detailing the contributions of a wide-range of factors to workplace performance show
Luke M. Montuori, Lara Montefiori
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Individual differences in the learning potential of human beings. [PDF]
NPJ Sci Learn, 2017 To the best of our knowledge, the genetic foundations that guide human brain development have not changed fundamentally during the past 50,000 years. However, because of their cognitive potential, humans have changed the world tremendously in the past ...
Stern E.
europepmc +2 more sources
Pretraining of attention-based deep learning potential model for molecular simulation [PDF]
npj Computational Materials, 2022 Machine learning-assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation. With the accumulation of high-quality electronic structure data, a model that can be pretrained on all available ...
Duoduo Zhang +6 more
semanticscholar +1 more source