SeparationML

Accelerating f-element separation with machine learning

A grand challenge in chemical separation currently stands in the way of the development of abundant, affordable, and reliable energy systems and the secure supply of critical materials.

Our central scientific mission is to accelerate f­ element separation science design using an integrated data-driven autonomous discovery loop that explores the vast space of separation chemistries with minimal human bias, in a way that provides fundamental molecular-level understanding of the separation process. Toward this end, this proposal concentrates on separation of critical rare earth materials from one another and from actinides relevant to nuclear energy generation, with a particular focus on +3 f-elements, specifically Neodymium (Nd), Europium (Eu), Terbium (Tb), Dysprosium (Dy), Holmium (Ho), Yttrium (Y), Americium (Am) and Curium (Cm). We will employ a multidisciplinary approach that combines high-throughput computations and experiments integrated together with modern data-science and super intelligence approaches to advance fundamental understanding and enable energy and critical material security. 

Energy Security

A grand challenge in chemical separation currently stands in the way of the development of abundant, affordable, and reliable forms of energy. Critical 4f rare earths and minor Sf actinides are all +3 f-elements with similar chemistries, but often very different applications. Separating these elements from one another in high purity and in high yield is essential to developing solutions for national energy security, but separation is extremely difficult given their nearly indistinguishable chemical characteristics.

Accelerated Separation Discovery

Our central scientific mission is to accelerate f­ element separation science design using an integrated data-driven autonomous discovery loop that explores the vast space of separation chemistries with minimal human bias, in a way that provides fundamental molecular-level understanding of the separation process. Toward this end, this proposal concentrates on separation of critical rare earth materials from one another and from actinides relevant to nuclear energy generation, with a particular focus on +3 f-elements, specifically Nd, Eu, Tb, Dy, Ho, Y, Am, and Cm. We will employ a multidisciplinary approach that combines high-throughput computations and experiments integrated together with modern data-science approaches to shed light on the fundamental understandings to enable a low-carbon future.

Automating Separations: LANL Super Separator

• Makes it easier to develop a new process.
• Makes it easier to optimize an operational process with the confines of an existing safety envelope.
• Automation increases throughput and minimizes human error.
• Commissioned for radioactive actinides.

Design Areas:

Thrust 1: Design and screen highly selective extractants by controlling coordination environment
High-throughput workflow and computation Architector, density functional theory, density functional tight binding theory, pyrion, machine learning model

Thrust 2: Navigate the vast f-element separation landscape with data-driven high throughput robotics
High-throughput robotic experiment, the Super Separator, Bayesian optimization 

Thrust 3: Understand temporal changes in structure and speciation at the molecular level
System identification, Chemical reaction network, molecular dynamics simulations, experimental spectroscopic validation

Partnership & Collaboration

NVIDIA

Logan Ward, Scot Halverson, Yuliana Zamora, Xiaoyun Wang, Geetika Gupta, Paul Cook, Justin Smith