摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx记者在Florida Gainesville的新闻报道，研究表明，“最近几年，发表的机器学习原子间势(MLIPs)的数量显著增加。这些新的数据驱动势能近似往往缺乏基于PHYSI CS的基础，这些基础为许多传统开发的原子间势能提供信息，因此需要可靠的验证方法来验证它们的准确性、计算效率和对预期应用的适用性。”新闻记者从Flo Rida大学的研究中引用了一句话，“这项工作提出了MLIP验证的顺序、三阶段工作流程：(i)初步验证，(ii)静态性能预测，和(iii)动态性能预测。该材料不可知程序用于开发碳化硼(B4C)的稳健MLIP，这是一种广泛使用的方法。结构复杂的陶瓷，在高压载荷下经历一种称为“非晶化”的有害变形机制。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Gainesville, F lorida, by NewsRx journalists, research stated, “The number of published Machine Learning Interatomic Potentials (MLIPs) has increased significantly in recent y ears. These new data-driven potential energy approximations often lack the physi cs-based foundations that inform many traditionally-developed interatomic potent ials and hence require robust validation methods for their accuracy, computation al efficiency, and applicability to the intended applications.” The news reporters obtained a quote from the research from the University of Flo rida, “This work presents a sequential, three-stage workflow for MLIP validation : (i) preliminary validation, (ii) static property prediction, and (iii) dynamic property prediction. This material-agnostic procedure is demonstrated in a tuto rial approach for the development of a robust MLIP for boron carbide (B4C), a wi dely employed, structurally complex ceramic that undergoes a deleterious deforma tion mechanism called ‘amorphization’ under high-pressure loading.”
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