James Saal

Dr. James Saal specializes in the design of new materials and computational materials simulation, with 20 years of experience in materials informatics, CALPHAD- and ICME-based alloy design, materials property database construction, and software infrastructure development. As Senior Director-External Research Programs at Citrine Informatics, James manages a team working on government-funded programs at the cutting edge of materials informatics with academic, laboratory, and industrial collaborators. Before joining Citrine in 2018, James was Manager of Technology at QuesTek Innovations, leading programs in computational materials design. He earned his Ph.D. in Material Science and Engineering from The Pennsylvania State University, where he focused on computational materials thermodynamics. He is the author of over 70 peer-review publications and has provided invited talks at several domestic and international conferences and workshops.  

Education History

• B.S., Materials Science and Engineering, Rice University
• M.S., Materials Science and Engineering, The Pennsylvania State University
• Ph.D., Materials Science and Engineering, The Pennsylvania State University

Work Experience

Senior Director-External Research Programs, Citrine Informatics, Inc.

Publications

1. Stuckner, J., Taheri-Mousavi, S. M. & Saal, J. E. Artificial Intelligence and Machine Learning in Materials Science. AM&P Technical Articles 182, 14–20 (2024).
2. Hegde, V. I. et al. Towards informatics-driven design of nuclear waste forms. Digital Discovery 3, 1450–1466 (2024).
3. Bauer, S. et al. Roadmap on data-centric materials science. Modelling Simul. Mater. Sci. Eng. 32, 063301 (2024).
4. Allec, S. I. et al. A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community. Integr Mater Manuf Innov 13, 406–419 (2024).
5. Allec, S. I. et al. Evaluation of GlassNet for physics‐informed machine learning of glass stability and glass‐forming ability. J Am Ceram Soc. 107, 7784–7799 (2024).
6. Muckley, E. S., Saal, J. E., Meredig, B., Roper, C. S. & Martin, J. H. Interpretable models for extrapolation in scientific machine learning. Digital Discovery 2, 1425–1435 (2023).
7. Hegde, V. I. et al. Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD. Phys. Rev. Materials 7, 053805 (2023).
8. Gerard, A. Y. et al. The role of chromium content in aqueous passivation of a non-equiatomic Ni38Fe20CrxMn21-0.5xCo21-0.5x multi-principal element alloy (x = 22, 14, 10, 6 at%) in acidic chloride solution. Acta Materialia 245, 118607 (2023).
9. Borg, C. K. H. et al. Quantifying the performance of machine learning models in materials discovery. Digital Discovery 2, 327–338 (2023).
10. Shen, J. et al. Reflections on One Million Compounds in the Open Quantum Materials Database (OQMD). Journal of Physics: Materials 0–13 (2022) doi:10.1088/2515-7639/ac7ba9.
11. Lee, A. et al. Machine learned synthesizability predictions aided by density functional theory. Communications Materials 3, 73 (2022).
12. Han, J. et al. Elementally Resolved Dissolution Kinetics of a Ni-Fe-Cr-Mn-Co Multi-Principal Element Alloy in Sulfuric Acid Using AESEC-EIS. J. Electrochem. Soc. 169, 081507 (2022).
13. Gurunathan, R. et al. Mapping Thermoelectric Transport in a Multicomponent Alloy Space. Adv Elect Materials 8, 2200327 (2022).
14. Annevelink, E. et al. AutoMat: Automated materials discovery for electrochemical systems. MRS Bulletin 47, 1036–1044 (2022).
15. Nyby, C. et al. Electrochemical metrics for corrosion resistant alloys. Scientific Data 8, 58 (2021).
16. Melia, H. R., Muckley, E. S. & Saal, J. E. Materials informatics and sustainability—The case for urgency. Data-Centric Engineering 2, e19 (2021).
17. Kautz, E. J. et al. Element redistributions during early stages of oxidation in a Ni38Cr22Fe20Mn10Co10 multi-principal element alloy. Scripta Materialia 194, 113609 (2021).
18. Han, J. et al. Potential Dependent Mn Oxidation and Its Role in Passivation of Ni 38 Fe 20 Cr 22 Mn 10 Co 10 Multi-Principal Element Alloy Using Multi-Element Resolved Atomic Emission Spectroelectrochemistry. Journal of The Electrochemical Society 168, 051508 (2021).
19. Scully, J. R. et al. Controlling the corrosion resistance of multi-principal element alloys. Scripta Materialia 188, 96–101 (2020).
20. Saal, J. E., Oliynyk, A. O. & Meredig, B. Machine Learning in Materials Discovery: Confirmed Predictions and Their Underlying Approaches. Annual Review of Materials Research 50, 49–69 (2020).
