Distributed Algorithms for AI Accelerated Materials Discovery

AI 加速材料发现的分布式算法

• 批准号: 2906112
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2906112
• 关键词: Distributed; Algorithms; AI; Accelerated; Materials

In the rapidly evolving field of chemical discovery, the integration of high-throughput experimental data with computational simulation results offers a promising avenue to accelerate the identification of novel compounds and understand their properties. This research introduces a novel artificial intelligence framework leveraging multi-Fidelity Bayesian machine learning models, including Gaussian Processes (GPs) and Bayesian Neural Networks (BNNs), to harmonize insights drawn from disparate data sources-laboratory experiments and computer-driven simulations. By employing Multi-Output Bayesian methods, our approach uniquely captures the correlations between different physical and chemical outcomes, as well as the inherent biases in datasets of varying fidelity. The framework's utility is demonstrated through its application in predicting the properties of new chemical entities, thereby informing experimental design and decision-making processes in drug discovery and materials science. Our findings indicate a substantial enhancement in prediction accuracy and efficiency, underscoring the potential of multi-fidelity models to revolutionize chemical discovery by enabling a more effective fusion of experimental and simulated data. This work not only contributes to the methodological advancements in the application of AI in chemistry but also sets a new benchmark for future research aiming to optimize the synergy between computational simulations and empirical experiments for accelerated discovery

在快速发展的化学发现领域，高通量实验数据与计算模拟结果的整合提供了一个有前途的途径，以加速新化合物的鉴定和了解其性质。这项研究引入了一种新的人工智能框架，利用多保真度贝叶斯机器学习模型，包括高斯过程（GP）和贝叶斯神经网络（BNN），以协调从不同的数据源-实验室实验和计算机驱动的模拟中得出的见解。通过采用多输出贝叶斯方法，我们的方法独特地捕获了不同物理和化学结果之间的相关性，以及不同保真度数据集的固有偏差。该框架的实用性通过其在预测新化学实体的性质中的应用得到证明，从而为药物发现和材料科学中的实验设计和决策过程提供信息。我们的研究结果表明，预测准确性和效率大大提高，强调了多保真度模型通过更有效地融合实验和模拟数据来彻底改变化学发现的潜力。这项工作不仅有助于人工智能在化学中应用的方法学进步，而且还为未来的研究设定了新的基准，旨在优化计算模拟和经验实验之间的协同作用，以加速发现