相关论文: Machine Learning Relationships between Nanoporous …
Self-supervised learning (SSL) plays a central role in molecular representation learning. Yet, many recent innovations in masking-based pretraining are introduced as heuristics and lack principled evaluation, obscuring which design choices…
The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we…
The realization of novel heterostructures arising from the combination of nanomaterials is an effective way to modify their physicochemical and electrocatalytic properties, giving them enhanced characteristics stemming from their individual…
Pulsed laser-deposited amorphous carbon nanofoams are potential candidate for electrochemical energy storage applications due to ultra-light weight, large volumetric void fractions, and co-existence of sp, sp2 and sp3 carbon hybridization.…
Metal-organic frameworks (MOFs) are promising materials for methane capture due to their high surface area and tunable properties. Metal substitution represents a powerful strategy to enhance MOF performance, yet systematic exploration of…
As renewable energy is becoming a critical energy source to meet the global demand, electrochemical energy storage devices become indispensable for the efficient energy storage and reliable supply. The electrode material is the key factor…
We present a machine learning (ML) method for predicting electronic structure correlation energies using Hartree-Fock input.The total correlation energy is expressed in terms of individual and pair contributions from occupied molecular…
We propose a computationally lean, two-stage approach that reliably predicts self-assembly behavior of complex charged molecules on a metallic surfaces under electrochemical conditions. Stage one uses ab initio simulations to provide…
Transition metal chromophores with earth-abundant transition metals are an important design target for their applications in lighting and non-toxic bioimaging, but their design is challenged by the scarcity of complexes that simultaneously…
Two recent experimental (Li, J.~\emphj{et al}, \emph{Proc.\ Natl.\ Acad.\ Sci.\ U.~S.~A.} {\bf 2014}, 111, 1282-1287) and theoretical studies (B\^aldea, I, \emph{Phys.\ Chem.\ Chem.\ Phys.}\ {\bf 2014}, 16, 25942-25949) have addressed the…
Organic electrode-active materials (OAMs) not only enable a variety of charge and storage mechanisms, but are also safer for the environment and of lower cost compared to materials in commonly used lithium-ion batteries. Cross-linked…
Reliable molecular property prediction is essential for various scientific endeavors and industrial applications, such as drug discovery. However, the data scarcity, combined with the highly non-linear causal relationships between…
The potential to utilize metal-organic frameworks as a replacement for rare earth materials as well as in technological applications has prompted increased interested in this material class. The simulation of organic materials, including…
Chemical functionalization of nanocarbons is an important strategy to produce electrochemical systems with higher energy/power density by generating surface functional groups with additional faradaic contribution, by increasing their…
Unlike molecular crystals, soft self-assembled fibres, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers, but also high structural dynamicity and variability. Defects and disordered local…
Molten salts are crucial for clean energy applications, yet exploring their thermophysical properties across diverse chemical space remains challenging. We present the development of a machine learning interatomic potential (MLIP) called…
The recent geopolitical crisis resulted in a gas price surge. Although lithium-ion batteries represent the best available rechargeable battery technology, a significant energy and power density gap exists between LIBs and petrol/gasoline.…
Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsorbates, and electrolyte. Predicting stable surface structures…
We address the degree to which machine learning can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and selection are presented, and the molecular-orbital-based…
This study investigates the use of machine learning (ML) to correct the enthalpy of formation (Hf) from two separate DFT functionals, PBE and SCAN, to the experimental Hf across 1011 solid-state compounds. The ML model uses a set of 25…