21. Li, X. et al. Communication—Dissolution and Passivation of a Ni-Cr-Fe-Ru-Mo-W High Entropy Alloy by Elementally Resolved Electrochemistry. Journal of The Electrochemical Society 167, 061505 (2020).
22. Gerard, A. Y. et al. Aqueous Passivation of Multi-Principal Element Alloy Ni38Fe20Cr22Mn10Co10: Unexpected High Cr Enrichment within the Passive Film. Acta Materialia (2020) doi:10.1016/j.actamat.2020.07.024.
23. Borg, C. K. H. et al. Expanded dataset of mechanical properties and observed phases of multi-principal element alloys. Scientific Data 7, 430 (2020).
24. Antono, E. et al. Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials. The Journal of Physical Chemistry A 124, 8330–8340 (2020).
25. Quiambao, K. F. et al. Passivation of a corrosion resistant high entropy alloy in non-oxidizing sulfate solutions. Acta Materialia 164, 362–376 (2019).
26. Peters, M. C., Doak, J. W., Saal, J. E., Olson, G. B. & Voorhees, P. W. Using First-Principles Calculations in CALPHAD Models to Determine Carrier Concentration of the Binary PbSe Semiconductor. Journal of Electronic Materials 48, 1031–1043 (2019).
27. Lu, P. et al. Computational design and initial corrosion assessment of a series of non-equimolar high entropy alloys. Scripta Materialia 172, 12–16 (2019).
28. Li, T. et al. Localized corrosion behavior of a single-phase non-equimolar high entropy alloy. Electrochimica Acta 306, 71–84 (2019).
29. Ward, L. et al. Strategies for accelerating the adoption of materials informatics. MRS Bulletin 43, 683–689 (2018).
30. Wang, D. et al. Crystal structure, energetics, and phase stability of strengthening precipitates in Mg alloys: A first-principles study. Acta Materialia 158, 65–78 (2018).
31. Taylor, C. D., Lu, P., Saal, J., Frankel, G. S. & Scully, J. R. Integrated computational materials engineering of corrosion resistant alloys. npj Materials Degradation 2, 6 (2018).
32. Computational Materials System Design. (Springer International Publishing, Cham, 2018). doi:10.1007/978-3-319-68280-8.
33. Saal, J. E., Berglund, I. S., Sebastian, J. T., Liaw, P. K. & Olson, G. B. Equilibrium high entropy alloy phase stability from experiments and thermodynamic modeling. Scripta Materialia 146, 5–8 (2018).
34. Lu, P. et al. Computational materials design of a corrosion resistant high entropy alloy for harsh environments. Scripta Materialia 153, 19–22 (2018).
35. Furmanchuk, A. et al. Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach. Journal of Computational Chemistry 39, 191–202 (2018).
36. Peters, M. C. et al. Thermodynamic modeling of the PbX (X=S,Te) phase diagram using a five sub-lattice and two sub-lattice model. Calphad 58, 17–24 (2017).
37. Gong, J. et al. ICME Design of a Castable, Creep-Resistant, Single-Crystal Turbine Alloy. JOM 69, 880–885 (2017).
38. Saal, J. E. & Wolverton, C. Energetics of antiphase boundaries in γ′ Co3(Al,W)-based superalloys. Acta Materialia 103, 57–62 (2016).
39. Kirklin, S., Saal, J. E., Hegde, V. I. & Wolverton, C. High-throughput computational search for strengthening precipitates in alloys. Acta Materialia 102, 125–135 (2016).
40. Emery, A. A., Saal, J. E., Kirklin, S., Hegde, V. I. & Wolverton, C. High-Throughput Computational Screening of Perovskites for Thermochemical Water Splitting Applications. Chemistry of Materials 28, 5621–5634 (2016).
41. Saal, J. E. & Orlov, D. Overview: Age-Hardenable Microalloying in Magnesium. JOM 67, 2425–2426 (2015).
42. Kirklin, S. et al. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies. npj Computational Materials 1, 15010 (2015).
43. Issa, A., Saal, J. E. & Wolverton, C. Formation of high-strength β′ precipitates in Mg–RE alloys: The role of the Mg/β″ interfacial instability. Acta Materialia 83, 75–83 (2015).
44. Singh, A. & Saal, J. E. Dynamic Properties of Magnesium Alloys. JOM 66, 275–276 (2014).
45. Saal, J. E. & Wolverton, C. Thermodynamic stability of Mg-based ternary long-period stacking ordered structures. Acta Materialia 68, 325–338 (2014).
46. Meredig, B. et al. Combinatorial screening for new materials in unconstrained composition space with machine learning. Physical Review B 89, 094104 (2014).
47. Ji, Y. Z. et al. Predicting β′ precipitate morphology and evolution in Mg–RE alloys using a combination of first-principles calculations and phase-field modeling. Acta Materialia 76, 259–271 (2014).
