Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Study on the Thermodynamic Characteristics of Methane Adsorption-Desorption in Coal under Infrared Radiation Heating. [PDF]
Wang Y, Zhang Y, Yang X.
europepmc +1 more source
The hydration behavior of C3S in seawater‐relevant solutions is studied based on experiments, boundary nucleation and growth (BNG) modeling, and machine learning. The main ions included in seawater modify hydration mechanisms, with MgCl2 showing the strongest acceleration effect at the same concentration.
Yanjie Sun +6 more
wiley +1 more source
Molecular Interactions of Pyridine With Water: A Combined DFT Benchmarking and QTAIM Analysis. [PDF]
Baikété J +3 more
europepmc +1 more source
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
Pd-Catalyzed Hydroalkoxycarbonylation and Hydroxycarbonylation: DFT Mapping of Catalyst-Substrate Landscape for Product Selectivity. [PDF]
Gupta S, Gupta P.
europepmc +1 more source
Recent advances in TiO2 modification strategies: structure regulation and composite engineering for improved activity and functionality. TiO2 has emerged as a pivotal catalyst for enhancing MgH2, a high‐capacity solid‐state hydrogen storage material, owing to its structural versatility.
Xiaopeng Chu +10 more
wiley +1 more source
Thermodynamic Activation Parameters for Chemical Reactions in Enzymes and Solution from Computer Simulations at a Single Temperature. [PDF]
van der Ent F, Demkiv AO, Åqvist J.
europepmc +1 more source
Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation
Predicting a crystal’s structure from composition alone is a long‐standing challenge in materials discovery. The CSP180 benchmark of 180 inorganic crystals evaluates thirteen crystal structure prediction algorithms requiring no density functional theory (DFT) against DFT‐based baselines across twelve metrics.
Lai Wei +9 more
wiley +1 more source
Molecular Modeling of Triethyl Phosphate: Extension and Validation of the TraPPE Force Field. [PDF]
Jomon G +3 more
europepmc +1 more source

