Evan Ronald Antoniuk
Stanford University, Chemistry, Graduate Student
Research Interests:
The high brightness, low emittance electron beams achieved in modern X‐ray free‐electron lasers (XFELs) have enabled powerful X‐ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub‐nanometer length... more
The high brightness, low emittance electron beams achieved in modern X‐ray free‐electron lasers (XFELs) have enabled powerful X‐ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub‐nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial‐and‐error‐based iterative approaches, this work represents the first data‐driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M2O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state‐of‐the‐art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.
Research Interests:
Research Interests:
Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model (SynthNN) that leverages... more
Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model (SynthNN) that leverages the entire corpus of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classification task, SynthNN identifies synthesizable materials with 7x higher precision than with DFT-calculated formation energies. In a head-to-head material discovery comparison against 20 expert material scientists, SynthNN outperforms all experts, achieves 3.6x higher precision and completes the task five orders of magnitude faster than the average human expert. Remarkably, without any prior chemical knowledge, our experiments indicate that SynthNN learns the chemical principles of charge-balancing, chemical family relationships and ionicity, and utilizes these principles to generate synthesizability predictions. The development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.
Research Interests:
Two-dimensional (2D) materials derived from van der Waals (vdW)-bonded layered crystals have been the subject of considerable research focus, but their one-dimensional (1D) analogues have received less attention. These bulk crystals... more
Two-dimensional (2D) materials derived from van der Waals (vdW)-bonded layered crystals have been the subject of considerable research focus, but their one-dimensional (1D) analogues have received less attention. These bulk crystals consist of covalently bonded multiatom atomic chains with weak van der Waals bonds between adjacent chains. Using density-functional-theory-based methods, we find the binding energies of several 1D families of materials to be within typical exfoliation ranges possible for 2D materials. In addition, we compute the electronic properties of a variety of insulating, semiconducting, and metallic individual wires and find differences that could enable the identification of and distinction between 1D, 2D, and 3D forms during mechanical exfoliation onto a substrate. We find 1D wires from chemical families of the forms PdBr2, SbSeI, and GePdS3 are likely to be distinguishable from bulk materials via photoluminescence. Like 2D vdW materials, we find some of these 1D vdW materials have the potential to retain their bulk properties down to nearly atomic film thicknesses, including the structural families of HfI3 and PNF2, a useful property for some applications including electronic interconnects. We also study naturally occurring bulk crystalline heterostructures of 1D wires and identify two families that are likely to be exfoliable and identifiable as individual 1D wire subcomponents.
Research Interests:
We report a solid-state Li-ion electrolyte predicted to exhibit simultaneously fast ionic conductivity, wide electrochemical stability, low cost, and low mass density. We discover four phases within the crystalline lithium-boron-sulfur... more
We report a solid-state Li-ion electrolyte predicted to exhibit simultaneously fast ionic conductivity, wide electrochemical stability, low cost, and low mass density. We discover four phases within the crystalline lithium-boron-sulfur (LBS) system, Li5B7S13, Li2B2S5, Li3BS3, and Li9B19S33, with exceptional DFT based single crystal ionic conductivity values at room temperature of approximately 74 mS cm–1, 10 mS cm–1, 2 mS cm–1, and 28 mS cm–1 respectively. To our knowledge, our prediction gives Li5B7S13 the second-highest reported DFT-computed single crystal ionic conductivity of any crystalline material. We compute the thermodynamic electrochemical stability window widths of these materials to be 0.50, 0.16, 0.45, and 0.60 V. Individually, these materials exhibit similar or better ionic conductivity and electrochemical stability than the best known sulfide-based solid-state Li-ion electrolyte materials, including Li10GeP2S12. However, we predict that electrolyte materials synthesized from a range of compositions in LBS system may exhibit even wider thermodynamic electrochemical stability windows of 0.63 V and possibly as high as 3 V or greater. The LBS system also has low elemental cost of approximately 0.05 USD/m2 per 10 μm thickness, significantly lower than that of germanium-containing LGPS, and a comparable mass density below 2 g/cc. These fast conducting phases were initially discovered by a machine learning-based approach to screen over 12,000 solid electrolyte candidates, and the evidence provided here represents an inspiring success for this model.
