首页|Brigham Young University Reports Findings in Machine Learning (TrIP Transformer Interatomic Potential Predicts Realistic Energy Surface Using Physical Bias)

Brigham Young University Reports Findings in Machine Learning (TrIP Transformer Interatomic Potential Predicts Realistic Energy Surface Using Physical Bias)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – New research on Machine Learning is the subject of a report. According to news reportingoriginating in Provo, Utah, by NewsRx journalists, research stated, “Accurate interatomic energies andforces enable high-quality molecular dynamics simulations, torsion scans, potential energy surface mappings,and geometry optimizations. Machine learning algorithms have enabled rapid estimates of the energies andforces with high accuracy.”

ProvoUtahUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningMolecular DynamicsPhysics

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jan.5)