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 forms of clean energy.

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 artificial intelligence approaches to shed light on the fundamental understandings to enable a low-carbon future. 

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

Personnel

Senior PIs

Ping Yang

Ping Yang is the Deputy Director of the G.T. Seaborg Institute for Transactinium Science and a Staff Scientist in the Physics and Chemistry of Materials Group of the Theoretical Division at Los Alamos National Laboratory. She has extensive experience in computational approaches to modeling electronic structure and reactivity of actinides, separation, surface chemistry, and nanomaterials in solution environments. Her research focuses on understanding fundamental electronic structures, spectroscopic properties, reactivity, and dynamical behaviors of f-element chemistry and materials that are crucial for energy security.  She has broad interests in both applications of current high-performance computing frameworks and development of new computational methods for long time-scale simulations of complex f-element systems. 

Stosh Kozimor

Dr. Kozimor is a staff scientist in the Inorganic, Isotope, and Actinide Chemistry group (C-IIAC), where he is heavily involved in the isotope production team.  He focus on the study of the unique properties and problems associated with radioactive elements, including synthetic expertise and spectroscopic expertise for f-elements. He leads a laboratory-based team in conducting X-ray spectroscopy measurements on radioactive samples at synchrotron facilities since 2006.In addition, Stosh serves as the Los Alamos National Laboratory BES HEC project PI and maintains a fully functional transuranic laboratory (the "Alpha Wing"). Stosh has a wide background in inorganic synthesis, spectroscopy, and radiochemistry with his work at the Lab branching out into studies of actinides (in particular transuranics). Of particular relevance, Stosh spent four years working on the C-NR radiochemistry, where his technical focus was center on nuclear forensics. He was additional the P.I. for the DTRA supported Nuclear Forensic Undergraduate Summer School.

Joshua Schrier

Joshua Schrier is a physical chemist interested in using computers to accelerate the discovery of new materials, by using a combination of physics-based simulations, cheminformatics, machine learning, and automated experimentation. He is the Kim B. and Stephen E. Bepler Professor of Chemistry at Fordham University in New York City.  Prior to joining Fordham in 2018, he was on the faculty at Haverford College, and a Luis W. Alvarez computational sciences postdoctoral fellow at Lawrence Berkeley National Laboratory. As a faculty member, he has received awards include the Dreyfus Teacher-Scholar, U.S. Department of Energy Visiting Faculty, and Fulbright scholar awards.

Sara Adelman

Adelman's diverse educational background, including a PhD in Chemistry from Michigan State University under Professor Jim McCusker, and her professional experiences have led her to a career at Los Alamos National Laboratory. There, she focuses on complex scientific questions vital for national security. During her time at MSU, she worked on developing earth-abundant dyes for solar cells and analyzed their electronic and physical properties. As a post-doc at the Laboratory, under Drs. Stosh Kozimor, Veronika Mocko, and Benjamin Stein, she developed a robotic system for automated chemical processes in a radiological lab. Now, as a scientist, she enhances automated systems for pharmaceutical isotope production and nuclear energy applications, collaborating with data scientists to improve separations and train machine learning algorithms. 

Enrique R. Batista

Enrique R. Batista, received a B.S. in physics, with honors from the University of Buenos Aires, Argentina, M.S. and Ph.D. in physics from the U. of Washington. Post Ph.D. he worked as a postdoctoral fellow at Columbia University, in the Environmental Molecular Sciences Institute, funded by the NSF.  He joined Los Alamos National Laboratory in 2003 as a post-doctoral fellow at the Theoretical Chemistry and Molecular Physics group and a staff scientist in 2005.  Since 2016 Batista is Deputy Center Director of the Center for Nonlinear Studies (CNLS).  His research areas of interest in the computational study of actinide chemistry in gas phase and in solution, spectroscopy, coordination, and reactivity, and on electronic structure studies of transition metal complexes with application to homogeneous catalytic processes.  His studies also extend to solid-state systems and surface chemistry problems. Batista works with, and towards the development of, efficient computational methodologies for atomistic simulations and electronic structure approaches. 

Nicholas Lubbers

Nicholas Lubbers is a computation scientist with Los Alamos National Laboratory’s Information Sciences group. His background is in statistical physics, with a Ph.D. from Boston University under Professor William Klein. He had a postdoctoral appointment with the Center for Nonlinear Studies and Theoretical Division at the Lab, and in 2018 the Information Sciences group in the Computer, Computational, and Statistical Sciences division as a staff scientist. He is an organizer and mentor for the Laboratory’s Applied Machine Learning Summer Program, which he lead in the years of 2021-2023.

His research focus is on Machine Learning for Science, with diverse applications to Materials Science, Seismology, Fluid Mechanics, Microbiome analysis, Nanoscale phenomena, and Computational Chemistry. He has particular interest in physics-informed algorithm design, uncertainty quantification and active learning, and meta-algorithms. His mission is to contribute to science and technology through innovation, mentoring, and open-source software development. 

