CAREER: Learning to learn - Artificial Intelligence Augmented Chemistry for Molecular Simulations and Beyond

马里兰大学帕克分校的Pratyush Tiwary博士得到了化学部化学理论、模型和计算方法项目的支持，以开发统计力学和人工智能(AI)界面上的模拟算法，用于研究罕见事件。统计力学和人工智能的协同使用使化学和生物化学中非常缓慢的过程能够自动、无人类偏见地建模，这些过程在许多时间和长度尺度上展开。Tiwary博士和他的团队正在开发的工具将被整合到高效的开源计算平台中，供科学界广泛使用。他正在追求的众多应用之一是量化生物宿主内小分子所花费的时间，这是生命过程化学的基本性质，但很难通过实验或模拟计算。Tiwary的建模结果将与他在石溪大学和国家癌症研究所的合作伙伴的实验调查进行比较。根据这一奖项，Tiwary博士还将开发平台，通过研讨会和在线教程向高中生/大学生和物理学教育工作者介绍编码和人工智能，为下一代劳动力提供适用于当今就业市场的可转移技能。这些努力是通过与乔治王子社区学院、鲍伊州立大学的合作以及与全国各地的其他合作伙伴的虚拟手段来进行的。Pratyush Tiwary的研究旨在开发下一代超长时间尺度分子动力学(MD)模拟方法，方法是将人工智能与统计力学相结合，通过“学习”框架。这个框架使用人工智能来学习表征通用分子系统的反应坐标(RC)，将其解释为过去-未来的信息瓶颈。通过有偏采样方法利用RC的知识来系统地采样更多的配置空间，从而生成更多的相关数据来训练人工智能。此外，统计力学的使用以不同的方式帮助人工智能，通过(I)使“黑匣子”人工智能技术更加透明，以及(Ii)处理人工智能可能产生误导性结果的不良训练数据的问题。人工智能和MD之间的迭代一直持续到RC收敛，导致在一次拍摄中估计热力学常数、速率和速率限制步骤。这些方法将被应用于蛋白质-配体和核糖开关-配体相互作用的研究，这两种方法都是生命过程的核心，但人们对此知之甚少。这些长时间尺度的全原子模拟方法旨在揭开这些和其他一般分子系统中不同自由度之间相互作用所产生的复杂性和丰富性。最后，这项研究计划还努力展示统计力学和人工智能的结合如何导致对人工智能的可解释和可信的使用，从而增加对人工智能在化学领域大规模部署的信心。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Dr. Pratyush Tiwary of University of Maryland, College Park, is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop simulation algorithms at the interface of statistical mechanics and artificial intelligence (AI) for the study of rare events. The synergistic use of statistical mechanics and AI enables the automatic, human bias-free modeling of very slow processes in chemistry and biochemistry that unfold across many time and length scales. The tools Dr. Tiwary and his team are developing are to be incorporated into efficient open-source computational platforms for widespread use by the scientific community. One of many applications he is pursuing is the quantification of time spent by small molecules inside biological hosts, a property fundamental to the chemistry of life processes, yet very hard to calculate through experiments or simulations. The results of Tiwary’s modeling will be compared against experimental investigations by his partners at Stony Brook University and the National Cancer Institute. Under this award, Dr. Tiwary will also be developing platforms to introduce coding and AI to high school/college students and educators in physical sciences through workshops and online tutorials, providing the workforce of the next generation with transferable skills for today's job markets. These efforts are being carried out through collaborations with Prince George’s Community College, Bowie State University and through virtual means with other partners across the country.Pratyush Tiwary’s research seeks to develop the next generation of ultra-long timescale molecular dynamics (MD) simulation methods by integrating AI with statistical mechanics through a “learning to learn” framework. This framework uses AI to learn the reaction coordinate (RC) characterizing a generic molecular system, interpreting it as a past-future information bottleneck. The knowledge of the RC is used through biased sampling methods to systematically sample more of the configuration space and thereby generate more relevant data to train AI. Furthermore, the use of statistical mechanics helps AI in different ways, by (i) making “black box” AI techniques more transparent, and (ii) dealing with the problem of poor training data from which AI can produce misleading results. The iteration between AI and MD continues till the RC converges, leading to estimates of thermodynamic constants, rates and rate-limiting steps in one shot. These methods will be applied to the study of protein-ligand and riboswitch-ligand interactions, both of which are central to life processes, yet poorly understood. These long timescale all-atom simulation methods are designed to unravel the complexity and richness that arises from the interplay between different degrees of freedom in these and other generic molecular systems. Finally, this research program also strives to demonstrate how mixing statistical mechanics and AI can lead to interpretable and trustworthy use of AI, thereby increasing confidence in large-scale deployments of AI across chemistry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.