Human personality is 30–60% heritable according to twin and adoption studies. Hundreds of genetic variants are expected to influence its complex development, but few have been identified. We used a machine learning method for genome-wide association studies (GWAS) to uncover complex genotypic–phenotypic networks and environmental interactions. The Temperament and Character Inventory (TCI) measured the self-regulatory components of personality critical for health (i.e., the character traits of self-directedness, cooperativeness, and self-transcendence). In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified five clusters of people with distinct profiles of character traits regardless of genotype. Third, we found 42 SNP sets that identified 727 gene loci and were significantly associated with one or more of the character profiles. Each character profile was related to different SNP sets with distinct molecular processes and neuronal functions. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of 95% of the 42 SNP sets in healthy Korean and German samples, as well as their associations with character. The identified SNPs explained nearly all the heritability expected for character in each sample (50 to 58%). We conclude that self-regulatory personality traits are strongly influenced by organized interactions among more than 700 genes despite variable cultures and environments. These gene sets modulate specific molecular processes in brain for intentional goal-setting, self-reflection, empathy, and episodic learning and memory.

Experimental studies of learning suggest that human temperament may depend on the molecular mechanisms for associative conditioning, which are highly conserved in animals. The main genetic pathways for associative conditioning are known in experimental animals, but have not been identified in prior genome-wide association studies (GWAS) of human temperament. We used a data-driven machine learning method for GWAS to uncover the complex genotypic–phenotypic networks and environmental interactions related to human temperament. In a discovery sample of 2149 healthy Finns, we identified sets of single-nucleotide polymorphisms (SNPs) that cluster within particular individuals (i.e., SNP sets) regardless of phenotype. Second, we identified 3 clusters of people with distinct temperament profiles measured by the Temperament and Character Inventory regardless of genotype. Third, we found 51 SNP sets that identified 736 gene loci and were significantly associated with temperament. The identified genes were enriched in pathways activated by associative conditioning in animals, including the ERK, PI3K, and PKC pathways. 74% of the identified genes were unique to a specific temperament profile. Environmental influences measured in childhood and adulthood had small but significant effects. We confirmed the replicability of the 51 Finnish SNP sets in healthy Korean (90%) and German samples (89%), as well as their associations with temperament. The identified SNPs explained nearly all the heritability expected in each sample (37–53%) despite variable cultures and environments. We conclude that human temperament is strongly influenced by more than 700 genes that modulate associative conditioning by molecular processes for synaptic plasticity and long-term memory.

A simulation study has been performed to analyze resistive switching (RS) phenomena in valence change memories (VCM) based on a HfO ₂ dielectric. The kernel of the simulation tool consists of a 3D kinetic Monte Carlo (kMC) algorithm implemented self-consistently with the 3D Poisson and heat equations. These VCM devices show filamentary conduction, their RS operation is based on the destruction and regeneration of an ohmic conductive filament (CF) composed of oxygen vacancies. The physics underlying the RS operation is described by means of processes linked to generation of oxygen vacancies, oxygen ion migration and recombination between vacancies and oxygen ions that can be accurately described by using the electric field and temperature distributions in the dielectric. The studied devices consist of TiN/Ti/HfO ₂ /W stacks where the Ti capping layer plays the role of oxygen ion getter material. The simulation tool is useful for obtaining information of internal physical variables, explaining RS dynamics and the CFs evolution from the microscopic viewpoint in terms of their size and shape under different electrical input signals; particularly, the pulsed operation regime has been studied in depth. Furthermore, interesting phenomena, such as partial SETs within overall RESET processes can be accurately reproduced.

A new RRAM simulation tool based on a 3D kinetic Monte Carlo algorithm has been implemented. The redox reactions and migration of cations are developed taking into consideration the temperature and electric potential 3D distributions within the device dielectric at each simulation time step. The filamentary conduction has been described by obtaining the percolation paths formed by metallic atoms. Ni/HfO ₂ /Si-n ⁺ unipolar devices have been fabricated and measured. The different experimental characteristics of the devices under study have been reproduced with accuracy by means of simulations. The main physical variables can be extracted at any simulation time to clarify the physics behind resistive switching; in particular, the final conductive filament shape can be studied in detail.