Danny Perez

Danny is a Scientist IV at Los Alamos National Laboratory. He obtained a Ph.D. in Physics from Université de Montréal in 2006. He then joined the Laboratory as a Director’s postdoctoral fellow in 2007. His research centers around the development of long-time atomistic simulation methods that are designed to explore systems that evolve through sequences of rare events. He then implements these methods in high performance simulation codes, and applied them to a range of problems of relevance to energy applications, such as nuclear fusion or fission. Over the last few years, Danny has been especially interested in developing strategies for the optimal use of exascale computing. He is currently the PI of the EXAALT project funded by DOE’s Exascale Computing Project where his team develops novel ultra-scalable approaches for materials simulations, including long-timescale molecular dynamics, optimization for GPUs, and high-throughput large-scale workflows. 

Yulia Pimonova

Yulia Pimonova is a Postdoctoral Research Associate in the Computing, and Artificial Intelligence (CAI) Division at Los Alamos National Laboratory. In the SeparationML team she develops machine learning approaches to learn from small, experimental datasets to predict high-value separations metrics. She also applies graphlet decomposition analysis to uncover structural and binding motifs across ligand families and metal chemistries, using these insights to propose new ligand designs. Her work sits at the intersection of molecular machine learning, cheminformatics, and molecular simulation, with a focus on uncertainty-aware modeling and data-efficient discovery. 

Yulia earned her Ph.D. in Physical Chemistry from the University of Utah in May 2024. As a graduate researcher with Dr. Michael Gruenwald, she performed large-scale empirical molecular dynamics simulations of coarse-grained cocrystallization and contributed to advancing cocrystallization theory. Her work has been recognized with multiple honors, including poster prizes at the Berkeley Statistical Mechanics Meeting and OpenEye’s annual scientific meeting (both 2023). She is also an active mentor in LANL’s Applied Machine Learning Summer Program. 

Pascal Grosset

Pascal Grosset is a scientist in the Data Science team at Los Alamos National Laboratory. Originally from Mauritius, he earned his PhD in Computing (Graphics and Visualization) from the University of Utah as a recipient of the International Fulbright Science and Technology Award, and joined Los Alamos as a postdoctoral researcher in 2016. His work centers on developing scalable workflows for large-scale scientific data in close collaboration with domain scientists, enabling efficient analysis of complex simulation outputs. His research spans the application of AI to scientific workflows, as well as the design of algorithms and tools for the analysis, compression, and visualization of massive datasets generated by high-performance computing systems. He has contributed to major national initiatives, including the DOE Exascale Computing Project, and currently works on the DOE Genesis project.

Early Career

Zafer Acar

Zafer Acar is a Ph.D. candidate at Michigan Technological University, holding a Bachelor of Science degree in Astrophysics and a Master's degree in Physics. Zafer has served as a software engineer and database administrator for financial institutions in Los Angeles. He is an enthusiastic advocate of big data, driven by a profound interest in Data Science, Machine Learning, and Artificial Intelligence. His insatiable curiosity and dedication have led him to conduct research in prestigious institutions such as the European Nuclear Research Center, Princeton University, UC Berkeley, the United States Navy, and the Los Alamos National Laboratory. His research interests encompass Machine Learning, Natural Language Processing, Computer Vision, Computational and Nuclear Chemistry, and Solvent Separation.

Logan Augustine

Logan Augustine is a Seaborg Postdoctoral Research Fellow in the Physics and Chemistry of Materials (T-1) group at Los Alamos National Laboratory. Within the SeparationML team, Logan has developed Bayesian Optimization techniques to accelerate the discovery of experimental parameters which maximize separations performance in relation to the f-elements. Along with this, he has conducted computational studies through high-throughput simulation for ligand discovery and the understanding of separation mechanisms. Logan first joined LANL in 2022 as a graduate researcher through a Department of Energy SCGSR fellowship before transitioning to a postdoctoral role in 2023. Before LANL, Logan earned his Ph.D. in chemistry from the University of Iowa, where he was advised by Dr. Sara E. Mason and Dr. Tori Z. Forbes. His doctoral research focused on density functional theory studies of contaminant adsorption at mineral–water interfaces, as well as the vibrational spectroscopy of the neptunyl ion engaged in intermolecular interactions. He received his B.S. in chemistry from Lincoln Memorial University in Tennessee in 2018.

Alexander Brown

I was first introduced and motivated to pursue science by a collection of 20th century science fiction writers. While I eventually concluded that faster-than light travel wasn’t possible yet and mathematical psychoanalysis couldn’t predict the future, I found a place at the interface of physical inorganic chemistry, materials science, and biology where challenges related to human health and sustainability can be addressed from fundamental physical principles. While these areas appear disparate, they are driven by a common thread – by uncovering molecular and electronic structure properties through synthesis, physical characterization, and theory, rationally design materials can be constructed. After earning a B.Sc. in biochemistry from Bridgewater State University I began my doctoral studies at Brown University working under Prof. Jerome Robinson. Here, my interest in developing structure-function relationships led to several projects in the fields of radiopharmaceutical development, oxygen reduction catalysis and ring opening polymerization reactions where an atomistic understanding of the chemical system is critical for achieving desirable results. These studies have branched into my pursuits at LANL where I am working as a DOE SCGSR fellow utilizing a combination of high-throughput simulation and synthetic modeling to evaluate the impact of chelate binding on the electronic structure and redox potentials of actinide elements for application to separations technologies. 