Research Interests:
We discover the chemical composition of over 1000 materials that are likely to exhibit layered and 2D phases but have yet to be synthesized. This includes two materials our calculations indicate can exist in distinct structures with... more
We discover the chemical composition of over 1000 materials that are likely to exhibit layered and 2D phases but have yet to be synthesized. This includes two materials our calculations indicate can exist in distinct structures with different band gaps, expanding the short list of 2D phase-change materials. Whereas databases of over 1000 layered materials have been reported, we provide the first full database of materials that are likely layered but are yet to be synthesized, providing a roadmap for the synthesis community. We accomplish this by combining physics with machine learning on experimentally obtained data and verify a subset of candidates using density functional theory. We find that our model performs five times better than practitioners in the field at identifying layered materials and is comparable to or better than professional solid-state chemists. Finally, we find that semisupervised learning can offer benefits for materials design where labels for some of the materials are unknown.
Research Interests:
Research Interests:
Research Interests:
Research Interests: Pharmacology, Evolutionary Biology, Materials Science, Biotechnology, Space Science, and 12 moreMedicine, Multidisciplinary, Ev, Band Gap, Methods for Research Synthesis, Material Design, Chemical Composition, Monolayer, Stacking, Photovoltaic Applications, Heterojunction, and Van Der Waals Heterostructures
Research Interests: Materials Science, Chemistry, Silicon, Medicine, Germanium, and 3 morePhosphine, CHEMICAL SCIENCES, and Borane
Research Interests:
Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model (SynthNN) that leverages... more
Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery. In this work, we develop a deep learning synthesizability model (SynthNN) that leverages the entire corpus of synthesized inorganic chemical compositions. By reformulating material discovery as a synthesizability classification task, SynthNN identifies synthesizable materials with 7x higher precision than with DFT-calculated formation energies. In a head-to-head material discovery comparison against 20 expert material scientists, SynthNN outperforms all experts, achieves 3.6x higher precision and completes the task five orders of magnitude faster than the average human expert. Remarkably, without any prior chemical knowledge, our experiments indicate that SynthNN learns the chemical principles of charge-balancing, chemical family relationships and ionicity, and utilizes these principles to generate synthesizability predictions. The develo...
Research Interests:
Research Interests:
Research Interests:
Research Interests:
Research Interests:
The high brightness, low emittance electron beams achieved in modern X-ray free-electron lasers (XFELs) have enabled powerful X-ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub-nanometer length... more
The high brightness, low emittance electron beams achieved in modern X-ray free-electron lasers (XFELs) have enabled powerful X-ray imaging tools, allowing molecular systems to be imaged at picosecond time scales and sub-nanometer length scales. One of the most promising directions for increasing the brightness of XFELs is through the development of novel photocathode materials. Whereas past efforts aimed at discovering photocathode materials have typically employed trial-and-error-based iterative approaches, this work represents the first data-driven screening for high brightness photocathode materials. Through screening over 74 000 semiconducting materials, a vast photocathode dataset is generated, resulting in statistically meaningful insights into the nature of high brightness photocathode materials. This screening results in a diverse list of photocathode materials that exhibit intrinsic emittances that are up to 4x lower than currently used photocathodes. In a second effort, multiobjective screening is employed to identify the family of M2 O (M = Na, K, Rb) that exhibits photoemission properties that are comparable to the current state-of-the-art photocathode materials, but with superior air stability. This family represents perhaps the first intrinsically bright, visible light photocathode materials that are resistant to reactions with oxygen, allowing for their transport and storage in dry air environments.