48. Issa, A., Saal, J. E. & Wolverton, C. Physical factors controlling the observed high-strength precipitate morphology in Mg–rare earth alloys. Acta Materialia 65, 240–250 (2014).
49. Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD). JOM 65, 1501–1509 (2013).
50. Saal, J. E. & Wolverton, C. Thermodynamic stability of Co-Al-W L12 γ’. Acta Materialia 61, 2330–2338 (2013).
51. Grindy, S., Meredig, B., Kirklin, S., Saal, J. E. & Wolverton, C. Approaching chemical accuracy with density functional calculations: Diatomic energy corrections. Physical Review B 87, 075150 (2013).
52. Saengdeejing, A., Saal, J. E., Manga, V. R. & Liu, Z. K. Defects in boron carbide: First-principles calculations and CALPHAD modeling. Acta Materialia 60, 7207–7215 (2012).
53. Saal, J. E. & Wolverton, C. Thermodynamic Stability of Mg-Y-Zn Long-Period Stacking Ordered Structures. Scripta Materialia 67, 798–801 (2012).
54. Saal, J. E. & Wolverton, C. Solute-vacancy binding of the rare earths in magnesium from first principles. Acta Materialia 60, 5151–5159 (2012).
55. Wang, Y. et al. First-principles lattice dynamics and heat capacity of BiFeO3. Acta Materialia 59, 4229–4234 (2011).
56. Wang, Y. et al. Effects of spin structures on Fermi surface topologies in BaFe2As2. Solid State Communications 151, 272–275 (2011).
57. Kim, D. et al. Thermodynamic modeling of fcc order/disorder transformations in the Co–Pt system. Calphad 35, 323–330 (2011).
58. Zacherl, C., Saal, J. E., Wang, Y. & Liu, Z. K. First-principles calculations and thermodynamic modeling of the Re-Y system with extension to the Ni-Re-Y system. Intermetallics 18, 2412–2418 (2010).
59. Wang, Y. et al. Phonon dispersion in Sr2RuO4 studied by a first-principles cumulative force-constant approach. Physical Review B 82, 172503 (2010).
60. Wang, Y. et al. Broken symmetry, strong correlation, and splitting between longitudinal and transverse optical phonons of MnO and NiO from first principles. Physical Review B 82, 081104(R) (2010).
61. Wang, Y. et al. A first-principles scheme to phonons of high temperature phase: No imaginary modes for cubic SrTiO3. Applied Physics Letters 97, 162907 (2010).
62. Shang, S. L., Saal, J. E., Mei, Z. G., Wang, Y. & Liu, Z. K. Magnetic thermodynamics of fcc Ni from first-principles partition function approach. Journal of Applied Physics 108, 123514 (2010).
63. Saal, J. E., Wang, Y., Shang, S. & Liu, Z. K. Thermodynamic properties of Co3O4 and Sr6Co5O15 from first-principles. Inorganic Chemistry 49, 10291–8 (2010).
64. Saal, J. E., Shin, D., Stevenson, A. J., Messing, G. L. & Liu, Z. K. First-Principles Thermochemistry and Thermodynamic Modeling of the Al2O3-Nd2O3-SiO2-Y2O3 Pseudoquaternary System. Journal of the American Ceramic Society 93, 4158–4167 (2010).
65. Saal, J. E., Shang, S., Wang, Y. & Liu, Z. K. Magnetic phase transformations of face-centered cubic and hexagonal close-packed Co at zero Kelvin. Journal of Physics: Condensed Matter 22, 096006 (2010).
66. Manga, V. R., Saal, J. E., Wang, Y., Crespi, V. H. & Liu, Z. K. Magnetic perturbation and associated energies of the antiphase boundaries in ordered Ni3Al. Journal of Applied Physics 108, 103509 (2010).
67. Zhang, H. et al. Enthalpies of formation of magnesium compounds from first-principles calculations. Intermetallics 17, 878–885 (2009).
68. Shin, D., Saal, J. E. & Liu, Z. K. Thermodynamic modeling of the Cu-Si system. Calphad 32, 520–526 (2008).
69. Saal, J. E., Shin, D., Stevenson, A. J., Messing, G. L. & Liu, Z. K. First-Principles Calculations and Thermodynamic Modeling of the Al2O3-Nd2O3 System. Journal of the American Ceramic Society 91, 3355–3361 (2008).
70. Saal, J. E., Andelm, J., Nothwang, W. D. & Cole, M. W. The Impact of Acceptor Dopant Magnesium and Oxygen Vacancy Defects on the Lattice of Barium Strontium Titanate. Integrated Ferroelectrics 101, 142–151 (2008).
71. Liu, Z.-K., Hansen, S., Murray, J., Spencer, P. & Saal, J. Summary of the CALPHAD XXXVI 2007 conference. Calphad 32, 9–31 (2008).
72. Saengdeejing, A., Saal, J. E., Wang, Y. & Liu, Z. K. Effects of carbon in MgB2 thin films: Intrinsic or extrinsic. Applied Physics Letters 90, 151920 (2007).
73. Saal, J. E., Shang, S. & Liu, Z. K. The structural evolution of boron carbide via ab initio calculations. Applied Physics Letters 91, 231915 (2007).

Professional Organizations

The Minerals, Metals & Materials Society (TMS)
ASM International