A5039234679- Machine Learning in Materials Science
- X-ray Diffraction in Crystallography
- High-pressure geophysics and materials
- Corneal surgery and disorders
- Sperm and Testicular Function
- Laser Material Processing Techniques
- Energetic Materials and Combustion
- Adhesion, Friction, and Surface Interactions
- Tribology and Lubrication Engineering
- Corneal Surgery and Treatments
- Intraocular Surgery and Lenses
- Ocular Surface and Contact Lens
- Drug Transport and Resistance Mechanisms
- Additive Manufacturing and 3D Printing Technologies
- Manufacturing Process and Optimization
- Nuclear Physics and Applications
- Protein Structure and Dynamics
- Additive Manufacturing Materials and Processes
- Diamond and Carbon-based Materials Research
- Pharmacogenetics and Drug Metabolism
- Computational Drug Discovery Methods
- Drug-Induced Hepatotoxicity and Protection
Peking University
2023
Hunan University
2018-2019
Huainan Normal University
2018
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version offers numerous advanced features, such DeepPot-SE, attention-based hybrid descriptors, ability to fit tensile properties, type embedding, model...
Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties Sn are sensitive to pressures. We develop machine-learning-based deep potentials with pressures ranging from 0 50 GPa and temperatures 2000 K. In particular, we find potential, which obtained by training ab initio data density functional theory calculations state-of-the-art SCAN exchange-correlation functional, suitable characterize Sn. systematically validate several...
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version offers numerous advanced features such DeepPot-SE, attention-based hybrid descriptors, ability to fit tensile properties, type embedding,...
Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties Sn are sensitive to pressures. We develop machine-learning-based deep potentials with pressures ranging from 0 50 GPa and temperatures 2000 K. In particular, we find potential, which obtained by training ab initio data density functional theory calculations state-of-the-art SCAN exchange-correlation functional, suitable characterize Sn. systematically validate several...
Objective To investigate the therapeutic effect of penetrating keratoplasty (PKP) for secondary keratoconus after Laser in situ keratomileusis (LASIK). Methods Six eyes LASIK undergoing PKP were analyzed with 7.5mm transplant which had same diameter that positively matching recipient bed. The been monitored and assessed follow-up 1 to 5 years. Results Post-operatively, six patients an average 504.7+8.3μ m central cornea thickness.The visual acuity improved obviously there was no recurrence...
A 70-year-old male patient received pantoprazole because of stomachache with bloody stools for 1 day and positive fecal occult blood test. The laboratory tests before medication showed total bilirubin(TBil) 11.3 μmol/L, direct bilirubin(DBil) 4.0 alkaline phosphatase(ALP) 72 U/L, alanine aminotransferase(ALT) 59 aspartate aminotransferase(AST) 55 glutamyltransferase(γ-GT ) 65 U/L. On 4 after medication, the was diagnosed as ulcerative colitis. Pantoprazole stopped cimetidine, mesalazin...