Yangxin Fan

I am Yangxin Fan, a Ph.D. candidate in Computer Science at Case Western Reserve University. My research focuses on graph neural networks, large language models, scalable machine learning, and spatio-temporal data analytics, with an emphasis on AI for Science. My work develops machine learning methods for real-world applications in materials science, molecular learning, chemistry, and finance.

Jiyoung Lee

I was born and grew up in Ulsan, South Korea before moving to Seoul for my undergraduate. I studied chemistry at Ewha Womans University where I found my strong interest in physical chemistry. After I finished my Bachelor’s, I came to US and did Master’s in computational chemistry at Iowa State University. I was part of a project to improve interoperability of quantum chemistry packages and it led me to pursue interdisciplinary research where I am currently being trained at University of Texas at Austin. My research interest at UT was to apply machine learning technique to accelerate searching local minima and saddle points.  

Garrett Santis

Garrett is a new Postdoctoral Research Associate in the T-1 group at Los Alamos National Laboratory.  For the Separation ML team, Garrrett is improving algorithms to generate 3-D structures of metal-ligand complexes stemming from previously investigated systems.  Concurrently, Garrett is investigating the solubility of meal-ligand complexes, extending current machine learning models to predict the solubility of large coordinate systems.  Before coming to LANL in October 2025, Garrett worked as a computational scientist (project hire) at the Pacific Northwest National Laboratory.  Garrett earned his Ph.D. in chemistry from the University of Washington under the mentorship of Dr. Sotiris Xantheas.  His thesis explored the hydrogen bond, ranging from applications in biological systems to the theoretical development and explanation of the energetic-spectral-structural relationship.  Garrett also received his B.S. in Chemistry from Lebanon Valley College in 2020.

Thomas Summers

A native of Tennessee, Thomas Summers received his B.S. in biochemistry from Christian Brothers University in Memphis. He continued on to receive his M.S. and Ph.D. in chemistry from the University of Memphis, his research focusing on modeling enzyme-catalyzed reactions and protein inter-residue interaction networks. He then joined the University of Nevada, Reno as a postdoctoral researcher for two years, where he worked on characterizing the solution structures of lanthanide-ligand complexes through molecular simulation and extended X-ray absorption fine structure spectroscopy. He recently joined the Theoretical Division at Los Alamos National Laboratory as a postdoctoral researcher where he continues to explore the coordination structures formed by f-elements using high-throughput computational modeling. 

Alyssa Sun

Alyssa Sun is a graduating senior from Los Alamos High School. She has interests in artificial intelligence and its use in diagnostic healthcare, inspired by the diagnosis of a friend, her mother, and herself. For her science fair project, she was a 2025 Regeneron International Science and Engineering Fair Finalist in biomedical engineering (the project even included a reading of her own pneumonia chest X-ray!). In her free time, she runs sticker shops on Etsy, selling them online and making personal gifts for friends and family. She plans on studying applied mathematics in college.

Michael Taylor

Michael Taylor is a chemical engineer interested in physics-based simulations, cheminformatics, and machine learning to discover new materials and chemistries.   He is a staff scientist in the Theoretical Division at Los Alamos National Laboratory.  Prior to a recently completed postdoc at the Laboratory he was a postdoc at MIT. Prior to MIT he completed his PhD in chemical engineering at the University of Pittsburgh as an NSF graduate research fellow (GRFP).   

Mike Tynes

Mike joined Separation ML as a Post-Masers fellow in Los Alamos National Laboratory’s T-1, after completing a masters degree in data science from Fordham University. As part of Separation ML Mike has developed algorithms and tools for machine learning in molecular property prediction which improve model accuracy, interpretability, and provide uncertainty quantification. These methods are available in our open source package minervachem. Mike also contributed to the Lab’s cinema scientific visualization tool improving support for categorical variables and for organic/inorganic chemical data. Mike is now a PhD student at the University of Chicago Department of Computer Science, advised by Dr. Ian Foster.

Yufei Wang

I am a postdoc working on the application of an automated system in high-throughput separations. Before I joined Los Alamos National Laboratory, I was a Ph.D. student at UC Berkeley where I performed research on the separations of lanthanides and actinides by liquid-liquid extraction. My interests include badminton, swimming, and skiing. 

Alumni

Brian Arko

Dr. Daniel Burrill

Dr. Jan Janssen

Rebecca Li 

Partnership & Collaboration

NVIDIA

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