{"schemaVersion":"jobsearcher.job.v1","id":"a29dc0e3a60fb22cb436f176","url":"https://jobsearcher.com/jobs/a29dc0e3a60fb22cb436f176","canonicalUrl":"https://jobsearcher.com/jobs/a29dc0e3a60fb22cb436f176","title":"Computational Materials Scientist for Machine-Learned Interatomic Potentials (MD/DFT)","description":"About Amphiform:Amphiform (amphiform.com) is building next-gen energy materials for AI data centres, defence and space: light, abundant and powerful. We achieve that by creating a new type of matter: hybrid materials, built atomic layer by atomic layer, where every atom has a pre-programmed purpose.We just closed a $5.5+1m pre-seed led by General Catalyst and Main Object, with Thomas Wolf (Hugging Face), Charlie Songhurst, and others.The role:We are developing a new class of material: an ultrathin, vapour-deposited hybrid materials with custom properties - and we want to understand the fundamental properties. You will own our first atomistic simulation campaign: predicting conductivity (electron, ion, heat), hydration behaviour, and interfacial transport in a material that has never been simulated, and testing your predictions against experiments that we'll run constantly alongside. The workflow (DFT benchmarking → amorphous structure generation → fine-tuned machine-learned interatomic potentials with active learning → large-scale transport MD, enhanced sampling, and path-integral MD) is scoped, budgeted, and precedented in the recent literature; the material is not. You will be our first dedicated computational hire, working directly with the experimental team.What you'll do:Benchmark DFT functionals against coupled-cluster references for proton-transfer energetics, and own the resulting error budgetBuild amorphous model structures of a hybrid metal-organic network validated against our dataFine-tune foundation MLIPs (MACE-class) with active learning; run DFT labelling campaignsRun nanosecond-scale transport MD, metadynamics, and PIMD to extract diffusion coefficients, activation energies, isotope effects, and interfacial behaviourTurn simulation into decisions: feed results into our materials and process development, and publish the methodology with usDefine our simulation infrastructure (environments, data management, reproducibility)What we're looking for:PhD in computational chemistry, physics, or materials scienceHands-on ab-initio MD experience on HPCYou have trained at least one machine-learned interatomic potential (MACE, NequIP, Allegro, DeepMD, GAP, or similar) and can explain how you built the training set and knew it was convergedStrong scientific PythonNice to have:Ion transport backgroundAmorphous/disordered structure generationPath-integral MD (i-PI), GCMC (RASPA), charge-partitioning workflows, or workflow engines (psiflow, Parsl, Snakemake)A benchmark culture: DLPNO-CCSD(T), functional selection, error bars you can defend","company":"Amphiform","rawCompany":"amphiform","city":"Springfield","state":"IL","isRemote":false,"isActive":true,"createdAt":"2026-10-06T06:07:31.893Z","occupations":[{"code":"19-2032.00","title":"Materials Scientists","slug":"materials-scientists"},{"code":"19-2012.00","title":"Physicists","slug":"physicists"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"}],"industries":[{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541713","title":"Research and Development in Nanotechnology","slug":"research-and-development-in-nanotechnology"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Computational Materials Scientist for Machine-Learned Interatomic Potentials (MD/DFT)","description":"About Amphiform:Amphiform (amphiform.com) is building next-gen energy materials for AI data centres, defence and space: light, abundant and powerful. We achieve that by creating a new type of matter: hybrid materials, built atomic layer by atomic layer, where every atom has a pre-programmed purpose.We just closed a $5.5+1m pre-seed led by General Catalyst and Main Object, with Thomas Wolf (Hugging Face), Charlie Songhurst, and others.The role:We are developing a new class of material: an ultrathin, vapour-deposited hybrid materials with custom properties - and we want to understand the fundamental properties. You will own our first atomistic simulation campaign: predicting conductivity (electron, ion, heat), hydration behaviour, and interfacial transport in a material that has never been simulated, and testing your predictions against experiments that we'll run constantly alongside. The workflow (DFT benchmarking → amorphous structure generation → fine-tuned machine-learned interatomic potentials with active learning → large-scale transport MD, enhanced sampling, and path-integral MD) is scoped, budgeted, and precedented in the recent literature; the material is not. You will be our first dedicated computational hire, working directly with the experimental team.What you'll do:Benchmark DFT functionals against coupled-cluster references for proton-transfer energetics, and own the resulting error budgetBuild amorphous model structures of a hybrid metal-organic network validated against our dataFine-tune foundation MLIPs (MACE-class) with active learning; run DFT labelling campaignsRun nanosecond-scale transport MD, metadynamics, and PIMD to extract diffusion coefficients, activation energies, isotope effects, and interfacial behaviourTurn simulation into decisions: feed results into our materials and process development, and publish the methodology with usDefine our simulation infrastructure (environments, data management, reproducibility)What we're looking for:PhD in computational chemistry, physics, or materials scienceHands-on ab-initio MD experience on HPCYou have trained at least one machine-learned interatomic potential (MACE, NequIP, Allegro, DeepMD, GAP, or similar) and can explain how you built the training set and knew it was convergedStrong scientific PythonNice to have:Ion transport backgroundAmorphous/disordered structure generationPath-integral MD (i-PI), GCMC (RASPA), charge-partitioning workflows, or workflow engines (psiflow, Parsl, Snakemake)A benchmark culture: DLPNO-CCSD(T), functional selection, error bars you can defend","datePosted":"2026-10-06T06:07:31.893Z","dateModified":"2026-10-06T06:07:31.893Z","hiringOrganization":{"@type":"Organization","name":"Amphiform","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Springfield","addressRegion":"IL","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"a29dc0e3a60fb22cb436f176"},"url":"https://jobsearcher.com/jobs/a29dc0e3a60fb22cb436f176"}}